Recurrent Neural Network for Human Activity Recognition in Embedded Systems Using PPG and Accelerometer Data

Electronics

Laura Falaschetti¹  Giorgio Biagetti¹  Michele Alessandrini¹  Claudio Turchetti¹  Paolo Crippa¹ 

[1] DII—Department of Information Engineering, Università Politecnica delle Marche, I-60131 Ancona, Italy;

关键词: recurrent neural network (RNN);    deep neural network (DNN);    photoplethysmography (PPG);    accelerometer;    human activity recognition (HAR);    embedded system;   

DOI  :  10.3390/electronics10141715

来源: DOAJ

【 摘 要 】

Photoplethysmography (PPG) is a common and practical technique to detect human activity and other physiological parameters and is commonly implemented in wearable devices. However, the PPG signal is often severely corrupted by motion artifacts. The aim of this paper is to address the human activity recognition (HAR) task directly on the device, implementing a recurrent neural network (RNN) in a low cost, low power microcontroller, ensuring the required performance in terms of accuracy and low complexity. To reach this goal, (i) we first develop an RNN, which integrates PPG and tri-axial accelerometer data, where these data can be used to compensate motion artifacts in PPG in order to accurately detect human activity; (ii) then, we port the RNN to an embedded device, Cloud-JAM L4, based on an STM32 microcontroller, optimizing it to maintain an accuracy of over 95% while requiring modest computational power and memory resources. The experimental results show that such a system can be effectively implemented on a constrained-resource system, allowing the design of a fully autonomous wearable embedded system for human activity recognition and logging.

【 授权许可】

Unknown