Getting started with MEMS Studio

MEMS Studio offers the following user experience:

• Sensor evaluation for motion, environmental, and infrared sensors in the MEMS portfolio
• Configuration and testing of in-sensor features such as the finite state machine (FSM), machine learning core (MLC), intelligent sensor processing unit (ISPU)
• Runtime and offline data analysis 
• No-code graphical design of algorithms

The key features of the application include:

• Sensor configuration
  • Easy sensor configuration and evaluation
  • Access to the full sensor register map
  • Interrupt status monitoring
• Sensor data analysis
  • Runtime sensor data visualization charts (line charts, bar graphs, 3D plots, …)
  • Data logging to .csv file
  • Offline data visualization, data labeling, and editing
  • Fast Fourier transform (FFT) analysis of online and offline data
  • Spectrogram analysis of online and offline data
• Application development
  • Testing of the advanced embedded features (FIFO, pedometer, free fall, …) in the sensor
  • In-sensor AI design and programming for the finite state machine (FSM), machine learning core (MLC), and intelligent sensor processing unit (ISPU)
  • Embedded AutoML tool (automatic filter and feature selection) to simplify the MLC configuration process
  • Visualization and data logging of the output of the embedded software libraries
  • Development of no-code algorithms for data processing in STM32 microcontrollers
  • Analysis of pretrained neural network model, optimization, and conversion to c code
• Support for Windows, macOS, and Linux operating systems
• Network updates with automatic notification of new releases

The MEMS Studio software has been designed to operate with Microsoft Windows platforms, Linux platforms, and macOS platforms.

Windows platforms

To install MEMS Studio, launch the mems-studio.exe installation file and follow the instructions that appear on the screen. When the software is installed, you can run it from: Start > STMicroelectronics > MEMS Studio

sudo dpkg -i mems-studio_VERSION_amd64.deb

sudo apt-get install <missing_library_name>

sudo dpkg -i mems-studio_VERSION_amd64.deb

macOS platforms

To install MEMS Studio, simply open the DMG package and drag MEMS Studio to your Applications folder.

To use MEMS Studio, it is mandatory to enter your myST login credentials. By logging in with your myST credentials, you can fully utilize all the functionalities and services offered by MEMS Studio.

Some application parameters can be adjusted in the Applications Settings in the Settings menu.

Application colors can be selected using Color theme. Available themes are Light, Dark, and Legacy.

Font size can be adjusted by the user in the application settings for good readability.

Plot grid in rolling mode can be set to be stationary or moving.

If Automatic registers reading is enabled, sensor registers are automatically read when entering Register map page.

The user can select the units in the application for acceleration, angular rate, magnetic field, temperature, humidity, and pressure.

Allow multiple page undocking enables the possibility to undock more than one page at a time. This option requires more computer resources.

If Automatic easy configuration is enabled, the sensor is automatically configured after connection (when the sensor is not in power-down mode). This option is valid only for ProfiMEMS firmware.

If Filter AlgoBuilder build output is enabled, AlgoBuilder automatically filters outputs from the external compiler and makes them more readable in the console.

If Notify about new firmware is enabled, the application automatically checks if new firmware for the connected board is available and offers a firmware update.

MEMS Studio utilizes some external tools. The paths to these tools need to be set in External Tools in the Settings menu.

It is necessary to specify the path to at least one compiler in order to compile AlgoBuilder firmware. IAR embedded workbench path, Keil uVision path, or STM32CubeIDE path can be specified.

Firmware programing in the standalone page or programming from the AlgoBuilder page utilizes the STM32CubeProgramer CLI tool. STM32CubeProgrammer path must be specified in order to make the programming available.

ISPU Model Converter requires that the path to the ST Edge AI Core tool needs to be specified.

The path to the Netron tool needs to be specified in order to graphically visualize the neural network.

The path to the ISPU toolchain needs to be specified in order to invoke ISPU sensor configuration generation directly from MEMS Studio.

The MEMS Studio application is able to check and notify if a new version is available. You can then decide whether to download and install the new version or not on the Updater Settings page. The network parameters have to be properly set in the Network Settings page. Both pages are in the Settings menu.

A check for a new version can be done manually by pressing the Check Now button or automatically during application startup. An interval for the automatic check can be set.

If the Download new firmware option is enabled, the application will check and download also new firmware for supported evaluation boards. The firmware package is downloaded separately and does not have any impact on the installed MEMS Studio version.

For proper MEMS Studio functionality, network settings must be correctly configured.

If a proxy server is used, corresponding parameters like the HTTP address and port must be set.

If the proxy server requires authentication, user credentials must be entered in the appropriate fields.

The Check Connection button can be used to check if the settings are done correctly and the server can be reached.

After the application startup, the Connect page is displayed. To evaluate the sensor and libraries, a compatible device with proper firmware must be connected. You can click on a particular board in the Supported Boards section to see the list of supported sensors and feature on the board or check Hardware and firmware compatibility. Features that do not require a connected device like data analysis, MLC design, AlgoBuilder, and others are available all the time.

In order to connect to a board, Communication type needs to be selected. Serial, USB, and BLE interfaces are supported. The USB is used for data acquisition using High Speed Datalog firmware. The BLE interface is used only for the AlgoBuilder firmware evaluation. According to the selected communication type, Communication port, USB device or BLE device can then be selected. Communication with the device is initialized by pressing the Connect button.

After the connection is established, the firmware for the sensor evaluation allows selecting the sensor. In the case of ProfiMEMS firmware, the reference part number of DIL24 adapters or the sensor name is used for the selection. In the case of DatalogExtended firmware, sensor names are used.

The communication interface between the microcontroller and the sensor as well as the power supply voltages can be configured before the connection is established by clicking on the Advanced... button.

In the case of firmware that does not allow sensor selection (firmware for library evaluation, AlgoBuilder firmware, …), the list of sensors is automatically populated and selected.

Details about connected boards, sensors, and firmware are displayed in the right panel including links to all available resources like datasheets, applications notes, websites, GitHub repositories, and others.

Development boards with appropriate AlgoBuilder firmware can be connected over a Bluetooth interface. In the Windows operating system, the device needs to be paired first. In order to successfully detect the device in Windows 11, the Advanced Bluetooth device discovery mode needs to be selected in the Bluetooth settings.

The device with AlgoBuilder firmware appears as ALGOB. The pin for pairing is always 123456.

The following tables list the combinations of hardware and firmware supported by MEMS Studio for MEMS sensor evaluation as well as those combinations that support library evaluation and the AlgoBuilder functionality.

For a complete evaluation of the sensor, the STEVAL-MKI109D or STEVAL-MKI109V3 (professional MEMS Studio) evaluation platform should be used, which supports the adapter boards and sensors indicated in Table 1 Supported hardware and firmware for full sensor evaluation.

Refer to Table 2 Supported hardware and firmware with reduced sensor evaluation for the X-NUCLEO expansion boards with STM32 ODE (open development environment) used for development and form factor boards dedicated to prototyping. The sensor evaluation firmware on these boards offers configuring and testing some embedded features, such as MLC, FSM, or interrupts.

Refer to Table 3 Supported hardware for datalogging using Datalog2 (High Speed Datalog) firmware for supported boards intended for data logging using Datalog2 (High Speed Datalog) firmware.

