Human Movement Prediction with Wearable Sensors on Loose Clothing

Human motion recognition and motion prediction are essential in human motion analysis. Nowadays, sensors can be seamlessly integrated into clothing using cuttingedge electronic textile (e-textile) technology, allowing longterm recording of human movements outside the laboratory. Motivated by the recent findings that clothing-attached sensors can achieve higher activity recognition accuracy than bodyattached sensors, this work investigates the performance of human motion prediction. It reports experiments in which statistical models learnt from the movement of loose clothing are used to predict motion patterns of the body of robotically simulated and real human behaviours. Counter-intuitively, the results show that fabric-attached sensors can have better motion recognition and prediction performance than rigidly-attached sensors. Specifically, The fabric-attached sensor can improve the accuracy up to $40 \%$ and requires up to $80 \%$ less duration of the past trajectory to achieve high recognition accuracy (i.e., 95%) compared to the rigidly-attached sensor.