Experimental Analysis of Artificial Neural Networks Performance for Physical Activity Recognition Using Belt and Wristband Devices

Physical activity (PA) is widely recognized as one of the important elements of personal healthy life. To date, as the development of wearable sensing technologies, it is possible to utilize wearable devices and machine learning algorithms to efficiently and accurately monitor PA types, intensity and its associated human pattern for many health applications. But there is a trade-off between less-attachment of wearable devices and achievement of high accuracy in existing PA recognition studies. This paper attempts to investigate possible utilisation of Artificial Neural Networks (ANN) achieving high recognition accuracy of PA using less-attachments of wearable devices. Following a four-steps designed experimental protocol, we collect the real activities dataset with only belt and wristband devices from 10 healthy subjects at home and gym environment. The parameters of typical PA recognition with ANN including time window sizes, features and activation functions are evaluated under 24 different subjects of activities. The experimental results indicate that ANN dealing with belt and wristband data can achieve satisfactory PA recognition results in dynamic and sedentary activities but suffers from transitional activities in both environments.