AI and Robotics to Rehabilitate Lower Body Paralysis

Spinal cord injuries have unpredictable outcomes. While some recover full movement, most of them suffer permanent paralysis, ranging from partial to full-body. Alongside paralysis often comes the loss of bladder control, sexual dysfunction, bedsores, and psychological state issues, among other challenges. The solution for this is the Exoskeleton and the AI technology that offers opportunities to expand virtual and physical access for individuals with disabilities. We will disclose some machines that have an excellent impact on the lives of the elderly and other people with disabilities. However, a very important part of bringing these opportunities to success is guaranteeing that approaching AI technology works well for individuals with a large variety of skills. This paper shows how a wearable exoskeleton suit helps paralyzed people regain their ability to walk, how it works, and how this technology can be improved.

Active Loading Control Design for a Wearable Exoskeleton with a Bowden Cable for Transmission

Exoskeletons with a Bowden cable for power transmission have the advantages of a concentrated mass and flexible movement. However, their integrated motor is disturbed by the Bowden cable’s friction, which limits the performance of the force loading response. In this paper, we solve this problem by designing an outer-loop feedforward-feedback proportion-differentiation controller based on an inner loop disturbance observer. Firstly, the inner loop’s dynamic performance is equivalent to the designed nominal model using the proposed disturbance observer, which effectively compensates for the parameter perturbation and friction disturbance. Secondly, based on an analysis of the stability of the inner loop controller, we obtain the stability condition and discuss the influence of modeling errors on the inner loop’s dynamic performance. Thirdly, to avoid excessive noise from the force sensors being introduced into the designed disturbance observer, we propose the feedforward-feedback proportion-differentiation controller based on the nominal model and pole configuration, which improves the outer loop’s force loading performance. Experiments are conducted, which verify the effectiveness of the proposed methods.

Design and control of a passive compliant actuation with positioning measurement by LED and photodiode detector for medical application

Control of assistive exoskeleton robot recently has to be crucial of development and innovation of medical application. To support daily motions for humans, control application of assistive exoskeleton robot allows for limb movement with increased strength and endurance during patient’s wearable exoskeleton robot application. The interaction between such exoskeleton device and the human body at the connecting joint, especially the knees, is the main interest of this design formation. The assistive device requires to design and to develop into innovation design aspect. This research presents the novel design of an active compliant actuation joint in order to increasing the higher torque of actuation than conventional actuation joint. Control design of the higher torque actuation usually difficult priori to conventional torque control. This will contributed to applying the supervisory control for compliant actuation that verified by experiment method. Then the hybrid Radial Basis Function neural network (RBFNN) and PID were proposed for actuating torque control methods. Experimental results show that the design of supervisory control is get better response, and higher producing torque output than the conventional design. Error of torque control of compliant actuation is not instead of [Formula: see text] N·m for applying supervisory control, RBFNN with PID controller. Indeed, the low electromagnetic interference (EMI) positioning system using LED and photodiode detector is proposed to be usable in medical application.

Early Recovery of Walking Ability in Patients After Total Knee Arthroplasty Using a Hip-Wearable Exoskeleton Robot: A Case-Controlled Clinical Trial

Introduction: The Honda Walking Assist (HWA) is a hip-wearable exoskeleton robot for gait training that assists in hip flexion and extension movements to guide hip joint movements during gait. This study aimed to evaluate the effects of walking exercises with HWA in patients who underwent total knee arthroplasty (TKA). Materials and Methods: This study involved 10 patients (11 knees) in the HWA group and 11 patients (11 knees) in the control group who underwent conventional physical therapy. The patients assigned to the HWA group underwent a total of 17-20 gait training sessions, each lasting approximately 20 min from week 1 to 5 following TKA. Self-selected walking speed (SWS), maximum walking speed (MWS), range of motion (ROM), knee extension and flexion torque, and Western Ontario and McMaster Universities Osteoarthritis Index subscales of pain (WOMAC-p) and physical function (WOMAC-f) scores were measured preoperatively, at 2, 4, and 8 weeks following TKA. Results: Interventions were successfully completed in all patients, with no severe adverse events. A significant difference was noted in the time × group interaction effect between preoperative and week 2 SWS and MWS. Regarding knee function, there was a significant difference in the time × group interaction between preoperative and week 2 active ROM extension; however, no significant difference in knee torque, WOMAC-p, and WOMAC-f scores were observed. In the between-group post hoc analysis, WOMAC-f in the HWA group was higher than that in the control group at week 8. Discussion: Although the control group showed a temporary reduction in SWS and MWS 2 weeks after TKA, the HWA group did not. These results suggest that HWA intervention promotes early improvement in walking ability after TKA. Conclusions: The gait training using HWA was safe and feasible and could be effective for the early improvement of walking ability in TKA patients.

SIAT-WEXv2: A Wearable Exoskeleton for Reducing Lumbar Load during Lifting Tasks

Lumbar Exoskeleton, as an important instance of wearable exoskeleton, has broad application prospects in logistics, construction, and other industries. Specifically, in the working scenarios that require long-term and repeated bending and rising movements, active lumbar exoskeleton (ALE) can provide effective protection and flexible assistance to wear’s waist muscles and bones, which will significantly reduce the risk of lumbar muscle strain. How to improve the human-machine coupling and enhance the assistance performance are the main challenges for ALE’s development. Based on the biomechanical analysis of the movement of lifting heavy objects from bottom up, this paper proposes a lightweight but powerful ALE, named as SIAT-WEXv2, which can output maximum assistive force of 28 N. Additionally, we use robust fuzzy adaptive algorithm to improve SIAT-WEXv2’s antidisturbance ability, so that it can provide continuous and supple assistance for wearer. Electromyography (EMG) signals of the lumbar erector spinae (LES) from ten subjects in two experimental cases (with or without SIAT-WEXv2) were collected to evaluate the effectiveness of our new ALE. The experimental results indicate that the reduction of iEMG signal at LES decreased monotonically from 60% ± 5.5% to 40.5% ± 6.5% as the weight of lifting load increased from 0 to 25 kg.

Design an Augmentation Exoskeleton to Enhance Lifting Strength

This paper describes the process of a team of mechanical engineering students in designing and simulating an easy to wear augmentation exoskeleton device. The goal of the project is to develop a fundamental technology associate with the design and control of an upper-body exoskeleton that augments human arm strength and endurance during lifting motion. The technology is intended to be used by military soldiers who are required to frequently lift more than 100 pounds objects, such as heavy machinery, tank ammunition, etc. This proof of concept design focuses on using a wearable device to provide and support both arms with considerable strength to lift more than 100 pounds objects. Some of the features of this upper body exoskeleton include easy to wear, light weight, portable, sturdy, and easy to control. The exoskeleton device mainly consists of two sections: wearable body vest and EMG sensors and Arduino control system. This paper also discussed the range of basic motion of the arms, how the wearable exoskeleton device is developed, and how the performance and efficiency of such device are evaluated.