For library evaluation, refer to Table 4 Supported hardware and firmware for library evaluation, which indicates the X-NUCLEO expansion board and supported sensor, using the firmware from the X-CUBE-MEMS1 package, however other sensors are supported if the application is generated using STM32CubeMX.

Finally, Table 5 Supported hardware and firmware with AlgoBuilder functionality provides the list of hardware platforms and development kits, used in conjunction with AlgoBuilder, that support a limited number of sensors.

The sensor evaluation features are dynamically adjusted according to the available functionalities of the connected evaluation board, sensor, and firmware.

Quick Setup page

The Quick Setup page allows the user to configure basic sensor configurations such as the output data rate, full scale, and others according to the device datasheet.

If more evaluation modes are available like sensor fusion low-power, FIFO, TDM, and others, they can be selected on this page. The available sensor evaluation pages are adjusted according to the selected mode. If applicable, the control can align its status to the current register value.

Registers Map page

The Registers Map displays the list of device registers divided by register pages, if any.

Every register item has a Read, Write, and Default(restore default value) button enabled according to the datasheet and a bit view populated according to the register value.

Using the dedicated button ?, any register item can be expanded to explore the register value bit map description.

Custom Register Action allows direct read and write transactions to a single register by specifying the register address, page and value.

Save to File page

The user can use this page to save a stream of sensor output data in a .csv file for postprocessing. This can be done by selecting the data to be stored.

The Browse button is used to select the folder and insert the file name. The acquisition period can be set to a fixed time in [ms] if desired. Then data to be saved can be selected from the list. The acquisition can be controlled using the Start and Stop buttons. If the Logging timeout option is used, the acquisition is going to run as long as the timeout period.

If the sensors are configured according to different output data rates, the user can select the Data log period source from among the available data types using a dedicated selection option. Refer to the following figure.

When using Datalog2 (High Speed Datalog) firmware data, are primarily stored in Datalog2 native binary format. Using the Convert to CSV switch, binary format can be automatically converted to CSV format at the end of the logging session.

Data Table tool

The Data Table tool displays the output values measured by the sensor connected to the evaluation board. This view provides an overview of all samples received with a table data update rate of 0.5 Hz to reduce overall CPU/ GPU consumption. This functionality is disabled in the case of very high data rates.

Data Monitor tool

The Data Monitor tool displays the latest output values measured by the sensor connected to the evaluation board. This view provides an overview of the last samples received with an update rate of 10 Hz to reduce overall CPU/GPU consumption in the case of high data rates.

MLC Monitor, FSM Monitor

The MLC Monitor and FSM Monitor pages can be used to display output from the machine learning core (MLC) or finite state machine (FSM) including the label assigned to each output value. This feature provides a clear and user-friendly overview of the current states and classifications detected by the sensors, making it easier to monitor and interpret the results in real time. The textual values are taken from the JSON file with the sensor configuration, which is loaded from the Load/Save Configuration page.

Bar Charts tool

The Bar Charts tool displays the data measured by the sensor in a bar chart format.

The height of each bar is determined by the amplitude of the signal measured by the sensor along the corresponding axis. The full scale of the graph depends on the configuration and can be changed through either the Quick Setup or the Registers Map tab.

Line Charts tool

The Line Charts tool shows the evolution of the output over time. This tool generates a time graph of data samples from the connected sensors.

Each waveform can be shown or hidden by clicking on the corresponding colored box in the top bar of the plot.

The user can click on the minus and plus at the bottom of the graph object to manually scale the plot horizontally. Furthermore, using a dedicated scroll bar at the bottom of the page, the user can display the older samples.

By mousing over the plot area, the top bar labels are updated to display the collected values of the samples.

Note: Individual signals can be disabled or enabled in the chart top bar. Using a right mouse click in this area, all channels can be selected or deselected.

Hovering over the right side of a graph object, available options and settings are displayed such as:

• Fit: updates the Y-axis scale to fit all waveforms
• Auto: automatic update of the Y-axis scale
• Full: sets the full-scale value according to the device configuration
• Save: exports a screenshot of the chart to an image file in .png format
• Copy: saves the chart as an image in the system clipboard
• Help: displays the available keyboard shortcuts for navigation in the chart

Scatter Plot tool

The Scatter Plot tool shows the graphical representation of the magnetometer data using Cartesian coordinates. In general, a scatter plot is used to display values for typically two variable sets of data.

The plot shows three lines according to the sensor axes (X-Y, Y-Z, X-Z). The three lines can be enabled and disabled independently by clicking on the corresponding colored box at the top of the graph. By clicking on the Clear button, the user can reset all data in the plot.

The plot component detects mouse wheel events in order to zoom in or out on data. Auto allows the user to set the automatic zoom in order to fit all data on the graph.

Compass tool

The Compass tool shows an example of a compass application, which can be implemented using a 6-axis module (3-axis accelerometer and 3-axis magnetometer).

The algorithm uses the magnetometer data to measure the Earth’s magnetic field, and the accelerometer data to compensate for the inclination of the board. Rotating the board, the application shows the heading of the compass.

The performance of the compass is related to the configuration used. So, the application shows the current configuration and the recommended configuration (accelerometer and magnetometer ODR).

Before using the compass demo, the system must be calibrated by moving the board randomly for a few seconds; the quality of the calibration step is indicated by the compass object border color according to the legend on the right side.

Interrupt tool

The Interrupt tool allows evaluating the interrupt generation features of the MEMS sensor.

On this page, it is possible to configure the characteristics of the inertial events that must be recognized by the device and to visualize (in real time) the evolution of the interrupt lines.

The application provides access to the interrupt registers, which allow the configuration of the interrupt sources of the device.

The user can configure the interrupts using the dedicated combo box in order to load the recommended configurations in the device.

Inclinometer tool

The Inclinometer tool represents the angle between the accelerometer axis and the horizontal plane. This tool is available if the sensor in use integrates an accelerometer.

FFT tool

The FFT tool displays the fast Fourier transform of the sensor data. The page shows the frequency-domain plot for each axis of the selected sensor.

The tool provides various options such as:

• DC nulling: removes the DC component from the signal
• Magnitude: calculates Euler’s formula of the vector composed of the axis components
• Magnitude (scale): Y-axis scale
• Frequency (scale): X-axis scale
• Style: style of chart (waveform or bars)
• Window: window function applied to the signal before FFT evaluation
• Length: length of the window used to evaluate FFT (number of samples from which FFT is computed)
• Zero padding: number of zero samples added to the captured signal
• Show spectrogram: enables or disables spectrogram visualization
• Fixed refresh rate: updates the FFT chart without overlapping windows
• Fixed overlap: updates the FFT chart, overlapping windows according to the slider value of the window overlap
• Slider value: percentage of the samples saved for the next FFT computation

6D tool

The 6D tool provides an example of how to use the 6D position function.

On this page, it is possible to configure the interrupt for 6D recognition by clicking on the 6D Enabled toggle button.

After loading the configuration, the selected 3D model is oriented depending on the 6D position detected.

The window also shows (in real time) the evolution of the interrupt line.

