Characterizing Peaks in Acceleration Signals–Application to Physical Activity Detection Using Wearable Sensors

The world is getting older by the minute due to rising life expectancy, leading to an urgent need for the continuous monitoring of patients. Tracking human activities without hospitalization has been tackled in the past decade thanks to the advancement of sensing technologies and wearable devices. However, the limited power resources of microcontrollers and the power consumption due to the use of different sensors are two issues that make the recognition process via embedded systems an open research topic to date. Consequently, this paper proposes a low-cost machine learning-based human activity recognition algorithm. It detects cyclic activities from wrist-worn tri-axial accelerometer data and classifies them into four classes. Specifically, a novel and smart peak detection technique is proposed, followed by the extraction of small handcrafted feature vectors, representing the input of a novel machine-learning architecture. These three contributions are approved by experimental results on a 3300-file dataset, showing an accuracy of 99.21% with a low computational cost thanks to an efficient implementation for feature extraction. The effectiveness of the proposed recognition process is validated in real world conditions.