Spectral and time-frequency domains features for quantitative lower-limb rehabilitation monitoring via wearable inertial sensors

Abstract

Inertial data represent a rich source of clinically relevant information which can provide details on motor assessment in subjects involved in a rehabilitation process. Thus, a number of metrics in the spectral and time-frequency domain has been considered to be reliable for measuring and quantifying patient progress and has been applied on the 3D accelerometer and angular rate signals collected on one impaired subject with knee injury through a wearable wireless inertial sensing system developed at the Tyndall National Institute. The subject has performed different activities evaluated across several sessions over time. Data show that most of the studied features can provide a quantitative analysis of the improvement of the subject along rehabilitation, and differentiate between impaired and unimpaired limb motor performance. The work proves that the studied features can be taken into account by clinicians and sport scientists to study the overall patients' condition and provide accurate clinical feedback as to their rehabilitative progress. The work is ongoing and additional clinical trials are currently being planned with an enhanced number of injured subjects to provide a more robust statistical analysis of the data in the study.