The user can change the register configuration by setting different values in the interrupt registers as shown in the tool.

3D Plot tool

The 3D Plot tool shows the graphical representation of the magnetometer data in 3D space.

Zooming in and out of the chart is possible using the mouse wheel. Changing the view angle is possible by clicking and panning in the chart.

Features Demo page

The Features Demo page allows the user to evaluate demo modes available for a connected sensor when this feature is supported. The side menu on the right displays indicators to notify the user about event detection and set parameters, if applicable.

Communication and Power Settings tool

The Comm. & Power Settings tool allows the user to read the current consumption and update VDD and VDDIO values on the Professional MEMS Studio board (STEVAL-MKI109D, STEVAL-MKI109V3).

FIFO tool

The FIFO tool can be used to test the first-in first-out data buffer embedded in the device when this feature is supported by the sensor (see the device datasheet for more details).

By using the combo box available on the page, the FIFO can be configured in all the supported modes (for example, bypass, FIFO, stream, stream-to-FIFO).

The application shows the values of the X, Y, Z data stored in the deep FIFO buffer, indicating both numerical data and the corresponding graph.

Sensor fusion tool

Some sensors can be equipped with the sensor fusion low-power (SFLP) feature for generating game rotation vector, gravity vector, and gyroscope bias data based on the accelerometer and gyroscope data processing. If this feature is available, the evaluation can be activated on the Quick Setup page.

If sensor fusion mode is selected, the available sensor evaluation pages are adjusted according to this mode. Game rotation vector, quaternions, gravity vector, and gyroscope bias signals are added in Data Table, Data Monitor, Bar Charts, Line Charts and Save to File. The 3D Model page is available in this mode. A 3D model is displayed on this page and the orientation of the model changes based on the game rotation vector data so the model follows the sensor movement. Different 3D models can be selected. The initial position of the model can be set by using the Reset Model button.

Load/Save Configuration page

The Load/Save Configuration page allows the user to save and load the current register configuration. Clicking on the Save button stores the register values in a .json file. The saved configuration can be loaded at any time by clicking on the Load button.

Middleware libraries for ST sensors are provided in the X-CUBE-MEMS1 package. This package contains dedicated projects for each library, which can be used for library evaluation. Using MEMS Studio and the firmware for a particular library with the selected development board, a user can evaluate the functionality of the library.

After the development board is programmed with the selected firmware, the connection can be established by selecting Communication port and pressing the Connect button. Used sensors are indicated by the firmware, and they are listed on the Connect page with all details.

After a successful connection, the Library Evaluation functionality is added in the menu.

Save To File

The user can use this page to save a stream of sensor and library output data in a .csv file. This can be done by selecting the data to be stored. The Browse button is used to select the folder and insert the file name, then the Start and Stop buttons define the acquisition period. Additionally, the user can define a recording timeout using the dedicated toggle button and text field to interrupt data logging after a defined [ms] interval.

Data Table

The Data Table tool displays the output values from the sensors and libraries. This view provides an overview of all values received with a table data update rate of 0.5 Hz to reduce overall CPU/GPU consumption.

Data Monitor

The Data Monitor tool displays the latest output values from the sensors and libraries. This view provides an overview of the last samples received with an update rate of 10 Hz to reduce overall CPU/ GPU consumption.

Data visualization pages

The available data visualization pages depend on the selected library. Line charts, bar charts, and a scatter plot (for magnetometer data) are always available to visualize sensor data. Additional pages for visualization of library outputs like 3D plot, 3D model, status indicator and table, and others are displayed based on the library functionality.

Time Statistics

The Time Statistics page displays the library elapsed time in line charts and statistical information line average, min, max values.

Data Injection

The Data Injection page allows injecting previously acquired sensor data into the library input instead of real-time sensor data. The library processes the data and sends the results in the same way as working with real-time sensor data. All the other features for the evaluation of the library are available and active.

The data log file with previously acquired data can be opened by clicking on the Browse button. The data must be acquired with the minimum sampling rate frequency given by the library and the library evaluation firmware. The data log with the higher sampling rate is automatically decimated. Input data are displayed in the table.

Data injection is enabled by switching the firmware to Offline mode. Data injection is controlled by the Start and Stop buttons. Injection line by line can be done using the Step button. The active line is highlighted in the table. A wraparound to move from the last sample to the first sample can be activated using the Repeat toggle switch.

The machine learning core (MLC) tool allows the user to configure a machine learning core that is embedded in some devices. For more details about the MLC, consult the specific application note for the sensor you would like to use. The configuration procedure is divided into several steps appearing in different tabs:

• Data patterns: allows managing the data patterns to be used and assigning a label to each data pattern loaded
• AFS tool: allows processing acquired data and recommends window length, filters, and features that can be used for decision tree generation
• ARFF generation: allows configuring inputs, filters, and features to generate an .arff file
• Decision tree generation: allows configuring the decision tree outputs and generating the decision trees
• Config generation: allows setting the metaclassifier and generating the configuration file
• Decision tree output viewer: allows live monitoring of MLC outputs in the chart when testing the generated MLC configuration
• Debug: allows testing the MLC configuration using previously acquired data

To start configuring the machine learning core, the workspace directory must be selected using the Browse button. After that a device must be selected. If a sensor using the MLC is connected, it is selected automatically. The entire MLC configuration is stored in the mlc_settings.json file inside the workspace directory. The changes are automatically saved to this file. The MLC configuration can be imported into the active workspace using the Import button.

All artifacts generated during the MLC configuration process can be deleted using the Clear Workspace button, and the mlc_settings.json is not deleted. All the settings can be deleted and restored to the default values using the Clear Settings button. The history of the performed actions and results are accessible in a dedicated window, which can be displayed using the Open Log button.

Data patterns

The Data patterns tab allows managing the data patterns to be used for the machine learning processing. The data patterns that are possible to load must have the same data format as the log files generated by MEMS Studio, Unico-GUI, or Unicleo-GUI.

A data pattern(s) is selected using the Browse button. Multiple files can be selected. A class label must be assigned to each data pattern, which is then used for machine learning processing. The class label is confirmed by the Load button. Data patterns can be removed from the list using the trash bin button in the tables.

AFS tool

The AFS tool offers the possibility to estimate suitable window length, filters, and features for classification done by the decision tree.

Automatic window length calculation

The window length is calculated based on the lowest frequency present in the signal across all data classes.

Automatic filter selection

There are two methods that can be used for automatic filter selection. Basic search is a peak-based method that identifies the maxima of the signal in the frequency spectrum. This method is less computationally expensive, but it is not accurate on flat signals without significant peaks. Exhaustive search is an entropy-based method that searches all combinations of frequency bands (on a logarithmic scale) and selects the frequency of interest that minimizes the entropy against the other classes.

Automatic feature selection

The automatic feature selection algorithm considers filters generated by the automatic filter selection and the features are computed for different filters and raw data. It uses an ensemble method for feature importance and cross correlation to remove highly correlated features. The following statistical features are supported for both single axis and norm: mean, variance, energy, peak to peak, minimum, maximum. The automatic feature selection algorithm selects the top features by computing the rank of each feature according to an ensemble method and averaging across all ensemble methods. There are five different ensemble methods available to determine feature importance: ANOVA, Ada Boost (ADA), Sequential Feature Selection (SFS), Random Forest (RF), Recursive Feature Elimination (RFE). By default, ANOVA, Ada Boost, Random Forest, and Recursive Feature Elimination are used as methods for ranking important features. Including Sequential Feature Selection may improve the overall performance but increases the overall runtime.

