Self-Powered Wearable Pressure Sensors for Detection and Separation of Signals for Various Human Movements

Abstract

A detailed study on the dynamic response of mountable pressure sensors is presented, with a focus on foot pressure sensors integrated with carbon nanotube (CNT)-coated cotton fibers. The research explores the sensor‘s sensitivity to pressure changes, repeatability, hysteresis, and durability through rigorous modeling and experimental validation. Computational simulations using Python (NumPy library) and experimental data demonstrate the sensor‘s nonlinear conductance response to applied force, attributed to the varying contact area and number of contact points among the fibers. Long-term outdoor exposure tests confirm the material‘s resilience to environmental stressors, maintaining its electrical conductivity and structural integrity. The study also investigates the sensor‘s capability to monitor human activities, such as walking, running, stair climbing, and jumping, by analyzing force profiles and step rates. Additionally, the sensors effectively detect muscle movements during swallowing, coughing, and speech, with potential applications in health monitoring and artificial voice synthesis. The Minimum Redundancy Maximum Relevance (MRMR) algorithm is utilized to implement feature selection methods aimed at distinguishing between various activities, thereby demonstrating the sensor‘s potential for activity recognition. An estimation of harvested electric power using a piezoelectric sensor on the pressure sensors has been done, which can provide power to the different wearable devices attached to our body. This work contributes to the advancement of self-powered wearable pressure sensors to monitor real-time human activity, with implications for healthcare, sports performance, and assistive technologies.