Effectiveness of lower limb rehabilitation protocol using mobile health on quality of life, functional strength and functional capacity among knee osteoarthritis patients who were overweight and obese: A randomized controlled trial

Objective: This study aimed to investigate the effectiveness of the lower limb rehabilitation protocol (LLRP) using mobile health (mHealth) on quality of life (QoL), functional strength, and functional capacity among knee OA patients who were overweight and obese. Materials and Methods: In the current trial, 114 patients were recruited and randomized into either the rehabilitation group with mobile health (RGw-mHealth) receiving reminders by using mHealth to carry on the strengthening exercises of LLRP and instructions of daily care (IDC), the rehabilitation group without mobile health (RGwo-mHealth) following the strengthening exercises of LLRP and instructions of daily care (IDC) and control group (CG) only following the IDC for duration of 12-weeks. The reminders for using mHealth were provided two times a day for three days a week. Primary outcome measures were QoL assessed by the Western Ontario and McMaster Universities Osteoarthritis Index summary score, and functional strength by Five-Repetition Sit-To-Stand Test. Secondary outcome measure was functional capacity assessed by the Gait Speed Test. The assessments of QoL, functional strength, and functional capacity were taken at baseline and posttest after 12-weeks of intervention. Results: After 12 weeks of intervention, patients in all three groups had statistically significant improvement in QoL within groups (p < 0.05). Furthermore, patients in the RGw-mHealth and RGwo-mHealth had statistically significant improvement in functional strength and walking gait speed within groups (p < 0.05). The pairwise between-group comparisons (Bonferroni post hoc test) of the mean changes in QoL, functional strength, and functional capacity at posttest assessments revealed that patients in the RGw-mHealth had statistically significant greater mean change in QoL, functional strength and functional capacity relative to both the RGwo-mHealth and CG (p < 0.001). Conclusion: Improvement in QoL, functional strength, and functional capacity was larger among patients in the RGw-mHealth compared with the RGwo-mHealth or CG. Keywords: Osteoarthritis, knee, overweight, rehabilitation. mobile health.

Robust Walking Control of a Lower Limb Rehabilitation Exoskeleton Coupled with a Musculoskeletal Model via Deep Reinforcement Learning

Background: Few studies have systematically investigated robust controllers for lower limb rehabilitation exoskeletons (LLREs) that can safely and effectively assist users with a variety of neuromuscular disorders to walk with full autonomy. One of the key challenges for developing such a robust controller is to handle different degrees of uncertain human-exoskeleton interaction forces from the patients. Consequently, conventional walking controllers either are patient-condition specific or involve tuning of many control parameters, which could behave unreliably and even fail to maintain balance. Methods: We present a novel and robust controller for a LLRE based on a decoupled deep reinforcement learning framework with three independent networks, which aims to provide reliable walking assistance against various and uncertain human-exoskeleton interaction forces. The exoskeleton controller is driven by a neural network control policy that acts on a stream of the LLRE’s proprioceptive signals, including joint kinematic states, and subsequently predicts real-time position control targets for the actuated joints. To handle uncertain human-interaction forces, the control policy is trained intentionally with an integrated human musculoskeletal model and realistic human-exoskeleton interaction forces. Two other neural networks are connected with the control policy network to predict the interaction forces and muscle coordination. To further increase the robustness of the control policy, we employ domain randomization during training that includes not only randomization of exoskeleton dynamics properties but, more importantly, randomization of human muscle strength to simulate the variability of the patient’s disability. Through this decoupled deep reinforcement learning framework, the trained controller of LLREs is able to provide reliable walking assistance to the human with different degrees of neuromuscular disorders. Results and Conclusion: A universal, RL-based walking controller is trained and virtually tested on a LLRE system to verify its effectiveness and robustness in assisting users with different disabilities such as passive muscles (quadriplegic), muscle weakness, or hemiplegic conditions. An ablation study demonstrates strong robustness of the control policy under large exoskeleton dynamic property ranges and various human-exoskeleton interaction forces. The decoupled network structure allows us to isolate the LLRE control policy network for testing and sim-to-real transfer since it uses only proprioception information of the LLRE (joint sensory state) as the input. Furthermore, the controller is shown to be able to handle different patient conditions without the need for patient-specific control parameters tuning.