Data analysis takes into account the machine learning core ODR and input specification parameters from the ARFF generation tab. The window length is also taken from this tab in case the window length calculation is not enabled.

After the desired parameter selection, the data analysis is executed using the Run button.

The generated window length, filter, and features can be transferred to the MLC configuration in the ARFF generation tab using the Apply to Settings button.

ARFF generation

Attribute-Relation File Format (ARFF) is a file format used for storing data in machine learning applications. It contains a list of instances, where each instance represents a single window processed by the MLC.

The ARFF generation tab allows configuring sensor and MLC parameters. The MLC processes the data at the Machine Learning Core ODR rate in consequential data windows. The number of samples in each window can be configured by the Window length option. The MLC processes the sensor data specified in the Inputs section. The full scale and output data rate of the selected sensors can be defined. Filters offer preprocessing on the machine learning core input signals. The Features section contains a list of features that are calculated from the input data (filtered or not filtered). After setting all the parameters, the ARFF file can be generated using the Generate ARFF File button. Statistical parameters computed from the inputs are then used for classification in the decision tree.

Decision tree generation

The Decision tree generation tab allows configuring a decision tree using selected classes to process and evaluating decision tree classification performance. The Number of decision trees can be specified.

A new decision tree algorithm was introduced in MEMS Studio v2.0.0. The new algorithm has both depth-first and best-first tree building implementations, implements Breiman's cost-complexity pruning, and identifies optimal tree size using cross validation.

The maximum depth of the tree (Max depth), minimum samples required in a leaf node (Min leaf samples), and Percentage of data to use for training can be configured.

The previous algorithm can be enabled using the Use legacy decision tree train algo switch. In this case, the Maximum number of nodes and Confidence factor, which control decision tree pruning, can be set.

A decision tree is generated using the Generate Decision Tree button. A file with an externally generated decision tree definition can be loaded using the Import button. After decision tree generation or loading, statistical parameters of the decision tree like accuracy, number of instances, confusion matrix, and others are displayed. The resulting decision tree can be analyzed using the Inspect tree info button for a textual content or using Show Decision Tree for a graphical representation of the same.

If the user imports a decision tree, a feature mapping is needed to map every feature selected during arff generation with feature listed into decision tree file every feature selected during arff generation must be mapped, listing each feature in the decision tree file. Note that this mapping is applied for every decision tree imported or generated.

Config generation

The Config generation tab allows configuring the metaclassifiers and generating the .json file containing the machine learning core configuration.

The generated MLC configuration can be stored also in .h file using the Export As button.

In the case of a board with a selected sensor connected, the generated configuration can be uploaded to the sensor using the Load Config into Sensor button.

Decision tree output viewer

The Decision tree output viewer tab allows testing the generated MLC configuration by monitoring individual MLC outputs in the chart.

Debug

The Debug tab allows checking the MLC functionality in order to validate the configuration using previously recorded log files, assuming the device is configured as it was before starting to log data in the file.

Data injection control

• Run: injects all the next samples
• Pause: pauses the data injection process with the possibility to resume it
• Stop: interrupts the data injection process and exits debug mode.
• Next: injects the next sample and updates the table with output data
• Next step: injects a defined number of samples according to the Step lengthsettings
• Export log: exports the content of the table with the results to a .csv file

The data table is updated at every step if the Results at each step switch is enabled, otherwise it is updated only when the data injection is paused or stopped. The Number of decision tree set settings specifies which MLC output registers will be monitored.

The debug page provides the user with a dedicated converter tool to simplify data conversion during log data processing. The tool can be opened using the Show Convertor button. The debug tool works with raw data in LSB and it converts sensor data internally into LSB using the sensitivity evaluated on the basis of a given configuration file if no LSB data was given.

The MLC in the hardware configures conditions in the units of measurement, which is why users might need a converter tool to easily understand if a certain condition of a decision tree is verified for a certain LSB data. For example if a decision tree condition has a threshold of 0.5 g, the corresponding LSB threshold value will be 1024 using a sensitivity of 0.488.

Merging MLC and FSM configurations requires a special procedure. The Merge MLC + FSM tool can be used to perform combinations of separate MLC and FSM configuration files into one file.

The Pedometer tool can be used to configure and test the pedometer embedded in the device when this feature is supported by the sensor (see the device datasheet for more details).

There are three different tabs for this tool:

• Configuration tab: allows fast-configuring the pedometer with its default configuration
• Debug tab: used to load a data pattern into the device in order to test the pedometer on the pattern loaded
• Regression Tool tab: allows finding an optimal configuration based on a predefined dataset

Configuration

The Configuration tab allows fast-configuring the pedometer. In this window, it is possible to visualize in real time the evolution of both the accelerometer signal and the two interrupt lines and to read the pedometer step count output.

The user can enable and configure the pedometer with its default configuration using a dedicated button.

The GUI also allows directly accessing the pedometer-related registers in order to set a user-defined pedometer configuration.

Debug

The Debug tab is used to load a data pattern into the device and run the pedometer logic on the pattern itself (offline postprocessing).

On the right side of the GUI, a three-button toolbar is available for loading a data pattern in the GUI. Pedometer configuration can be loaded from a .ucf or .json file. It is also possible to fast-configure the pedometer in its default configuration by clicking on the Pedometer Easy Configuration button.

The device enters debug mode right after the data pattern has been loaded.

On the top of the GUI, a toolbar is available to control the data injection, which can be applied with the maximum level of flexibility:

Data injection controls:

• Run: injects all samples
• Pause: pauses the data injection process with the possibility to resume it
• Stop: interrupts the data injection process and exits debug mode
• Next: injects the next sample and updates the table with the output data
• Next step: injects a defined number of samples according to the Step lengthsettings
• Export log: exports the content of the table with the results to a .csv file

The data table is updated at every step if the Results at each step switch is enabled, otherwise it is updated only when the data injection is paused or stopped.

Once the debug session is completed, another debug session can be started (the data pattern must be reloaded).

Regression Tool

The Regression Tool tab allows finding an optimal configuration on the basis of a predefined dataset (in this context, a dataset is a collection of data patterns with a reference number of steps for each pattern).

On the right side of the GUI, the initialization view contains the two initialization parameters:

• Error type: the error to be minimized by the tool (that is, mean, mean plus standard deviation, mean plus two times the standard deviation)
• Complexity level: the level of depth (in terms of iterations) of the regression. The execution time of a low-complexity level regression is lower than a high-complexity level, but the parameter space is less deeply explored.

At the top of the GUI, the run view contains the textbox for the input data path and the output file path.

Using dedicated Start and Stop buttons, the user can run the regression. A progress bar is available in order to notify the user about the progress of the regression.

