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关键任务。当前，利用前沿的电子纺织（e-textile）技术可将传感器无缝集成至衣物中，从而在实验室外长期记录人体运动。受近期研究发现（衣物附着传感器比身体附着传感器能实现更高的活动识别准确率）的启发，本文探究了相较于身体附着传感器，使用衣物附着传感器进行人体运动预测的性能表现。本文报告了一系列实验，其中利用从松衣运动中习得的统计模型，预测机器人模拟和真实人体行为中的身体运动模式。反直觉的是，研究结果表明，织物附着传感器比刚性附着传感器具有更优的运动预测性能。具体而言，与刚性附着传感器相比，织物附着传感器可将预测准确率提升高达40%，且达到高预测准确率（如95%）所需的过去轨迹时长减少多达80%。