Reliability Enhancement Method of Attitude Estimation for Wearable Motion-Capture Systems in Human Lower Limb Rehabilitation

Wearable motion-capture systems offer promising avenues for human lower limb rehabilitation. However, unstable data transmission and attitude estimation challenge their practical application. Aiming at this problem, a reliable method utilizing wearable inertial sensors for rehabilitation applications is innovatively proposed and implemented within our designed wearable motion-capture systems tailored to patients with impaired lower limbs. A stable data transmission process based on star-type Bluetooth body sensor networks is designed by establishing a connection parameter setting method to guarantee reliable attitude estimation. Then, a robust attitude estimating method based on an improved gradient descent method is proposed to promote the anti-interference capability of the algorithm by introducing trust coefficients. Lower limb motion-capture experiments are conducted, and results show that the proposed method enables the system to maintain a package loss rate of no more than 0.24% and has a maximum coefficient of variation (CV) of 5.9% during the data transmission process. Attitude estimation reliability experiments reveal that the proposed algorithm substantially enhances anti-interference capabilities while preserving estimation accuracy. Compared to the state-of-the-art method, under acceleration shock, estimation errors decrease by up to 39.1% (roll), 42.9% (pitch), and 20.2% (yaw). When exposed to external magnetic field interference, conventional estimation algorithms falter, whereas the proposed method maintains an average error within 2 degrees. Significance analysis underscores the method's distinctiveness at the 0.05% significance level (p < 0.05). This study effectively bridges the gap between wearable inertial motion-capture systems and their application in clinical lower limb rehabilitation.