Trajectory Forecasting with Loose Clothing Using Left-to-Right Hidden Markov Model

Trajectory forecasting has become an interesting research area driven by advancements in wearable sensing technology. Sensors can be seamlessly integrated into clothing using cutting-edge electronic textiles 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 motion prediction and trajectory forecasting using rigid-attached and clothing-attached sensors. The future trajectory is forecasted from the probabilistic trajectory model formulated by left-to-right hidden Markov model (LR-HMM) and motion prediction accuracy is computed by the classification rule. Surprisingly, the results show that clothing-attached sensors can forecast the future trajectory and have better performance than body-attached sensors in terms of motion prediction accuracy. In some cases, the clothing-attached sensor can enhance accuracy by 45% compared to the body-attached sensor and requires approximately 80% less duration of the historical trajectory to achieve the same level of accuracy as the body-attached sensor.