Note: The input data path to be specified in the textbox must be a folder containing only the pedometer data patterns to be applied to the regression session. Each file must contain accelerometer data in a tab or space-separated format in [mg] and collected at the proper output data rate according to the pedometer description in the datasheet. Each filename must contain the ‘stepsXXX’ string, where XXX is the effective reference number of steps (that is, test001_steps100.txt, data_steps54.txt, pattern_steps1043.txt).

Once the regression has been completed, the tool generates a folder under the output folder, named [data]_[hour]_Pedometer, containing two files:

• pedometer.ucf, containing the optimal pedometer configuration generated by the tool
• regression_tool.config, containing some metainformation used by the Regression Tool itself and statistical data about the pedometer performance on the input dataset

Under the output folder, another regression_tool.config file is generated. This file is automatically used by the Regression Tool in order to start a new regression analysis from the optimal configuration found in the latest regression executed. If the user's intention is to run a new regression from scratch (starting from the default pedometer configuration), the user should click on the Clear Regression History button.

The ISPU Model Converter page allows importing pretrained neural network (NN) models from popular ML frameworks. Users can analyze these models in detail, optimize and convert them to .c / .h files, and finally validate the converted model (on the host PC or target using dedicated firmware).

The mentioned .c / .h files might then be included in a custom project (template available in the ISPU GitHub repository in order to build a binary to run the neural network on the ISPU-equipped sensor.

There are four different tabs for this tool:

• Load NN Model / Generate: loads the pretrained neural network model in the application, generates the .c / .h files and a "Generate" textual report related to the NN model conversion process
• Analyze: provides information about the NN model architecture, its memory footprint, and computational complexity. It also generates an “Analyze” textual report from the NN model and represents data in table and radar charts.
• Validate: runs the original and the converted models and compares the results. It also generates a “Validate” textual report from the NN model and represents a summary of data in table, radar chart, and confusion matrix, if applicable.
• Benchmark: allows the user to compare different model results by numerical parameters and graphical representation using radar charts

Load NN Model / Generate page

To start configuring the ISPU Model Converter, the Workspace directory and the Output directory must be selected using the corresponding Browse buttons.

All GUI settings set during neural network processing can be restored to the default values by clicking on the Clear Settings button.

By clicking on the Terminal button, accessible from all pages, the user is able to inspect additional information related to the execution of commands requested in the available pages.

A drag and drop box allows importing NN files with supported formats such as Keras, ONNX, and TFLite (.h5, .onnx, .tflite).

By clicking on the [Display NN graph] button, the user is able to see visual graph of the chosen neural network model. To be able to access this function, the user needs to install the Netron application on the device and add it to the MEMS Studio tool path in the “External Tools” page.

The Model history section stores files previously uploaded by the user, displaying their directory path and the date of upload. Users can reselect files if they still exist in the same directory by clicking the Select button or delete them from the list by clicking on Delete. Deleting a file from the history only removes it from the graphical view, not from the container directory.

Once the workspace directory, output directory, and file to process are set, the Run Generate button becomes active. Users are able to start the generation of reports, .c and .h files by clicking on this button. Once the process is completed, the Inspect Report button becomes active, allowing users to view the textual report generated.

An additional button Open Output Directory is shown to navigate to the output directory.

Analyze page

Once the workspace directory, output directory, and file to process are set, the Run Analyze button becomes active and the user is able to start the analysis of the neural network model. Once the process is completed, the Inspect Report button becomes active, allowing users to view the textual report generated.

Users can view a summary of the report with the following data representation:

• Table: displays structured data extracted from the Analyze report, providing a detailed overview of key information

• Radar Chart: shows data metrics in a radar chart format, offering a graphical representation of different parameters.

Note: All the parameters in the radar chart are normalized in order to present data in a range [0, 1]. They are not in the same scale, so, to be able to show them in a radar chart, they are normalized according to their maximum value.

Considering (as an example) the LSM6DSO16IS sensor, the list of maximum values for radar chart parameters is as follows:

• Total data ram: 8 KiB (activation + input + output size)
• Activation: 8 KiB
• Input: 8 KiB
• Output: 8 KiB
• Total code ram: 32 KiB (weight + code)
• Weight: 32 KiB

The percentage of total ram used by the model is divided into two parts: data ram (8 KiB), which is the sum of activation, input and output size, and code ram (32 KiB), which includes code size and weight size.

Validate page

On the Validate page, the user is able to run the original and converted models and compare the results by clicking on the Run Validate button. The converted model runs on the host PC and on the Target by connecting the sensor and choosing the mode to run On Host/ On Target. Once the process is completed, the Inspect Report button becomes active, allowing users to view the textual report generated. The processed data results are available in three different representations:

• Table: displays real values extracted from the Validate report, providing detailed information about various neural network metrics and parameters
• Radar Chart: displays normalized values in a radar chart format, offering a graphical representation of the key parameter.
  Note: All the parameters in the radar chart are normalized in order to present data in a range [0, 1]. They are not on the same scale, so, to be able to show them in a radar chart, they are normalized according to their maximum value.

The maximum scale for each parameter is:

• Classification accuracy: 100 percent
• Cosine: 1
• Nash-Sutcliffe efficiency: 1

Converter models have more similar results to the original model if the parameters are close to the maximum.

• Confusion Matrix: if available in the report, displays the performance of a classification model, showing true positive, true negative, false positive, and false negative values, assuming the original model as the reference
• Select the output: this combo box appears if the model contains multiple outputs, then the user is able see the result in a table based on the selected output

To be able to "Run Validate" on the Target, after selecting the mode On Target, the user needs to import the UCF file. In order to generate the ISPU UCF file  using MEMS Studio, the ISPU-toolchain path should be set in the “External Tools” page and a valid validation template should be selected. MEMS Studio embeds a template and link to download it. Once the Generate Validate UCF request is completed, a new .ucf file is created in the output directory in the subdirectory ispu_validation_out /make/bin/ispu.ucf

By default, the validation of the selected neural network is executed using random data. The user can select custom input and output to quantize using custom data.

Benchmark page

On the Benchmark page, the user can compare different neural network models through numerical and visual results. This functionality allows for the comparison of the same model in different validation modes (On Host / On Target).

A drag and drop box allows importing neural network report files with the supported format as text (.txt).

By clicking on the Export results button, the user is able to extract the results of the entire benchmark table as a .csv file.

By clicking on the Show Chart button, the user is able to see multiple layers and compare the parameter visually.

The radar chart viewer displays normalized values in a radar chart format, offering a graphical representation of the key parameter in multiple layers of the selected model.

Note: All the parameters in the radar chart are normalized in order to present data in a range [0, 1]. They are not on the same scale, so, to be able to show them in a radar chart, they are normalized according to their maximum value.

The maximum scale for each parameter is:

• Classification accuracy: 100 percent
• Cosine: 1
• Nash-Sutcliffe efficiency: 1
• Total data ram: 8 KiB (activation + input + output size)
• Activation: 8 KiB
• Input: 8 KiB
• Output: 8 KiB
• Total code ram: 32 KiB (weight + code)
• Weight: 32 KiB

The converter models have more similar results to the original model if the parameters are close to the maximum.

By clicking on the Save button, the user is able to store the chart viewer results as a .png file.

The IR Algorithms Tuning tool is designed to fine-tune infrared sensor settings to accurately detect presence, motion, and ambient shock.

