Human motion prediction and trajectory forecasting are essential in human motion analysis. Nowadays, sensors can be seamlessly integrated into clothing using cutting-edge electronic textile (e-textile) technology, allowing long-term recording of human movements outside the laboratory. Motivated by the recent findings that clothing-attached sensors can achieve higher activity recognition accuracy than body-attached sensors. This work investigates the performance of human motion prediction using clothing-attached sensors compared with body-attached sensors. 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. Counterintuitively, the results show that fabric-attached sensors can have better motion prediction performance than rigid-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 prediction accuracy (i.e., 95%) compared to the rigid-attached sensor.

翻译：人体运动预测与轨迹预测是人体运动分析中的核心问题。当前，通过前沿电子纺织（e-textile）技术可将传感器无缝集成至衣物中，实现实验室环境外对人体运动的长期记录。受最新研究发现（衣物附着传感器较身体附着传感器可达到更高活动识别精度）的启发，本工作探究了利用衣物附着传感器与身体附着传感器进行人体运动预测的性能差异。本文通过实验证明，基于宽松衣物运动习得的统计模型，可有效预测机器人模拟及真实人体行为的运动模式。违反直觉的是，实验结果显示织物附着传感器比刚性附着传感器具有更优的运动预测表现。具体而言，相较刚性附着传感器，织物附着传感器可提升最高40%的预测精度，且在达到95%高预测精度时，所需历史轨迹时长可缩短达80%。