Applications of EMG in Clinical and Sports Medicine

This second of two volumes on EMG (Electromyography) covers a wide range of clinical applications, as a complement to the methods discussed in volume 1. Topics range from gait and vibration analysis, through posture and falls prevention, to biofeedback in the treatment of neurologic swallowing impairment. The volume includes sections on back care, sports and performance medicine, gynecology/urology and orofacial function. Authors describe the procedures for their experimental studies with detailed and clear illustrations and references to the literature. The limitations of SEMG measures and methods for careful analysis are discussed. This broad compilation of articles discussing the use of EMG in both clinical and research applications demonstrates the utility of the method as a tool in a wide variety of disciplines and clinical fields

Trajectory Planning and Subject-Specific Control of a Stroke Rehabilitation Robot using Deep Reinforcement Learning

There are approximately 13 million annual new stroke cases worldwide. Research has shown that robotics can provide practical and efficient solutions for expediting post-stroke patient recovery. Assistive robots provide automatic limb training, which saves a great deal of time and energy. In addition, they facilitate the use of data acquisition devices. The data is beneficial in terms of quantitative evaluation of the patient progress. This research focused on the trajectory planning and subject-specific control of an upper-extremity post-stroke rehabilitation robot. To find the optimal rehabilitation practice, the manipulation trajectory was designed by an optimization-based planner. A linear quadratic regulator (LQR) controller was then applied to stabilize the trajectory. The integrated planner-controller framework was tested in simulation. To validate the simulation results, hardware implementation was conducted, which provided good agreement with simulation. One of the challenges of rehabilitation robotics is the choice of the low-level controller. To find the best candidate for our specific setup, five controllers were evaluated in simulation for circular trajectory tracking. In particular, we compared the performance of LQR, sliding mode control (SMC), and nonlinear model predictive control (NMPC) to conventional proportional integral derivative (PID) and computed-torque PID controllers. The real-time assessment of the mentioned controllers was done by implementing them on the physical hardware for point stabilization and circular trajectory tracking scenarios. Our comparative study confirmed the need for advanced low-level controllers for better performance. Due to complex online optimization of the NMPC and the incorporated delay in the method of implementation, performance degradation was observed with NMPC compared to other advanced controllers. The evaluation showed that SMC and LQR were the two best candidates for the robot. To remove the need for extensive manual controller tuning, a deep reinforcement learning (DRL) tuner framework was designed in MATLAB to provide the optimal weights for the controllers; it permitted the online tuning of the weights, which enabled the subject-specific controller weight adjustment. This tuner was tested in simulation by adding a random noise to the input at each iteration, to simulate the subject. Compared to fixed manually tuned weights, the DRL-tuned controller presented lower position-error. In addition, an easy to implement high-level force controller algorithm was designed by incorporating the subject force data. The resulting hybrid position/force controller was tested with a healthy subject in the loop. The controller was able to provide assist as needed when the subject increased the position-error. Future research might consider model reduction methods for expediting the NMPC optimization, application of the DRL on other controllers and for optimization parameter adjustment, testing other high-level controllers like admittance control, and testing the final controllers with post-stroke patients

The biomechanics of human locomotion

Includes bibliographical references. The thesis on CD-ROM includes Animate, GaitBib, GaitBook and GaitLab, four quick time movies which focus on the functional understanding of human gait. The CD-ROM is available at the Health Sciences Library

Dynamic modeling and CPG-based trajectory generation for a masticatory rehab robot

International audienceHuman mastication is a complex and rhythmic biomechanical process which is regulated by a brain stem central pattern generator (CPG). Masticatory patterns, frequency and amplitude of mastication are different from person to person and significantly depend on food properties. The central nervous system controls the activity of muscles to produce smooth transitions between different movements. Therefore, to rehab human mandibular system, there is a real need to use the concept of CPG for development of a new methodology in jaw exercises and to help jaw movements recovery. This paper proposes a novel method for real-time trajectory generation of a mastication rehab robot. The proposed method combines several methods and concepts including kinematics, dynamics, trajectory generation and CPG. The purpose of this article is to provide a methodology to enable physiotherapists to perform the human jaw rehabilitation. In this paper, the robotic setup includes two Gough-Stewart platforms. The first platform is used as the rehab robot, while the second one is used to model the human jaw system. Once the modeling is completed, the second robot will be replaced by an actual patient for the selected physiotherapy. Gibbs-Appell's formulation is used to obtain the dynamics equations of the rehab robot. Then, a method based on the Fourier series is employed to tune parameters of the CPG. It is shown that changes in leg lengths, due to the online changes of the mastication parameters, occur in a smooth and continuous manner. The key feature of the proposed method, when applied to human mastication, is its ability to adapt to the environment and change the chewing pattern in real-time parameters, such as amplitudes as well as jaw movements velocity during mastication