Abstract
In this paper, we demonstrate an inertial measurement unit (IMU) based hand gesture recognition (HGR) on a lowpower microcontroller (STM32L476JGY) by using deep neural networks (DNN). To train the DNNs, a dataset was recorded which contains accelerometer and gyroscope readings from three IMUs mounted on the fingertips. With this dataset, various neural networks (NN) were trained and analysed. The best NN, in terms of accuracy and memory usage, was then selected and ported to the microcontroller. Finally, a runtime analysis of the model has been performed on the controller. The analysis showed that a LSTM is best suited for the detection of hand gestures. The selected model achieves an accuracy of 93 % and only takes up around 40 KiB of memory. In addition, the model has a throughput time of only 3.52 ms, which means that the prototype
can be used in real time.
can be used in real time.
Original language | English |
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Publication status | Accepted/In press - 2 Sept 2022 |
Event | 21st IEEE International Conference on Machine Learning and Applications - Nassau, Bahamas Duration: 12 Dec 2022 → 15 Dec 2022 https://www.icmla-conference.org/icmla22/howtosubmit.html |
Conference
Conference | 21st IEEE International Conference on Machine Learning and Applications |
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Abbreviated title | ICSIMA |
Country/Territory | Bahamas |
City | Nassau |
Period | 12.12.2022 → 15.12.2022 |
Internet address |
Keywords
- HGR
- DNN
- LSTM
- Microcontroller
- IMU