Sensor optimization in smart insoles for post-stroke gait asymmetries using total variation and L1 distances

Abstract

By deploying pressure sensors on insoles, the forces exerted by the different parts of the foot when performing tasks standing up can be captured. The number and location of sensors to use are important factors in order to enhance the accuracy of parameters used in assessment while minimizing the cost of the device by reducing the number of deployed sensors. Selecting the best locations and the required number of sensors depends on the application and the features that we want to assess. In this paper, we present a computational process to select the optimal set of sensors to characterize gait asymmetries and plantar pressure patterns for stroke survivors based upon the total variation and L1 distances. The proposed mechanism is ecologically validated in a real environment with 14 stroke survivors and 14 control users. The number of sensors is reduced to 4, minimizing the cost of the device both for commercial users and companies and enhancing the cost to benefit ratio for its uptake from a national healthcare system. The results show that the sensors that better represent the gait asymmetries for healthy controls are the sensors under the big toe and midfoot and the sensors in the forefoot and midfoot for stroke survivors. The results also show that all four regions of the foot (toes, forefoot, midfoot, and heel) play an important role for plantar pressure pattern reconstruction for stroke survivors, while the heel and forefoot region are more prominent for healthy controls