The tool is divided in four tabs. The first tab is for presence detection, the second for motion detection, the third one for ambient shock detection, and the fourth for sequential processing of multiple files.

To begin using the tool, you need to load previously acquired data. Follow these steps:

1. Click on the Browse button.
2. Select the file containing the data.
3. Choose the columns corresponding to t_object and t_ambient data.
4. The data is displayed in charts for further analysis.

In the Presence Detection section, you can configure the following parameters:

• T_Presence Abs Mode: enables or disables absolute mode.
• Threshold [LBS]: sets the threshold value for presence detection.
• Hysteresis [LBS]: sets the hysteresis value for presence detection.
• LPF cut-off (Presence): sets the cutoff frequency for the low-pass filter specific to presence detection.
• LPF cut-off (Presence & Motion): sets the cutoff frequency for the low-pass filter that applies to both presence and motion detection.

In the Motion Detection section, you can configure the following parameters:

• Threshold [LBS]: sets the threshold value for motion detection.
• Hysteresis [LBS]: sets the hysteresis value for motion detection.
• LPF cut-off (Motion): sets the cutoff frequency for the low-pass filter specific to motion detection.
• LPF cut-off (Presence & Motion): sets the cutoff frequency for the low-pass filter that applies to both presence and motion detection.

In the Ambient Shock Detection section, you can configure the following parameters:

• Threshold [LBS]: sets the threshold value for ambient shock detection.
• Hysteresis [LBS]: sets the hysteresis value to prevent false detections.
• LPF cut-off (Motion): sets the cutoff frequency for the low-pass filter specific to motion detection.
• LPF cut-off (Ambient Shock): sets the cutoff frequency for the low-pass filter specific to ambient shock detection.

The toolbar provides several essential functions for managing your configurations and simulations:

• Run processing: executes the simulation based on the current settings.
• Save log: saves the results of the simulation to a file.
• Read configuration from sensor: loads the current configuration settings from the sensor.
• Write configuration from sensor: writes the current configuration settings to the sensor.
• Load configuration from file: reads the configuration settings from a file.
• Save configuration from file: writes the current configuration settings to a file.
• Restore defaults: resets all settings to their default values.

The Sequential Processing tab is designed to process multiple files sequentially. Follow these steps:

1. Click on the Browse button.
2. Select a folder containing the files to be processed.
3. The tool processes each file one by one.
4. The results are saved to the respective files.

The data analysis feature allows visualizing and analyzing data in the time and frequency domains. In the frequency domain, the frequency spectrum from part of or the whole data log can be calculated using fast Fourier transform (FFT). Another option of frequency domain analysis is to use a spectrogram, which visualizes frequency spectrum progress over time.

Time domain analysis is intended for data visualization, measuring, editing, and assigning labels to specific parts of the data.

Previously acquired data logs can be opened by clicking on the Browse button.

After successfully loading the data log, data are visualized in the line chart. The user can navigate through the chart in the same way as other charts in the application. Moving the cursor to the Y-axis offers a context menu for the chart.

There are two options for data visualization. Data can be visualized as individual points or as lines connecting all the measured values.

The chart offers the possibility to measure various values. This can be enabled by switching from Cursor mode to Measure mode. Two cursors are displayed in the chart, their X and Y positions can be adjusted using the mouse. The corresponding X and Y values and their differences are displayed in the table bellow. Cursors can be hidden by closing the cursor table in the chart area.

The data log can be edited and labels assigned if the Cursor mode is switched to Select mode. The start of the area is marked by clicking at the desired position in the graph, the start cursor is placed at this position, then the signal is selected by mousing over it, the next click defines the end of the selected area, and the end cursor is placed at this position. The following operations are available with the selected area:

• Delete Selection: deletes the selected part of the signal
• Crop Selection: deletes all parts except the selected area
• Set Label: assigns a label to the selected part, the existing label name can be selected, or a new label created
• Set Default Label: sets a label to all unlabeled parts
• Clear All Labels: removes all labels
• Clear Selected Labels: removes the label from the selected part
• Save Selection: saves the selected part to a new file

There are also other operations for data labeling:

• Save Labeled Data: saves each data for each label to a separate file
• Save All Without Labels: saves all changes without assigned labels to one file
• Save All: saves all changes including assigned labels to one file

Frequency domain analysis is used to analyze a frequency spectrum using fast Fourier transformation (FFT). If a data log was already selected on the Time Domain page, it is automatically used on the Frequency Domain page. Other data logs can be selected using the Browse button.

It is very important to know the sampling rate of the data for frequency domain analysis. If there is a timestamp in the data log, the sampling rate is calculated from this value. In the case of several sampling rates used in one data log, the signals are grouped according to each sampling rate and are analyzed separately. The active group can be selected by the user. If the timestamp is not available, the sampling rate must be entered by the user.

The frequency spectrum can be calculated from the entire signal or from a selected area. If Automatic evaluation is enabled, these two modes are automatically switched with respect to the part of the signal that is selected. Otherwise, the following buttons can be used:

• Compute FFT / Compute PSD: calculates the frequency spectrum from the entire signal
• Selected Data FFT / Select Data PSD: calculates the frequency spectrum from the selected area

If the data log contains labels, a labeled part of the signal can be quickly selected by clicking on the label.

A graph with the frequency spectrum can be configured using the following options:

• Magnitude [Linear, dB]: specifies the scale on the Y-axis
• Frequency [Linear, Logarithmic]: specifies the scale on the X-axis
• Style [Bar, Lines]: specifies visualization of the values

A frequency domain analysis can be configured using the following options:

• DC nulling: removes the DC component from the signal
• Window: window function multiplied by the signal before frequency spectrum calculation in order to reduce the amplitude of the discontinuities at the boundaries of the signal part
• Length: number of samples used for the frequency spectrum calculation
• Zero padding: number of zeros added to the signal in order to increase resolution
• Window overlap: percentage of the previous window reused for the evaluation of the next window

A spectrogram analysis is used to analyze the evaluation of the frequency spectrum over time. If a data log was already selected on the Time Domain or Frequency Domain page, it is automatically used on the Spectrogram page. Other data logs can be selected using the Browse button.

As the spectrogram utilizes the same fast Fourier transform as the frequency domain analysis, the same parameters can be configured also on the Spectrogram page.

AlgoBuilder is a tool for the graphical design of algorithms.

This tool quickly creates prototypes of applications for STM32 microcontrollers and MEMS sensors, including already existing algorithms (in the example of sensor fusion), user-defined data processing blocks, and additional functionalities.

The tool facilitates the process of implementing a proof of concept using a graphical interface without writing the code.

The workflow starts from the graphical design of the desired functionality by using a simple "drag and drop" approach.

The flow diagram is composed of the predefined function blocks provided in the libraries. Custom function blocks can be created as well. Some function blocks have properties that can be adjusted.

Then, function blocks can be interconnected. AlgoBuilder automatically checks the compatibility between input and output and allows connecting only terminals with the same type and dimension.

When the design is finished, AlgoBuilder generates the C code from the defined graphical design. The final firmware project is created from the C code generator, combined with preprepared firmware templates and binary libraries.

