4. Smart Gait Healthcare Applications: Walking Status and Gait Biometrics

This chapter introduces the fundamental principles of gait analysis, covering normal and abnormal gait features and the significance of clinical gait analysis. Wearable sensor technology, particularly Inertial Measurement Units (IMUs), is highlighted for utilization in gait analysis, referencing studies that explore IMU data methods for gait recognition and prediction and using deep learning models for gait classification. Thoroughly explores gait biometrics, highlighting physical and behavioral traits crucial for gait recognition while underscoring the importance of sensor and posture datasets in training and assessing machine and deep learning models. Furthermore, diverse sensing modalities like video, pressure, and IMU are applied in gait recognition, detailing their respective features and techniques. Environmental-based gait biometrics systems’ potential applications and value showcase practical scenarios such as indoor positioning, activity monitoring, and personal identification in smart buildings and home environments. Intelligent gait health applications encompass clinical intelligent gait, remote gait monitoring, and smart home integration. An innovative method for gait assessment involving advanced wearable technology and AI-powered computational platforms, live monitoring via wearable IMU devices and machine learning, and a gait biometric recognition system hosted in the cloud for secure smart home entry. These innovations offer broader prospects for gait assessment and monitoring, along with associated challenges and requirements for clinical and daily life applications.