A method for precision extraction of gait features based on a double model was proposed to obtain gait parameters, and an access monitoring platform was presented for identity authentication. Automatic recognition by gait has the unique capability to recognize people at a distance when other biometrics are obscured. Its recognition capability has been supported by studies in other domains such as medicine (biomechanics), mathematics, and psychology, which continue to suggest that gait is unique. The presently developed biosignature has constructed an integrated system with edge computing, low-latency wireless body area networks, and heterogeneous sensors. A neuro-ergonomic approach will connect three aspects of cognition, performance, and environment to decipher intentions of our heading direction. This gait recognition system aims to implement from smart home to smart factory by confirming the identification concerning practicality, privacy, and security.

The first system uses heterogeneous sensors to detect gait and build an integrated system for real-time computing and transmission functions for identity recognition. The prototype of this system is gait acquisition, caption, extraction, and comparison. Taking a 30 people database as an example, the accuracy rate of successfully identifying personnel in the database is 100%, and the accuracy rate for identifying foreign visitors is 92.3%.

The system can dispel surveillance suspicion and improve the harmony of gait patterns detection in personal medical care, industrial safety monitoring, and environmental flow resources. The reliable and deployable system has direct industrial applications from smart homes, buildings/factories, exhibition halls/airports to smart cities, and optimize the layout of artificial intelligence industries.