Gait Recognition Method Based on Wearable Sensor Information Fusion

Abstract:
Gait recognition offers non-contact, non-intrusive, easily sensed, and hard-to-hide features, demonstrating significant research value and application potential. Although prevailing gait recognition techniques have produced certain results, the formulation and actualization of a functional gait recognition system continue to encounter numerous obstacles, such as the resources of the wearable devices, user experience and so on. This paper proposes a gait recognition method based on the fusion of information from multiple sensors, aiming to overcome the limitations of traditional identification technologies and enhance the accuracy and user experience in practical applications. A novel lightweight network model was designed in this study, integrating an attention mechanism to reduce the number of parameters and improve recognition accuracy. The most individual-differentiating sensor locations were identified by comparing the gait recognition performance of sensors in different body parts. Furthermore, decision-level and data-level fusion methods are studied and found that the data-level fusion strategy outperforms the decision-level one. Combinations of sensors in various body parts are investigated and the optimal combination is proposed. Finally, the impact of the scale of identified subjects on model performance was analyzed, demonstrating efficient data processing capabilities and robustness.