SonoMyoNet: A Convolutional Neural Network for Predicting Isometric Force from Highly Sparse Ultrasound Images

Ultrasound imaging or sonomyography has been found to be a robust modality for measuring muscle activity due to its ability to image deep-seated muscles directly while providing superior spatiotemporal specificity compared to surface electromyography-based techniques. Quantifying the morphological changes during muscle activity involves computationally expensive approaches for tracking muscle anatomical structures or extracting features from brightness-mode (B-mode) images and amplitude-mode (A-mode) signals. This paper uses an offline regression convolutional neural network (CNN) called SonoMyoNet to estimate continuous isometric force from sparse ultrasound scanlines. SonoMyoNet learns features from a few equispaced scanlines selected from B-mode images and utilizes the learned features to estimate continuous isometric force accurately. The performance of SonoMyoNet was evaluated by varying the number of scanlines to simulate the placement of multiple single-element ultrasound transducers in a wearable system. Results showed that SonoMyoNet could accurately predict isometric force with just four scanlines and is immune to speckle noise and shifts in the scanline location. Thus, the proposed network reduces the computational load involved in feature tracking algorithms and estimates muscle force from the global features of sparse ultrasound images.

IMU-Based Real-Time Estimation of Gait Phase Using Multi-Resolution Neural Networks

This work presents a real-time gait phase estimator using thigh- and shank-mounted inertial measurement units (IMUs). A multi-rate convolutional neural network (CNN) was trained to estimate gait phase for a dataset of 16 participants walking on an instrumented treadmill with speeds varying between 0.1 to 1.9 m/s, and conditions such as asymmetric walking, stop-start, and sudden speed changes. One-subject-out cross-validation was used to assess the robustness of the estimator to the gait patterns of new individuals. The proposed model had a spatial root mean square error of 5.00±1.65%, and a temporal mean absolute error of 2.78±0.97% evaluated at the heel strike. A second cross-validation was performed to show that leaving out any of the walking conditions from the training dataset did not result in significant performance degradation. A 2-sample Kolmogorov-Smirnov test showed that there was no significant increase in spatial or temporal error when testing on the abnormal walking conditions left out of the training set. The results of the two cross-validations demonstrate that the proposed model generalizes well across new participants, various walking speeds, and gait patterns, showcasing its potential for use in investigating patient populations with pathological gaits and facilitating robot-assisted walking.

An Optimized Stimulation Control System for Upper Limb Exoskeleton Robot-Assisted Rehabilitation Using a Fuzzy Logic-Based Pain Detection Approach

The utilization of robotic systems in upper limb rehabilitation has shown promising results in aiding individuals with motor impairments. This research introduces an innovative approach to enhance the efficiency and adaptability of upper limb exoskeleton robot-assisted rehabilitation through the development of an optimized stimulation control system (OSCS). The proposed OSCS integrates a fuzzy logic-based pain detection approach designed to accurately assess and respond to the patient's pain threshold during rehabilitation sessions. By employing fuzzy logic algorithms, the system dynamically adjusts the stimulation levels and control parameters of the exoskeleton, ensuring personalized and optimized rehabilitation protocols. This research conducts comprehensive evaluations, including simulation studies and clinical trials, to validate the OSCS's efficacy in improving rehabilitation outcomes while prioritizing patient comfort and safety. The findings demonstrate the potential of the OSCS to revolutionize upper limb exoskeleton-assisted rehabilitation by offering a customizable and adaptive framework tailored to individual patient needs, thereby advancing the field of robotic-assisted rehabilitation.

Overground Robotic Exoskeleton Training for Patients With Stroke on Walking-Related Outcomes: A Systematic Review and Meta-analysis of Randomized Controlled Trials

OBJECTIVE

This review aims to evaluate the effectiveness of solely overground robotic exoskeleton (RE) training or overground RE training with conventional rehabilitation in improving walking ability, speed, and endurance among patients with stroke.

DATA SOURCES

Nine databases, 5 trial registries, gray literature, specified journals, and reference lists from inception until December 27, 2021.

STUDY SELECTION

Randomized controlled trials adopting overground robotic exoskeleton training for patients with any phases of stroke on walking-related outcomes were included.

DATA EXTRACTION

Two independent reviewers extracted items and performed risk of bias using the Cochrane Risk of Bias tool 1 and certainty of evidence using the Grades of Recommendation Assessment, Development, and Evaluation.

DATA SYNTHESIS

Twenty trials involving 758 participants across 11 countries were included in this review. The overall effect of overground robotic exoskeletons on walking ability at postintervention (d=0.21; 95% confidence interval [CI], 0.01, 0.42; Z=2.02; P=.04) and follow-up (d=0.37; 95% CI, 0.03, 0.71; Z=2.12; P=.03) and walking speed at postintervention (d=0.23; 95% CI, 0.01, 0.46; Z=2.01; P=.04) showed significant improvement compared with conventional rehabilitation. Subgroup analyses suggested that RE training should combine with conventional rehabilitation. A preferable gait training regime is <4 times per week over ≥6 weeks for ≤30 minutes per session among patients with chronic stroke and ambulatory status of independent walkers before training. Meta-regression did not identify any effect of the covariates on the treatment effect. The majority of randomized controlled trials had small sample sizes, and the certainty of the evidence was very low.

