An Intelligent Rehabilitation Assessment Method for Stroke Patients Based on Lower Limb Exoskeleton Robot

• Main Authors: Shisheng Zhang (Author), Liting Fan (Author), Jing Ye (Author), Gong Chen (Author), Chenglong Fu (Author), Yuquan Leng (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.2023.3298670
• Keywords: Intelligent rehabilitation assessment; 6MWD; FMA-LE; lower limb exoskeleton robot; machine learning; Medical technology; R855-855.5; Therapeutics. Pharmacology; RM1-950
• Source: IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol 31, Pp 3106-3117 (2023)

The 6-min walk distance (6MWD) and the Fugl-Meyer assessment lower-limb subscale (FMA-LE) of the stroke patients provide the critical evaluation standards for the effect of training and guidance of the training programs. However, gait assessment for stroke patients typically relies on manual observation and table scoring, which raises concerns about wasted manpower and subjective observation results. To address this issue, this paper proposes an intelligent rehabilitation assessment method (IRAM) for rehabilitation assessment of the stroke patients based on sensor data of the lower limb exoskeleton robot. Firstly, the feature parameters of the patient were collected, including age, height, and duration, etc. The sensor data of the exoskeleton robot were also collected, including joint angle, joint velocity, and joint torque, etc. Secondly, a gait feature model was constructed to deduce the walking gait parameters of the patient according to the sensor data of the exoskeleton, including the support phase to swing phase ratio, step length and leg lift height of the patient, etc. Then, the 6MWD and FMA-LE values were collected by traditional methods, feature parameters, gait parameters and human-machine interaction parameters (joint torque) of the patient were adopted to train the rehabilitation assessment model. Finally, the assessment model was trained by a machine-learning based algorithm. The new stroke patients’ the 6MWD and FMA-LE values can be predicted by the trained model. The experimental results present that the prediction accuracy for the 6MWD and FMA-LE values reach to 85.19% and 92.66%, respectively.