Recognition of gait cycle phases using wearable sensors

• This paper deals with the detection of the main characteristics of the gait phases.
• A segmentation of the human body COF is done using the in-shoe pressure mapping system.
• Identification of the gait phases is done using a regression model.
• The proposed method has the advantage of detecting abrupt and smooth transitions.
• The proposed method is implemented and verified by experiment tests.

The analysis and monitoring of the human daily living activities plays an important role for rehabilitation goals, fall prevention rehabilitation and general health-care treatments. Among these activities, walking is the most important daily motion. Studying the evolution of the gait cycle through the analysis of the human center of force is beneficial to predict any abnormal walking pattern. The analysis is based on the use of pressure-based mapping system that collects pressure and force measurement during the gait cycle. This paper deals mainly with the detection of the main characteristics of the gait phases. To this end, a segmentation of the center of force of the human body measure through the in-shoe pressure mapping system is performed to identify the gait phases. The proposed segmentation approach consists in modeling each segment by a regression model and using logistic functions to model the transitions between segments. This flexible modeling through the logistic functions has the advantage of detecting abrupt and smooth transitions between segments.