Feet Pressure Prediction from Lower Limbs IMU Sensors for Wearable Systems in Remote Monitoring Architectures

The eHealth systems are in great demand, particularly during times of outbreak like COVID-19, when there is a shortage of caregivers. The technological advancements, such as wearable wireless devices, the Internet of Things, and improved machine learning methods have made these systems more reliable. In modern times, these systems can play a vital role in post-rehabilitation journeys, which have significant social impact and high costs in traditional settings. Cost efficiency, portability, and generalization are key factors in adopting new technology.In this study, we investigate the potential for optimizing and simplifying hardware in order to increase the cost-effectiveness and versatility of post-stroke eHealth rehabilitation systems. It leverages the rich information available from Inertial Measurement Unit (IMU) sensors to compensate the need for foot pressure sensing. We present the first attempt to demonstrate the potential of machine learning, aided by affordable off the shelf motion sensing devices, for foot pressure analysis. Our proposed foot pressure decoding model is trained in an exercise-agnostic, self-supervised manner that eliminates the need for human annotation. The algorithm is evaluated using appropriate performance metrics, and our experimental results show very promising performance.