B-Spline Modeling of Inertial Measurements for Evaluating Stroke Rehabilitation Effectiveness

• Main Authors: Yi-Ting Hwang (Author), Yu-Qian Tung (Author), Chun-Shu Chen (Author), Bor-Shing Lin (Author)
• Format: Book
• Published: IEEE, 2023-01-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.
• Notes: 1558-0210; 10.1109/TNSRE.2023.3323375
• Keywords: B-spline; linear mixed model; inertial measurement unit (IMU); rehabilitation; virtual reality (VR); Medical technology; R855-855.5; Therapeutics. Pharmacology; RM1-950
• Source: IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol 31, Pp 4008-4016 (2023)

Patients who experience upper-limb paralysis after stroke require continual rehabilitation. Rehabilitation must be evaluated for appropriate treatment adjustment; such evaluation can be performed using inertial measurement units (IMUs) instead of standard scales or subjective evaluations. However, IMUs produce large quantities of discretized data, and using these data directly is challenging. In this study, B-splines were used to estimate IMU trajectory data for objective evaluations of hand function and stability by using machine learning classifiers and mathematical indices. IMU trajectory data from a 2018 study on upper-limb rehabilitation were used to validate the proposed method. Features extracted from <inline-formula> <tex-math notation="LaTeX">${B}$ </tex-math></inline-formula>-spline trajectories could be used to classify individuals in the 2018 study with high accuracy, and the proposed indices revealed differences between these groups. Compared with conventional rehabilitation evaluation methods, the proposed method is more objective and effective.