Short-Term Effects of Strengthening Exercises of the Lower Limb Rehabilitation Protocol on Pain, Stiffness, Physical Function, and Body Mass Index among Knee Osteoarthritis Participants Who Were Overweight or Obese: A Clinical Trial

Background. Osteoarthritis (OA) of the knee is defined as a progressive disease of the synovial joints and is characterized by wear and tear of the cartilage and underlying bone. This study aimed to determine the short-term effects of the lower limb rehabilitation protocol (LLRP) on pain, stiffness, physical function, and body mass index (BMI) among knee OA participants who were overweight or obese. Methodology. A single-blinded randomized controlled trial of one-month duration was conducted at Rehmatul-Lil-Alameen Postgraduate Institute, Lahore, Pakistan. Fifty overweight or obese participants with knee OA were randomly divided into two groups by a computer-generated number. Participants in the rehabilitation protocol group (RPG) were provided with leaflets explaining the strengthening exercises of the LLRP and instruction of daily care (IDC), while the participants in the control group (CG) were provided with leaflets explaining the IDC only for a duration of four weeks. The primary outcome measures were the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) scores for pain, stiffness, and physical function. The secondary outcome measures were BMI, exercise adherence, and patients’ satisfaction assessed by using the numeric rating scale ranging from 0 to 10. The paired-sample t-test was used to analyze the differences within groups from baseline to posttest evaluations. The analysis of variance 2 × 2 factor was used to analyze the differences in BMI, knee pain, stiffness, and physical function between the groups. Results. Participants in the RPG and CG reported a statistically significant reduction in knee pain and stiffness ( p ≤ 0.05 ) within the group. The reduction in the scores of knee pain was higher in participants in the RPG than that in participants in the CG ( p = 0.001 ). Additionally, participants in the RPG reported greater satisfaction ( p = 0.001 ) and higher self-reported exercise adherence ( p = 0.010 ) and coordinator-reported exercise adherence ( p = 0.046 ) than the participants in the CG. Conclusion. Short-term effects of the LLRP appear to reduce knee pain and stiffness only, but not physical function and BMI.

Effects of a Brain-Computer Interface-Operated Lower Limb Rehabilitation Robot on Motor Function Recovery in Patients with Stroke

Purpose. To observe the effect of a brain-computer interface-operated lower limb rehabilitation robot (BCI-LLRR) on functional recovery from stroke and to explore mechanisms. Methods. Subacute-phase stroke patients were randomly divided into two groups. In addition to the routine intervention, patients in the treatment group trained on the BCI-LLRR and underwent the lower limb pedal training in the control group, both for the same time (30 min/day). All patients underwent assessment by instruments such as the National Institutes of Health Stroke Scale (NIHSS) and the Fugl–Meyer upper and lower limb motor function and balance tests, at 2 and 4 weeks of treatment and at 3 months after the end of treatment. Patients were also tested before treatment and after 4 weeks by leg motor evoked potential (MEP) and diffusion tensor imaging/tractography (DTI/DTT) of the head. Results. After 4 weeks, the Fugl–Meyer leg function and NIHSS scores were significantly improved in the treatment group vs. controls ( P < 0.01 ). At 3 months, further significant improvement was observed. The MEP amplitude and latency of the treatment group were significantly improved vs. controls. The effect of treatment on fractional anisotropy values was not significant. Conclusions. The BCI-LLRR promoted leg functional recovery after stroke and improved activities of daily living, possibly by improving cerebral-cortex excitability and white matter connectivity.