The project can be compiled using an external compiler tool and the most common compilers are supported (STM32CubeIDE, Arm^® Keil^® μVision^®, IAR Embedded Workbench).

A development board is then programmed by the generated binary file.

When the firmware is executed, it starts reading data from the selected sensor, processes the data via the algorithm, and sends the results to the MEMS Studio application. During the graphical design, you can select how to see the results. Many options like graphs, logical analyzers, bar charts, 3D plots, scatter plots, and others are supported. During the startup, the firmware configures the UI to display the data in the desired format.

The graphical designs as well as the libraries are stored as .json files.

The AlgoBuilder tool is controlled from a toolbar that offers all the necessary functions.

The algorithm design under development is created in the workspace area.

The Library dock gives you access to all the available libraries and function blocks located in a particular library.

AlgoBuilder scans [Install path]\release\Library\ and the user's home directory \STMicroelectronics\MEMS Studio\algobuilder\library during startup and loads all valid libraries located there.

The Subdesigns dock gives you access to all the available subdesigns. AlgoBuilder scans [Install path]\release\Subdesigns\ and the user's home directory\STMicroelectronics\MEMS Studio\algobuilder\subdesigns during startup and loads all valid subdesigns located there. The subdesign templates are located in the [Install path]\release\Subdesigns\ directory.

The Description dock provides information about the component selected in the workspace or in the Library dock.

If you select a function block, the following information is shown:

• Name
• Version
• Description of the function block functionality
• Type, size, and functionality of all inputs and outputs
• Description of all function block properties

If a function block has a property or properties, they are displayed in the Properties dock.

Each property has Name, Value, and Type fields.

The values can be modified.

AlgoBuilder automatically checks if the value is valid and does not allow setting an invalid value (for example, a value out of an available range).

For the STRING type, the % character is forbidden and is automatically deleted.

The toolbar provides quick access to all needed functions.

Table 6. AlgoBuilder toolbar functions

Toolbar icon	Function
	Create new design
	Open existing design
	Save design (subdesign)
	Save design (subdesign) as a different file
	Undo
	Redo
	Copy
	Paste
	Cut
	Delete
	Zoom in
	Zoom out
	Zoom to default
	Fit whole design into the window
	Align blocks to the left
	Align blocks to the right
	Center blocks horizontally
	Align blocks to the top
	Align blocks to the bottom
	Center blocks vertically
	Project configuration
	Initialize project directory
	Generate code
	Build project
	Rebuild project
	View design source file
	View generated source C file
	Program device by automatic selected method
	Display order configuration
	Add or remove conditional input
	Custom Block Creator

Already prepared algorithms in binary form can also be integrated in the user design.

Binary libraries are included in the firmware only if at least one function block is used in the design. This reduces occupied flash memory and RAM memory.

Each input or output is characterized by its type and size, the color of the connection line indicates the type.

Important: Only input and output with the same type and size can be connected together. The only exception is the VARIANT input, which gets the type of the connected output.

It is not possible to connect the input and output of the same function block. If this is desired, the Feedback function block for the particular data type needs to be used. The Feedback function block has an Init value, which defines the output value of the block for the first run.

Use the appropriate function block according to your requirements for data visualization.

Use the Angle Level function block in the design to display data as an angle level indicator. This graph works with any floating or integer value and each of them can have up to three indicators. In the Properties field, you can set the name of the graph as well as the name of each angle level indicator with unit. Predefined configurations are available in the Preset Values property.

Use the Bar Graph function block in the design to display data as a bar graph. A bar graph is suitable for a quick check of an actual value without needing to see the history. This graph works with any floating or integer value and each of them can have up to six bars. In the Properties field, you can set the name of the graph as well as the name of each bar, unit on the Y-axis, position of 0 on the Y-axis, full scale, and enable or disable autoscale. Predefined configurations are available in the Preset Values property.

Use the Fusion function block in the design to display quaternion data on a 3D model. The 3D model (teapot, Nucleo board, car, head) is usually used to check device orientation in 3D space. This graph requires quaternions, which can be obtained, for example, from the sensor fusion (MotionFX) algorithm.

Use the Graph function block in the design to display data as a time graph. This graph works with any floating or integer value and each of them can have up to six waveforms. In the Properties field, you can set the name of the graph as well as the name of each waveform, unit on the Y-axis, position of the 0 on the Y-axis, full scale, and enable or disable autoscale. Predefined configurations are available in the Preset Values property.

Use the Logic Analyzer function block in the design to display logic signals, which can have values of only 0 or 1. The logic analyzer can have up to 16 channels. In Properties, you can change the name of each channel and the logic analyzer.

Use the Plot3D function block in the design to display X, Y, Z data as points in 3D space. This graph works with any floating or integer value. In the Properties field, you can set the name of the graph as well as the name of each value, unit, full scale, and enable or disable autoscale. Predefined configurations are available in the Preset Values property.

Use the Scatter function block in the design to display X, Y, Z data in 2D space (X-Y, X-Z, Y-Z charts). In the Properties field, you can set the name of the graph as well as the name of each value, unit, full scale, and enable or disable autoscale. Predefined configurations are available in the Preset Values property.

Use the Value function block in the design to display the exact float or integer value as text. Each function block can display up to eight values. In Properties, you can change the name of the value and unit for each item.

Three types of data can be sent from the MEMS Studio application to run the firmware: binary, integer, and float. This can be done by adding the Input Value function block (with the appropriate type) from the User Input library to the design. Each Input Value function block can represent up to four values. In the Properties, it is possible to set the name of each value and the default value. The Input Value function block output size is defined by the Number of Values property.

In some cases, it is needed to execute the function block operation only if a certain condition is valid. For this case, it is possible to add Conditional Execution Input to the selected function block. This input then defines if the function block code is executed or not. To add or remove the conditional execution input to the selected function block, click on the (Add or Remove Conditional Input) icon in the toolbar.

AlgoBuilder offers a function block for the frequency analysis of the sensor’s output signal using FFT (fast Fourier transform). Fourier analysis converts a signal from the time domain to a representation in the frequency domain.

The FFT function block offers frequency analysis from 32, 64, 128, 256, 512 and 1024 samples. It is also possible to enable window usage to eliminate spectrum leakage. Hanning, Hamming, and Flat Top windows can be used.

Output from the FFT function block can be connected to the FFT plot function block, which visualizes the data as a frequency spectrum. To plot data only when the FFT calculation is finished, conditional execution input must be added to the FFT plot and connected to the full output of the FFT function block.

AlgoBuilder allows using outputs from the finite state machine (FSM) and/or machine learning core (MLC) in the design. The FSM/MLC function block is available in the Sensor Hub library.

The configuration of the FSM and MLC is stored in the .json file. The .json file can be created in the Advanced Features page.

It is mandatory to specify the number of FSM and MLC outputs used and the sensor name in the .json file in case the .json contains more configurations. The size of the FSM/MLC output vector is adjusted accordingly.

Warning:

The .json file usually contains settings of the sensor full scale (FS) and output data rate (ODR). These settings might be different than the settings required by the sensor hub. The firmware generated by AlgoBuilder contains a function to check if the FS and ODR set by the .ucf file is compatible with the sensor hub settings. If not, an error message is generated during connection to the device.

