Haichuan Ren’s research while affiliated with Zhengzhou University and other places

Intention tremor refers to the rhythmic and involuntary contraction and relaxation of muscles with movement toward a target, which is a common sequela of multiple sclerosis and usually occurs in the distal joints of the upper limb. Functional electrical stimulation (FES) is feasible for tremor suppression because of its fewer side effects, low cost, and portability. Most existing FES-based design methods assume that tremor is a single-frequency signal, though it is multifrequency in reality. The idealized simplification will limit the performance of tremor suppression. To address the problem, this article proposes an FES-based multiperiodic repetitive control (MP-RC) scheme to suppress multiple frequency wrist tremors. First, a nonlinear wrist musculoskeletal model with a Hammerstein structure is established. Then, a control strategy combining the model inverse linearization control and MP-RC is proposed for tremor suppression. A frequency-modified inverse RC algorithm and a gradient-based RC algorithm are developed to regulate the FES level. Finally, comparative experiments on four unimpaired participants and an intention tremor patient are conducted to validate the effectiveness of the proposed control schemes. Experimental results show that the MP-RC scheme can suppress tremors by up to 90.52%. Compared with the traditional filter-based feedback controller and the single-periodic repetitive controller, the proposed multiperiodic repetitive controller can achieve an average of 26% and 16% improvement, respectively, in tremor suppression, demonstrating the advantages of the proposed design.

The human balance ability is investigated using a multi-sensor human balance assessment system. Based on the pressure sensor, the gyroscope, the accelerometer and the magnetometer, the quantitative perception of human balance under different postures is realized. The characteristics of human balance ability are extracted through time domain methods and a new hybrid feature extraction. The results demonstrate that the hybrid feature extraction method with the support vector machine method can effectively classify and evaluate the human balance ability under different postures.

Based on human-computer interaction, a wrist motor function rehabilitation training and evaluation system is developed for the treatment or improvement of wrist motor dysfunction. Specifically, the joint angle sensor and the MYO wristband are used to realize the perception of the wrist motion on the ROS, the wrist motor function rehabilitation training game with information feedback is designed, and the quantitative evaluation on the wrist motor function is realized. The experimental results demonstrate that in the rehabilitation training session, the online accuracy of wrist motion recognition is 95.2%, and in the evaluation session, the root mean square error of the measured and actual values of the wrist joint angle is less than 5°. The paper works provide the basis for further clinical experiments of the wrist motor function rehabilitation training and evaluation.

Tremor is a rhythmic, alternating swing motion caused by involuntary repetition of muscle contraction and relaxation. Although it does not endanger life, it will make the work and daily life of patients difficult. Functional electrical stimulation (FES) has been shown as a promising technique for tremor suppression. Wrist motion is produced by a group of muscles in a collective and coordinate way. However, existing FES-based design methods mostly aim at one pair of muscles associated with the wrist motion, thus limiting the performance of tremor suppression. Furthermore, the possible high level of stimulation required for a single muscle pair can also accelerate muscle fatigue of the patients. To address these problems, this paper uses multiple muscles FES to suppress tremor by fully considering the properties of wrist motion. This paper develops a wrist musculoskeletal model with Hammerstein structure, identifies its parameters, and proposes repetitive controllers based on frequency modified inverse algorithm to suppress tremor. Experimental results are presented to demonstrate its advantages over single muscle stimulation based tremor suppression.