User Walking Intention Recognition and Position Estimation Using a ToF Depth Camera for Frontal Following

Smart walkers that provide walking assistance for the elderly or disabled increasingly require the ability to recognize the condition of the user and realize real-time interactions. Among the core components of such systems, recognizing the walking intention and estimating the position of the user are essential. This study proposes a technique that simultaneously performs walking-intention recognition and position estimation using depth image sequences acquired from a single time-of-flight depth camera. For user-walking- intention recognition, the depth image sequence is used as the input to a transformer model. This enables the network to learn spatial depth features and temporal walking patterns in an end-to-end manner, allowing it to predict walking intention. For user-position estimation, pixel-level depth variation analysis based on depth differences is applied to quantitatively classify user movements. A Kalman filter is then employed to enhance the stability of distance estimation between humans and walking devices. The experimental results achieved an F1-score of approximately 74% for walking-intention prediction and an average position error of 0.723 ± 0.027 m for distance estimation. The proposed method can serve as a core perception and intention recognition technology for smart assistive- walker control. Future research will aim to further enhance the prediction performance of the developed system by integrating motion trajectory data from smart walkers.