The interrupt pin INT1 is usually configured by the MLC tool in which case it is not possible to use the accelerometer or gyroscope as Data Rate Control in the sensor hub.

The FSM/MLC function block can be used only with sensors that are equipped with this functionality.

AlgoBuilder also allows using outputs from the intelligent sensor processing unit (ISPU) in the design. The ISPU function block is available in the Sensor Hub library.

The configuration of the ISPU is stored in the .json file. The path to the .json file is the Property of the ISPU function block. The .json file can be created using the ISPU-Toolchain software. An output description needs to be present in the same file with the ISPU configuration. The outputs of the ISPU function block are automatically adjusted according to the output description structure in the .json file.

Supported types of outputs are the following:

• int8_t, int16_t, int32_t, uint8_t, uint16_t, uint32_t
• float

Arrays might be defined using a size key (that is, “size”: 3).

An example of the output description file is as follows:

"outputs": [
  {
    "name": "Acc x [LSB]",
    "core": "ISPU",
    "type": "int16_t",
    "len": "1",
    "reg_addr": "0x10",
    "reg_name": "ISPU_DOUT_00_L",
    "results": [
    ]
  },
  {
    "name": "Acc y [LSB]",
    "core": "ISPU",
    "type": "int16_t",
    "len": "1",
    "reg_addr": "0x12",
    "reg_name": "ISPU_DOUT_01_L",
    "results": [
    ]
  },
  {
    "name": "Acc z [LSB]",
    "core": "ISPU",
    "type": "int16_t",
    "len": "1",
    "reg_addr": "0x14",
    "reg_name": "ISPU_DOUT_02_L",
    "results": [
    ]
  },
  {
    "name": "Acc norm [LSB]",
    "core": "ISPU",
    "type": "float",
    "len": "1",
    "reg_addr": "0x16",
    "reg_name": "ISPU_DOUT_03_L",
    "results": [
    ]
  }
]

During firmware startup, the ISPU is configured according to the .json file and the output values are then read by the MCU and used in the AlgoBuilder flow.

Warnings:

The .json file also usually contains settings of the sensor full scale (FS) and output data rate (ODR). These settings might be different than the settings required by the sensor hub. The firmware generated by AlgoBuilder contains a function to check if the FS and ODR set by the .ucf file is compatible with the sensor hub settings. If not, an error message is generated.

If the interrupt pin INT1 is used by the ISPU algorithm, it is not possible to use the accelerometer or gyroscope as Data Rate Control in the Sensor Hub.

The ISPU function block can be used only with sensors that are equipped with this functionality.

The connected target can be programmed directly from the AlgoBuilder interface. After a successful build of the firmware, the target can be programmed by clicking on the icon (Program target by automatically selected method).

The following programming methods are supported:

• STLINK interface
• DFU (device firmware upgrade)
• Copy to flash drive

The STM32CubeProgrammer CLI application is used to program the STM32 microcontroller with an STLINK or DFU interface. The path to the STM32CubeProgrammer CLI application must be set in the MEMS Studio settings in the External Tools page. Copying to the flash drive is available for STM32 Nucleo boards and does not require an STM32CubeProgrammer CLI application.

The programming method is automatically selected with the following priority:

1. STLINK interface
2. Copy to flash drive
3. DFU (device firmware upgrade) interface

DFU mode allows programming the target without requiring a programmer. DFU mode is available for devices with a USB interface, for example SensorTile.box Pro. By pressing the Program button, a dedicated window for DFU is opened. There are two options for switching the device to DFU mode. The procedures are described in the window. After the device is switched to DFU mode, the memory is automatically erased and programmed by the new firmware.

AlgoBuilder offers the possibility to create a multilevel design. This feature allows encapsulating part of the design into a standalone part called subdesign. This subdesign is represented by a single function block and can be used multiple times in the main design and in all other designs. The subdesign can be shared between users. The subdesigns significantly increase the clarity of a highly complex project.

Subdesigns can be created by double-clicking on the particular template. Existing subdesigns, listed in the Subdesigns dock or used in the design, can be opened in the same way. If a new or an existing subdesign is open, AlgoBuilder creates a dedicated tab in the workspace.

The subdesign can be saved in the same way as the main design by clicking on the icon (Save Design) or (Save Design As). The subdesign can then be used in the main design or other subdesign. To use the subdesign, drag and drop the particular item from the Subdesigns dock to the workspace. To be able to connect a subdesign with another function block, input and output terminals must be defined for each subdesign. The terminals can be added from the Subdesign library, which is active only if a subdesign is being edited.

Each terminal has three properties: size, name, and description. The size and name must be defined, moreover the name must be unique in the subdesign. The description is optional. In the graphical design, the subdesign is represented by the same rectangle with inputs and outputs as function blocks.

The templates for the for and while loop contain several function blocks that are mandatory. These blocks cannot be deleted. Mandatory function blocks consist of the following:

AlgoBuilder offers a Custom Block Creator that allows creating user-defined function blocks. The Custom Block Creator can be opened from the toolbar by pressing the button. User-defined function blocks are stored in the user libraries, which are located in the user’s home directory \STMicroelectronics\MEMS Studio\algobuilder\library. The Custom Block Creator also offers the possibility to edit or delete an already existing custom function block and custom libraries.

The custom block configuration is divided into several sections. In the Block Info section, Block Name, Display Name, Major Version, Minor Version, and Block Description can be defined.

In the Inputs section, Input Name, Data Type, Constant Size, Variable Size, and Input Description can be defined. The input parameters as well as the input order can be modified.

In the Outputs section, Output Name, Data Type, Constant Size, Variable Size, and Output Description can be defined. The output parameters as well as the output order can be modified.

In the Properties section, Property Name, Data Type, Value (or Enum Values), and Property Description can be defined. The property parameters as well as the property order can be modified.

In the Code section, a C code that represents the function block functionality can be entered. The input and output are referenced in C code using NAME[index] where NAME is the name of the input or output and index is the number of the item in the array. All inputs and outputs are represented by array so the index is mandatory even if only one item is in the array. Properties are scalar so the index is not used and only the property name is used as the reference.

The Firmware Programming page offers quick development board (STM32 microcontroller) programming in the example to update firmware in the ProfiMEMS board. The following programming methods are supported:

• STLINK interface
• DFU (device firmware upgrade)
• Mass storage

The STM32CubeProgrammer CLI application is used to program the STM32 microcontroller with an STLINK or DFU interface. The path to the STM32CubeProgrammer CLI application must be set in the MEMS Studio settings in the External Tools page. Copying the binary file with the firmware to the mass storage drive is available for the STM32 Nucleo boards and does not require the STM32CubeProgrammer CLI application.

The user first selects the binary file with the firmware for the STM32 microcontroller using the Browse button. Then the user selects the programming interface. Based on the selected interface, the list of devices is populated. The list can be updated by pressing the Refresh button. Programming is executed by clicking on the Program button.

The Firmware Directory button offers quick access to the folder where the firmware distributed together with MEMS Studio is located.

DFU mode allows programming the target without requiring a programmer. DFU mode is available for devices with a USB interface, for example the ProfiMEMS board (STEVAL-MKI109D, STEVAL-MKI109V3).