Personalized Detection of Functional-State Changes Through Continuous Gait Monitoring: A Methodology for Assistive-Device Users

Lower limb mobility impairments resulting from neurological diseases, trauma injuries or aging significantly impact the quality of life of individuals by limiting their autonomy. Rehabilitation plays an important role in addressing the challenges of these impairments, with early detection of changes in the functional state of individuals being essential. This enables therapies to be adjusted based on the current condition of the patient, thereby enhancing their effectiveness. Such early detection, however, requires continuous assessment by specialists, which is unfeasible given the existing limited resources. Since gait is a reflection of the physical and mental states of each individual, its continuous monitoring and subsequent data analysis can serve as a valuable tool for the aforementioned objective. This study proposes a methodology that, based on continuous gait monitoring data, detects significant changes in the functional state of patients who require an assistive device for walking. Given the variability that may exist among different individuals, this methodology tackles the issue from an individualized approach generating personalized models for each individual using the OC-SVM technique. The proposed methodology was validated in nine healthy people who had different simulated functional states, obtaining an accuracy in the range of 70–97%. In addition, a one-year longitudinal study was also carried out with three post-stroke individuals to validate the methodology in real cases, obtaining an average accuracy of 78%.