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Related Experiment Video

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Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision

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Microcontroller Implementation of LSTM Neural Networks for Dynamic Hand Gesture Recognition.

Kevin Di Leo¹, Giorgio Biagetti¹, Laura Falaschetti¹

¹DII-Dipartimento di Ingegneria dell'Informazione, Università Politecnica delle Marche, Via Brecce Bianche 12, I-60131 Ancona, Italy.

Sensors (Basel, Switzerland)

|June 27, 2025

Summary

This summary is machine-generated.

This study demonstrates high accuracy in hand-gesture recognition using accelerometers on microcontrollers. The system achieves over 90% accuracy for human motion recognition, even on resource-constrained devices.

Keywords:

LSTM STM32 accelerometer embedded systems hand gesture recognition microcontroller neural networks

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Area of Science:

Wearable technology
Machine learning for human motion analysis
Embedded systems

Background:

Accelerometers in portable devices offer unobtrusive human motion data acquisition.
Human motion recognition is crucial for human-computer interaction and health monitoring.

Purpose of the Study:

To evaluate the performance of a Long Short-Term Memory (LSTM) neural network for hand-gesture classification on a microcontroller.
To analyze the trade-offs between accuracy and resource utilization for embedded human motion recognition systems.

Main Methods:

Utilized a publicly available dataset of 20 hand gestures from 10 subjects using wrist-worn accelerometers.
Implemented an LSTM neural network model for gesture classification.
Deployed and evaluated the model on an STM32L4-series microcontroller.

Main Results:

Achieved nearly 90.25% accuracy for hand-gesture classification.
Inference time was 418 ms for 4-second sequences.
Average CPU usage was approximately 10% for the recognition task.

Conclusions:

LSTM neural networks can effectively perform hand-gesture recognition on resource-constrained microcontrollers.
The system demonstrates a viable balance between high accuracy and efficient resource utilization for embedded applications.