A portable assist-as-need upper-extremity hybrid exoskeleton for FES-induced muscle fatigue reduction in stroke rehabilitation

Function Electrical Stimulation Effect on Muscle Fatigue Based on Fatigue Characteristic Curves of Dumbbell Weightlifting Training

The parameter setting of functional electrical stimulation (FES) is important for active recovery training since it affects muscle health. Among the FES parameters, current amplitude is the most influential factor. To explore the FES effect on the maximum stimulation time, this study establishes a curve between FES current amplitude and the maximum stimulation time based on muscle fatigue. We collect 10 subjects' surface electromyography under dumbbell weightlifting training and analyze the muscle fatigue state by calculating the root mean square (RMS) of power. By analyzing signal RMS, the fatigue characteristic curves under different fatigue levels are obtained. According to the muscle response under FES, the relationship curve between the current amplitude and the maximum stimulation time is established and FES parameters' effect on the maximum stimulation time is obtained. The linear curve provides a reference for FES parameter setting, which can help to set stimulation time safely, thus preventing the muscles from entering an excessive fatigue state and becoming more active to muscle recovery training.

The efficacy of hybrid neuroprostheses in the rehabilitation of upper limb impairment after stroke, a narrative and systematic review with a meta-analysis

BACKGROUND

Paresis of the upper limb (UL) is the most frequent impairment after a stroke. Hybrid neuroprostheses, i.e., the combination of robots and electrical stimulation, have emerged as an option to treat these impairments.

METHODS

To give an overview of existing devices, their features, and how they are linked to clinical metrics, four different databases were systematically searched for studies on hybrid neuroprostheses for UL rehabilitation after stroke. The evidence on the efficacy of hybrid therapies was synthesized.

RESULTS

Seventy-three studies were identified, introducing 32 hybrid systems. Among the most recent devices (n = 20), most actively reinforce movement (3 passively) and are typical exoskeletons (3 end-effectors). If classified according to the International Classification of Functioning, Disability and Health, systems for proximal support are expected to affect body structures and functions, while the activity and participation level are targeted when applying Functional Electrical Stimulation distally plus the robotic component proximally. The meta-analysis reveals a significant positive effect on UL functions (p < 0.001), evident in a 7.8-point Mdiff between groups in the Fugl-Meyer assessment. This positive effect remains at the 3-month follow-up (Mdiff  = 8.4, p < 0.001).

CONCLUSIONS

Hybrid neuroprostheses have a positive effect on UL recovery after stroke, with effects persisting at least three months after the intervention. Non-significant studies were those with the shortest intervention periods and the oldest patients. Improvements in UL functions are not only present in the subacute phase after stroke but also in long-term chronic stages. In addition to further technical development, more RCTs are needed to make assumptions about the determinants of successful therapy.

Towards AI-Controlled Movement Restoration: Learning FES-Cycling Stimulation with Reinforcement Learning

Functional electrical stimulation (FES) has been increasingly integrated with other rehabilitation devices, including rehabilitation robots. FES cycling is one of the common FES applications in rehabilitation, which is performed by stimulating leg muscles in a certain pattern. The appropriate pattern varies across individuals and requires manual tuning which can be time-consuming and challenging for the individual user. Here, we present an AI-based method for finding the patterns, which requires no extra hardware or sensors. Our method starts with finding model-based patterns using reinforcement learning (RL) and customised cycling models. Next, our method fine-tunes the pattern using real cycling data and offline RL. We test our method both in simulation and experimentally on a stationary tricycle. Our method can robustly deliver model-based patterns for different cycling configurations. In the experimental evaluation, the model-based pattern can induce higher cycling speed than an EMG-based pattern. And by using just 100 seconds of cycling data, our method can deliver a fine-tuned pattern with better cycling performance. Beyond FES cycling, this work is a case study, displaying the feasibility and potential of human-in-the-loop AI in real-world rehabilitation.

Development of portable robotic orthosis and biomechanical validation in people with limited upper limb function after stroke

Stroke has a considerable incidence in the world population and would cause sequelae in the upper limbs. One way to increase the efficiency in the rehabilitation process of patients with these sequelae is through robot-assisted therapy. The present study developed a portable robotic orthosis called Pinotti Portable Robotic Exoskeleton (PPRE) and validated its functioning in clinical tests. The static and dynamic parts of the device modules are described. Design issues, such as heavyweight and engine positioning, have been optimized. The implementation of control was through a smartphone application that communicates with a microcontroller to perform desired movements. Four individuals with motor impairment of the upper limbs due to stroke performed clinical tests to validate the device. Participants did not mention pain, discomfort, tingling, and paresthesia. The robotic device showed the ability to perform the flexion and extension movements of the fingers and elbow. The PPRE was confirmed to be adequate and functional at different levels of motor impairment assessed. The orthosis presented advantages over the currently existing devices, concerning its biomechanical functioning, portability, comfort, and versatility. Thus, the apparatus has the great innovative potential to become a device for home use, serving as an aid to the therapist and facilitating the rehabilitation of patients after an injury. In a larger sample, future studies are needed to assess the effect of a robotic orthosis on the level of rehabilitation in individuals with upper limb impairment.