Time-scaling based sliding mode control for Neuromuscular Electrical Stimulation under uncertain relative degrees

Design and validation of a pediatric gait assistance exoskeleton system with fast non-singular terminal sliding mode controller

Existing exoskeletons for pediatric gait assistance have limitations in anthropometric design, structure weight, cost, user safety features, and adaptability to diverse users. Additionally, creating precise models for pediatric rehabilitation is difficult because the rapid anthropometric changes in children result in unknown model parameters. Furthermore, external disruptions, like unpredictable movements and involuntary muscle contractions, add complexity to the control schemes that need to be managed. To overcome these limitations, this study aims to develop an affordable stand-aided lower-limb exoskeleton specifically for pediatric subjects (8-12 years, 25-40 kg, 128-132 cm) in passive-assist mode. The authors modified a previously developed model (LLESv1) for improved rigidity, reduced mass, simplified motor arrangement, variable waist size, and enhanced mobility. A computer-aided design of the new exoskeleton system (LLESv2) is presented. The developed prototype of the exoskeleton appended with a pediatric subject (age: 12 years old, body mass: 40 kg, body height: 132 cm) is presented with real-time hardware architecture. Thereafter, an improved fast non-singular terminal sliding mode (IFNSTSM) control scheme is proposed, incorporating a double exponential reaching law for expedited error convergence and enhanced stability. The Lyapunov stability warrants the control system's performance despite uncertainties and disturbances. In contrast to fast non-singular terminal sliding mode (FNSTSM) control and time-scaling sliding mode (TSSM) control, experimental validation demonstrates the effectiveness of IFNSTSM control by a respective average of 5.39% and 42.1% in tracking desired joint trajectories with minimal and rapid finite time converging errors. Moreover, the exoskeleton with the proposed IFNSTSM control requires significantly lesser control efforts than the exoskeleton using contrast FNSTSM control. The Bland-Altman analysis indicates that although there is a minimal mean difference in variables when employing FNSTSM and IFNSTSM controllers, the latter exhibits significant performance variations as the mean of variables changes. This research contributes to affordable and effective pediatric gait assistance, improving rehabilitation outcomes and enhancing mobility support.

Adaptive Filter Model of Cerebellum for Biological Muscle Control With Spike Train Inputs

Prior applications of the cerebellar adaptive filter model have included a range of tasks within simulated and robotic systems. However, this has been limited to systems driven by continuous signals. Here, the adaptive filter model of the cerebellum is applied to the control of a system driven by spiking inputs by considering the problem of controlling muscle force. The performance of the standard adaptive filter algorithm is compared with the algorithm with a modified learning rule that minimizes inputs and a simple proportional-integral-derivative (PID) controller. Control performance is evaluated in terms of the number of spikes, the accuracy of spike input locations, and the accuracy of muscle force output. Results show that the cerebellar adaptive filter model can be applied without change to the control of systems driven by spiking inputs. The cerebellar algorithm results in good agreement between input spikes and force outputs and significantly improves on a PID controller. Input minimization can be used to reduce the number of spike inputs, but at the expense of a decrease in accuracy of spike input location and force output. This work extends the applications of the cerebellar algorithm and demonstrates the potential of the adaptive filter model to be used to improve functional electrical stimulation muscle control.

Effects of neuromuscular electrical stimulation on glycemic control: a systematic review and meta-analysis

Background

Physical inactivity increases the risk for metabolic diseases such as obesity and type 2 diabetes. Neuromuscular electrical stimulation (NMES) is an effective method to induce muscle contraction, particularly for populations with physical impairments and/or metabolic diseases. However, its effectiveness to improve glycemic control is unclear. This review aimed to determine the effectiveness of NMES on glycemic control.

Methods

Electronic search consisted of MEDLINE (PubMed), EMBASE, Cochrane Library, Google Scholar, and Web of Science to identify studies that investigated the effects of NMES on glycemic control for this systematic review. The meta-analysis consists of the studies designed as randomized controlled trials. Effect sizes were calculated as the standardized mean difference (SMD) and meta-analysis was conducted using a random-effects model.

Results

Thirty-five studies met the inclusion criteria for systematic review and of those, nine qualified for the meta-analysis. Existing evidence suggested that NMES effectively improves glycemic control predominantly in middle-aged and elderly population with type 2 diabetes, obesity, and spinal cord injury. The meta-analysis is comprised of 180 participants and reported that NMES intervention lowered fasting blood glucose (SMD: 0.48; 95% CI: 0.17 to 0.78; p=0.002; I²=0%). Additional analysis using the primary measures reported by each study to indicate glycemic control (i.e., OGTT, HOMA-IR, and fasting glucose) also confirmed a significant effect of NMES on improving glycemic control (SMD: 0.41; 95% CI, 0.09 to 0.72; p=0.01; I²=11%). NMES protocol varied across studies and requires standardization.

Conclusion

NMES could be considered as a therapeutic strategy to improve glycemic control in populations with physical impairments and/or metabolic disorders.

Systematic review registration

https://www.crd.york.ac.uk/prospero/, identifier CRD42020192491.

RBF neural network-based admittance PD control for knee rehabilitation robot

Early-stage rehabilitation therapy for post-stroke patients consists of intensive and accurate training sessions. During these sessions, the therapist moves the patient’s joint within its range of motion repetitively. Patients, at this stage, often cannot control their muscles, and neurological disorders may occur and lead to undesirable movements. Thus, the therapist should train the joint gently to handle any sudden involuntary movements. Otherwise, the joint may undergo excessive torques, which may injure it. In this paper, we address this case and develop a clinical rehabilitation robotic system for training the knee joint taking into account the occurrence of these undesirable movements. The developed system has an innovative mechanism to measure interaction torques exerted by involuntary movements. Then, we introduce a new control approach consisting of an admittance controller and a proportional-derivative controller augmented by a radial basis function (PD-RBF) neural network. The PD-RBF guides the robot joint along a predefined trajectory, while the admittance part tracks any sudden interaction torques and updates the predefined trajectory accordingly. Thus, the robot trains the knee joint and once an undesirable movement occurs the robot gets along with this movement smoothly, then it gets back to the predefined trajectory. To validate the performance of the proposed admittance PD-RBF controller, we consider two controllers, an admittance adaptive sliding mode control and an admittance conventional PD one. Then, a compatarive study is conducted on these controllers via real-world experiments. The obtained results verify the efficiency of the admittance PD-RBF and prove its superiority over the other aforementioned controllers.