Online human movement classification using wrist-worn wireless sensors

• Published in: Journal of ambient intelligence and humanized computing Vol. 10; no. 1; pp. 89 - 106
• Main Authors: Sarcevic, Peter, Kincses, Zoltan, Pletl, Szilveszter
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.01.2019; Springer Nature B.V
• Subjects: Accelerometers; Algorithms; Artificial Intelligence; Classification; Computational Intelligence; Data acquisition; Datasets; Discriminant analysis; Efficiency; Energy consumption; Engineering; Feature extraction; Human mechanics; Human motion; Investigations; Methods; Original Research; Prototypes; Radio frequency identification; Robotics and Automation; Sensors; User Interfaces and Human Computer Interaction; Wearable computers; Wrist; Activity recognition; Dimension reduction; Feature extraction; Time-domain analysis; Wearable sensors
• ISSN: 1868-5137; 1868-5145
• DOI: 10.1007/s12652-017-0606-1

The monitoring and analysis of human motion can provide valuable information for various applications. This work gives a comprehensive overview about existing methods, and a prototype system is also presented, capable of detecting different human arm and body movements using wrist-mounted wireless sensors. The wireless units are equipped with three tri-axial sensors, an accelerometer, a gyroscope, and a magnetometer. Data acquisition was done for multiple activities with the help of the used prototype system. A new online classification algorithm was developed, which enables easy implementation on the used hardware. To explore the optimal configuration, multiple datasets were tested using different feature extraction approaches, sampling frequencies, processing window widths, and used sensor combinations. The applied datasets were constructed using data collected with the help of multiple subjects. Results show that nearly 100% recognition rate can be achieved on training data, while almost 90% can be reached on validation data, which were not utilized during the training of the classifiers. This shows high correlation in the movements of different persons, since the training and validation datasets were constructed of data from different subjects.