Kinetic Gait Analysis Using a Low-Cost Insole.

Abnormal gait caused by stroke or other pathological reasons can greatly impact the life of an individual. Being able to measure and analyze that gait is often critical for rehabilitation. Motion analysis labs and many current methods of gait analysis are expensive and inaccessible to most individuals. The low-cost, wearable, and wireless insole-based gait analysis system in this study provides kinetic measurements of gait by using low-cost force sensitive resistors. This paper describes the design and fabrication of the insole and its evaluation in six control subjects and four hemiplegic stroke subjects. Subject-specific linear regression models were used to determine ground reaction force plus moments corresponding to ankle dorsiflexion/plantarflexion, knee flexion/extension, and knee abduction/adduction. Comparison with data simultaneously collected from a clinical motion analysis laboratory demonstrated that the insole results for ground reaction force and ankle moment were highly correlated (all >0.95) for all subjects, while the two knee moments were less strongly correlated (generally >0.80). This provides a means of cost-effective and efficient healthcare delivery of mobile gait analysis that can be used anywhere from large clinics to an individual's home.

A laboratory insole for analysis of sensor placement to determine ground reaction force and ankle moment in patients with stroke.

An insole system was constructed with 32 sensors inside a size 10 men's shoe. This system allows evaluation of the contributions of individual sensors spread throughout the surface area of the insole. The kinetic variables of interest in this initial study are ground reaction force and anterior-posterior ankle moment. Use of all 32 sensors are able to replicate the shape of the ground reaction force and ankle moment in a stroke patient who has regained a more normal gait, but less so in a stroke patient with impaired gait. Subsets of sensors can now be evaluated in order to ultimately identify an optimum set of sensors for determining kinetic variables necessary to classify presence or absence of a particular gait abnormality or other pathology.