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.
Original languageEnglish
Publication statusAccepted/In press - 2 Sept 2022
Event21st IEEE International Conference on Machine Learning and Applications - Nassau, Bahamas
Duration: 12 Dec 202215 Dec 2022


Conference21st IEEE International Conference on Machine Learning and Applications
Abbreviated titleICSIMA
Internet address


  • HGR
  • DNN
  • LSTM
  • Microcontroller
  • IMU


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