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.

Motion intensity modeling and trajectory control of upper limb rehabilitation exoskeleton robot based on multi-modal information

The motion intensity of patient is significant for the trajectory control of exoskeleton robot during rehabilitation, as it may have important influence on training effect and human–robot interaction. To design rehabilitation training task according to situation of patients, a novel control method of rehabilitation exoskeleton robot is designed based on motion intensity perception model. The motion signal of robot and the heart rate signal of patient are collected and fused into multi-modal information as the input layer vector of deep learning framework, which is used for the human–robot interaction model of control system. A 6-degree of freedom (DOF) upper limb rehabilitation exoskeleton robot is designed previously to implement the test. The parameters of the model are iteratively optimized by grouping the experimental data, and identification effect of the model is analyzed and compared. The average recognition accuracy of the proposed model can reach up to 99.0% in the training data set and 95.7% in the test data set, respectively. The experimental results show that the proposed motion intensity perception model based on deep neural network (DNN) and the trajectory control method can improve the performance of human–robot interaction, and it is possible to further improve the effect of rehabilitation training.

Serious Games Design Principles Using Virtual Reality to Gamify Upper Limb Stroke Rehabilitation

Stroke is a debilitating condition that impairs one's ability to live independently while also greatly decreasing one's quality of life. For these reasons, stroke rehabilitation is important. Engagement is a crucial part of rehabilitation, increasing a stroke survivor's recovery rate and the positive outcomes of their rehabilitation. For this reason, virtual reality (VR) has been widely used to gamify stroke rehabilitation to support engagement. Given that VR and the serious games that form its basis may not necessarily be engaging in themselves, ensuring that their design is engaging is important. This chapter discusses 39 principles that may be useful for engaging stroke survivors with VR-based rehabilitation post-stroke. This chapter then discusses a subset of the game design principles that are likely to engage stroke survivors with VR designed for upper limb rehabilitation post-stroke.

Biomechanical analysis of grip strength in upper limb rehabilitation

This research is based on biomechanics as a science that involves concepts of engineering, mechanics, physic, anatomy, physiology, and many others, to study the human body with the desire to solve certain problems that may affect the performance of an individual in their work or personal level. This work is an investigative process in these areas of scientific and applied disciplines, in which the attention is focused on the hand as a valuable tool for the occupational performance of the human being, since through it is possible to touch, move, grasp, or manipulate objects. Injuries to this limb may be due to various causes, which require complex surgeries and long periods of rehabilitation to be reversed. This research highlights the importance of certain physical concepts that must be understood by the rehabilitation expert in order not to affect the surgery and thus guarantee the maximum functionality of the patient at the end of the recovery cycle.

Upper Limb Action Identification Based on Physiological Signals and Its Application in Limb Rehabilitation Training

pper limb motor dysfunction brings huge pain and burden to patients with brain trauma, stroke, and cerebral palsy, as well as their relatives. Physiological signals are closely related to the recovery of patients with limb dysfunction. The joint analysis of two key physiological signals, namely, surface electromyographic (sEMG) signal and acceleration signal, enables the scientific and effective evaluation of upper limb rehabilitation. However, the existing indices of upper limb rehabilitation are incomplete, and the current evaluation approaches are not sufficiently objective or quantifiable. To solve the problems, this paper explores upper limb action identification based on physiological signals, and tries to apply the approach to limb rehabilitation training. Specifically, the upper limb action features during limb rehabilitation training were extracted and identified by time-domain feature method, frequency-domain feature method, time-frequency domain feature method, and entropy feature method. Then, the evaluation flow of upper limb rehabilitation, plus the relevant evaluation indices, were given. Experimental results demonstrate the effectiveness of the proposed composite feature identification of upper limb actions, and the proposed evaluation method for limb rehabilitation.

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.