Human activity recognition has become an attractive research area with the development of on-body wearable sensing technology. Textiles-based sensors have recently been used for activity recognition. With the latest electronic textile technology, sensors can be incorporated into garments so that users can enjoy long-term human motion recording worn comfortably. However, recent empirical findings suggest, surprisingly, that clothing-attached sensors can actually achieve higher activity recognition accuracy than rigid-attached sensors, particularly when predicting from short time windows. This work presents a probabilistic model that explains improved responsiveness and accuracy with fabric sensing from the increased statistical distance between movements recorded. The accuracy of the comfortable fabric-attached sensor can be increased by $\mathrm{67\%}$ more than rigid-attached sensors when the window size is $0.5$$\mathrm{s}$. Simulated and real human motion capture experiments with several participants confirm the model’s predictions, demonstrating that this counterintuitive effect is accurately captured.

Human motion analysis plays a crucial role in fields such as healthcare, human-robot interaction and virtual reality. Conventional approaches typically rely on tightly attached body sensors, which can prove uncomfortable and impractical. Here, we investigate motion recognition and prediction using garments incorporating embedded sensors. We analyse how the movement of loose-fitting clothing can predict body motion in both simulated and real-world scenarios. Results demonstrate that sensors attached to fabric can improve recognition accuracy by up to 40% improvement and require approximately 80% less movement history compared to sensors directly attached to the body. These findings indicate that garment motion provides valuable information for analysing human movement. The study additionally offers insights regarding the design of intelligent textiles with integrated sensing capabilities.

The integration of sensors into garments has paved the way for human activity recognition (AR), enabling users to engage in extended human motion recordings. The inherent fluidity of loose clothing allows it to mirror the wearer’s movements. From a statistical standpoint, clothing captures additional valuable insights beyond rigid body motions, improving AR. This work demonstrates how fabric’s orientation, layering and width contribute to the enhanced performance of AR with clothing in periodic motion. Experiments are reported in which a scotch yoke and a KUKA robot manipulator are used to induce the periodic motion of fabric cloth at different frequencies. These reveal that clothing-attached sensors exhibit higher frequency classification accuracy among sensors with an improvement of 27% for perpendicular-oriented fabric, 18% for triple-layered fabric, and 9% for large-width fabric, exceeding that seen with rigid attached sensors.

This work presents an empirical study into the design of fabric pressure sensors, taking into account the electro-mechanical variability of spacer fabric. The saturation of the fabric sensor at high force levels is a major factor limiting the range of measurable pressures. In order to address this, experiments were carried out to investigate several methods of modifying the sensitivity characteristics through the layering and specifications of the textile sheets.

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.