CONCLUSION

Overground RE training may have a beneficial effect on walking ability and walking speed to complement conventional rehabilitation. Further large-scale and long-term, high-quality trials are recommended to enhance the quality of overground RE training and confirm its sustainability.

Evaluation of a machine-learning-driven active-passive upper-limb exoskeleton robot: Experimental human-in-the-loop study

Evaluating exoskeleton actuation methods and designing an effective controller for these exoskeletons are both challenging and time-consuming tasks. This is largely due to the complicated human-robot interactions, the selection of sensors and actuators, electrical/command connection issues, and communication delays. In this research, a test framework for evaluating a new active-passive shoulder exoskeleton was developed, and a surface electromyography (sEMG)-based human-robot cooperative control method was created to execute the wearer's movement intentions. The hierarchical control used sEMG-based intention estimation, mid-level strength regulation, and low-level actuator control. It was then applied to shoulder joint elevation experiments to verify the exoskeleton controller's effectiveness. The active-passive assistance was compared with fully passive and fully active exoskeleton control using the following criteria: (1) post-test survey, (2) load tolerance duration, and (3) computed human torque, power, and metabolic energy expenditure using sEMG signals and inverse dynamic simulation. The experimental outcomes showed that active-passive exoskeletons required less muscular activation torque (50%) from the user and reduced fatigue duration indicators by a factor of 3, compared to fully passive ones.

A Unified Gait Phase Estimation and Control of Exoskeleton using Virtual Energy Regulator (VER)

Virtual Energy Regulator (VER) is a time independent controller that can generate stable limit cycles in lower-limb exoskeleton devices. In this work, we apply VER to control a lower-limb exoskeleton for assistive walking. We design two different limit cycles for hip and knee joints to assist the user during overground walking with the Indego explorer lower-limb exoskeleton. We tested the designed VER on a single participant for overground walking at a self-selected speed. Interestingly, due to VER time-independent nature, it can properly coordinate with the user's motions and produce mechanically stable overground walking in which the user can walk overground without a walker or crutches. The resultant gait is also more similar to a normal gait with improved range of motion compared to cases without controller; range of motion improved from $42.9 \pm 4.8{deg}$ and $44.9 \pm 4.9 {deg}$ to $46.6 \pm 1.3 {deg}$ and $63.0 \pm 6.8 {deg}$ at hip and knee joints, respectively. Especially, for the knee joint, the user is able to fully extend her knee during stance phase only when the VER is in the loop. In VER, the radius of each desired limit cycle is a function of phase. Accordingly, during walking, the internal phase of the VER is a monotonically increasing parameter that can be considered as a candidate for real-time gait phase estimation and heel-strike event detection. Hence, for gait phase estimation, VER relies only on a single joint position provided by the exoskeleton.

Model-Based Mid-Level Regulation for Assist-As-Needed Hierarchical Control of Wearable Robots: A Computational Study of Human-Robot Adaptation

The closed-loop human–robot system requires developing an effective robotic controller that considers models of both the human and the robot, as well as human adaptation to the robot. This paper develops a mid-level controller providing assist-as-needed (AAN) policies in a hierarchical control setting using two novel methods: model-based and fuzzy logic rule. The goal of AAN is to provide the required extra torque because of the robot’s dynamics and external load compared to the human limb free movement. The human–robot adaptation is simulated using a nonlinear model predictive controller (NMPC) as the human central nervous system (CNS) for three conditions of initial (the initial session of wearing the robot, without any previous experience), short-term (the entire first session, e.g., 45 min), and long-term experiences. The results showed that the two methods (model-based and fuzzy logic) outperform the traditional proportional method in providing AAN by considering distinctive human and robot models. Additionally, the CNS actuator model has difficulty in the initial experience and activates both antagonist and agonist muscles to reduce movement oscillations. In the long-term experience, the simulation shows no oscillation when the CNS NMPC learns the robot model and modifies its weights to simulate realistic human behavior. We found that the desired strength of the robot should be increased gradually to ignore unexpected human–robot interactions (e.g., robot vibration, human spasticity). The proposed mid-level controllers can be used for wearable assistive devices, exoskeletons, and rehabilitation robots.

Design and Analysis of a Lower Limb Rehabilitation Training Component for Bedridden Stroke Patients

Carrying out the immediate rehabilitation interventional therapy will better improve the curative effect of rehabilitation therapy, after the condition of bedridden stroke patients becomes stable. A new lower limb rehabilitation training module, as a component of a synchronous rehabilitation robot for bedridden stroke patients’ upper and lower limbs, is proposed. It can electrically adjust the body shape of patients with a different weight and height. Firstly, the innovative mechanism design of the lower limb rehabilitation training module is studied. Then, the mechanism of the lower limb rehabilitation module is simplified and the geometric relationship of the human–machine linkage mechanism is deduced. Next, the trajectory planning and dynamic modeling of the human–machine linkage mechanism are carried out. Based on the analysis of the static moment safety protection of the human–machine linkage model, the motor driving force required in the rehabilitation process is calculated to achieve the purpose of rationalizing the rehabilitation movement of the patient’s lower limb. To reconstruct the patient’s motor functions, an active training control strategy based on the sandy soil model is proposed. Finally, the experimental platform of the proposed robot is constructed, and the preliminary physical experiment proves the feasibility of the lower limb rehabilitation component.