Assessment of Gait Pattern Changes in Lower Limb Amputees Using Inertial Sensor Signals: An Alternative to Gait Parameter Measurement

Effective gait monitoring and rehabilitation are essential for improving the quality of life in individuals with disabilities. Inertial sensors have the potential to enable long-term gait monitoring and assessment beyond the clinical setting. However, developing minimally intrusive systems that accommodate a wide range of gait deviations remains challenging. This study investigated an alternative to traditional approaches of using gait parameters for gait assessment, to evaluate whether changes in the overall gait patterns of lower-limb prosthetic users could be assessed by directly analyzing gyroscope and accelerometer data from inertial sensors. Eleven lower-limb prosthetic users completed walk trials with a biofeedback system designed to perturb gait patterns, while an additional twelve completed a gait training session with a physiotherapist. Inertial sensors were affixed at various locations along the lower body to collect gyroscope and accelerometer data. Three algorithms were evaluated: a hidden Markov model-based similarity measure (HMM-SM), self-organizing maps, and dynamic time warping. Statistical analyses demonstrated that self-organizing maps and dynamic time warping effectively assessed changes in gait patterns under a variety of gait perturbation strategies, with sensors located on the upper legs and lower legs significantly outperforming the pelvis location overall. The findings suggest the potential for wearable and adaptable gait monitoring systems capable of assessing changes in gait patterns. These systems could enable precise gait monitoring and real-time therapeutic intervention in real-world settings, offering a promising tool for long-term rehabilitation.