Toward unsupervised Human Activity Recognition on Microcontroller Units

Bringing artificial intelligence to embedded devices has become a central research topic in many scientific domains (environment, agriculture, sociology, health…). For Human Activity Recognition, Artificial Neural Networks (ANNs) have shown their capability to provide better performance compared to other machine learning methods. However, ANNs suffer from two major limitations. First, ANNs are often trained using supervised learning requiring labelled databases, which are often difficult to build in real applications. Then, those algorithms are usually very expensive in terms of computing power. For that reason, their integration into low-power microcontrollers has been so far only evaluated to a limited extent. In this paper, we propose to evaluate quantitatively and qualitatively the embedded implementation of different neural networks for human activity recognition. First, supervised learning approaches are presented, followed by an exploratory study of unsupervised learning approaches using Self-Organizing Maps. Finally, some aspects of embedded unsupervised online learning are investigated to improve classification results using subject-specific data over a more general training. Each neural network is tested on a Human Activity Recognition dataset acquired from a smartphone using accelerometer and gyroscope sensing information (UCI HAR) and deployed on the SparkFun Edge board. This board hosts a low-power ARM Cortex-M4F-based microcontroller.