Wearable IMU-Based System for Real-Time Monitoring of Lower-Limb Joints

Recent advances in micro-electromechanical systems technology have enabled the evolution of miniature, low-power, and high-performance inertial motion sensors that are commonly found in most present-day smart gadgets. Furthermore, high-speed and power-efficient communication and computing technologies may enable these sensors to potentially pave the way for home-based remote monitoring and assessment of human health in the imminent age of new technologies such as Smart Home, internet-of-things, and internet-of-everything. Continuous monitoring of lower-limb joints in a wearable platform is such an application that may potentially enable the tele-rehabilitation of patients with motor impairment, gait abnormalities, and joint injuries through quantitative rather than observational analysis of gait health. In this work, we designed, implemented, and validated a two-stage sensor fusion algorithm to estimate lower-limb joint angles in real-time. The drift in the cumulatively integrated gyroscope data was estimated in real-time using a gradient descent approach that was subsequently used to correct the inclination of the sensors. The roll and pitch angles thus obtained for each sensor mounted above and below the joint were then fused in the second stage to obtain a real-time estimate of joint angle by exploiting a gradient descent method. Since the joint angles were estimated primarily from the gyroscope data and without incorporating any magnetic field measurement, the joint angles thus obtained were least affected by the external acceleration and are insensitive to magnetic disturbances. The performance of the proposed algorithm was validated with a publicly available dataset and in the presence of simulated external acceleration.