UC San Diego

Currently 1.6 million people have undergone limb amputation in the US alone and the dependency on lower limb prosthesis has been increasing for its many advantages. Gait analysis forms the backbone of post prosthesis attachment rehabilitation. In this study, a gait analysis module is developed using IMUs and FSRs, to collect and analyse gait parameters in real-world conditions. The sensor data was validated against a ground truth established by mounting the module on a 3D printer extruder head. Post processing of the data is done using a Madgwick Filter. We can accurately determine gait parameters like stride length, step length, time of stance and swing phases and cadence in a subject with normal gait. The cadence value obtained was 216 steps/min and the stride length was found to be 0.9m, which falls within the normal range for the subject's age.