Short-Term Effects of Strengthening Exercises of Lower Limb Rehabilitation Protocol on Pain, Stiffness, Physical Function and Body Mass Index among Knee Osteoarthritis Participants Who Were Overweight or Obese: A Clinical Trial

Background. Osteoarthritis (OA) of the knee is defined as a progressive disease of the synovial joints and is characterized by wear and tear of cartilage and underlying bone. This study aimed to determine the short-term effects of the lower limb rehabilitation protocol (LLRP) on pain, stiffness, physical function, and body mass index (BMI) among knee OA participants who were overweight or obese. Methodology. Single blinded randomized controlled trial of one-month duration was conducted at Rehmatul-Lil-Alameen Postgraduate Institute, Lahore, Pakistan. Fifty overweight or obese participants with knee OA were randomly divided into two groups by a computer-generated number. Participants in the Rehabilitation Protocol Group (RPG) were provided with leaflets explaining the strengthening exercises of the LLRP and instruction of daily care (IDC), while the participants in the Control Group (CG) were provided with leaflets explaining IDC only for a duration of four weeks. The primary outcome measures were the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) scores for pain, stiffness and physical function. The secondary outcome measures were BMI, exercise adherence, and patients satisfaction by the numeric rating scale ranging from 0 to 10. Paired Samples t-test was used to analyze the differences within groups from baseline to post-test evaluations. The analysis of variance was used to analyze the difference of BMI, knee pain, stiffness, and physical function between the groups. Results. Participants in the RPG and CG reported a statistically significant reduction in knee pain, and stiffness (p ≤ 0.05) within group. The reduction in the scores of knee pain was higher in participants of the RPG than the CG (p = 0.001). Additionally, participants in the RPG reported greater satisfaction (p = 0.001), higher self-reported exercise adherence (p = 0.010) and coordinator-reported exercise adherence (p = 0.046) compared to the participants in the CG. Conclusion. Short-term effects of the LLRP appear to reduce knee pain and stiffness only, but not physical function and BMI.

Design and Simulation Analysis of a Robot-Assisted Gait Trainer with the PBWS System

In response to the ever-increasing demand of lower limb rehabilitation, this paper presents a novel robot-assisted gait trainer (RGT) to assist the elderly and the pediatric patients with neurological impairments in the lower limb rehabilitation training (LLRT). The RGT provides three active degrees of freedom (DoF) to both legs that are used to implement the gait cycle in such a way that the natural gait is not significantly affected. The robot consists of (i) the partial body weight support (PBWS) system to assist patients in sit-to-stand transfer via the precision linear rail system and (ii) the bipedal end-effector (BE) to control the motions of lower limbs via two mechanical arms. The robot stands out for multiple modes of training and optimized functional design to improve the quality of life for those patients. To analyze the performance of the RGT, the kinematic and static models are established in this paper. After that, the reachable workspace and motion trajectory are analyzed to cover the motion requirements and implement natural gait cycle. The preliminary results demonstrate the usability of the robot.

A Self-Adaptive-Coefficient-Double-Power Sliding Mode Control Method for Lower Limb Rehabilitation Exoskeleton Robot

Lower limb rehabilitation exoskeleton robots have the characteristics of nonlinearity and strong coupling, and they are easily disturbed during operation by environmental factors. Thus, an accurate dynamic model of the robot is difficult to obtain, and achieving trajectory tracking control of the robot is also difficult. In this article, a self-adaptive-coefficient double-power sliding mode control method is proposed to overcome the difficulty of tracking the robot trajectory. The method combines an estimated dynamic model with sliding mode control. A nonlinear control law was designed based on the robot dynamics model and computational torque method, and a compensation term of control law based on double-power reaching law was introduced to reduce the disturbance from model error and environmental factors. The self-adaptive coefficient of the compensation term of the control law was designed to adaptively adjust the compensation term to improve the anti-interference ability of the robot. The simulation and experiment results show that the proposed method effectively improves the trajectory tracking accuracy and anti-interference ability of the robot. Compared with the traditional computed torque method, the proposed method decreases the tracking error by more than 71.77%. The maximum absolute error of the hip joint and knee joint remained below 0.55° and 1.65°, respectively, in the wearable experiment of the robot.