Reachable Workspace and Proximal Function Measures for Quantifying Upper Limb Motion.

There are a lack of quantitative measures for clinically assessing upper limb function. Conventional biomechanical performance measures are restricted to specialist labs due to hardware cost and complexity, while the resulting measurements require specialists for analysis. Depth cameras are low cost and portable systems that can track surrogate joint positions. However, these motions may not be biologically consistent, which can result in noisy, inaccurate movements. This paper introduces a rigid body modelling method to enforce biological feasibility of the recovered motions. This method is evaluated on an existing depth camera assessment: the reachable workspace (RW) measure for assessing gross shoulder function. As a rigid body model is used, position estimates of new proximal targets can be added, resulting in a proximal function (PF) measure for assessing a subject's ability to touch specific body landmarks. The accuracy, and repeatability of these measures is assessed on ten asymptomatic subjects, with and without rigid body constraints. This analysis is performed both on a low-cost depth camera system and a gold-standard active motion capture system. The addition of rigid body constraints was found to improve accuracy and concordance of the depth camera system, particularly in lateral reaching movements. Both RW and PF measures were found to be feasible candidates for clinical assessment, with future analysis needed to determine their ability to detect changes within specific patient populations

Bilateral Assessment of Functional Tasks for Robot-assisted Therapy Applications

This article presents a novel evaluation system along with methods to evaluate bilateral coordination of arm function on activities of daily living tasks before and after robot-assisted therapy. An affordable bilateral assessment system (BiAS) consisting of two mini-passive measuring units modeled as three degree of freedom robots is described. The process for evaluating functional tasks using the BiAS is presented and we demonstrate its ability to measure wrist kinematic trajectories. Three metrics, phase difference, movement overlap, and task completion time, are used to evaluate the BiAS system on a bilateral symmetric (bi-drink) and a bilateral asymmetric (bi-pour) functional task. Wrist position and velocity trajectories are evaluated using these metrics to provide insight into temporal and spatial bilateral deficits after stroke. The BiAS system quantified movements of the wrists during functional tasks and detected differences in impaired and unimpaired arm movements. Case studies showed that stroke patients compared to healthy subjects move slower and are less likely to use their arm simultaneously even when the functional task requires simultaneous movement. After robot-assisted therapy, interlimb coordination spatial deficits moved toward normal coordination on functional tasks

Design, implementation, control, and user evaluations of assiston-arm self-aligning upper-extremity exoskeleton

Physical rehabilitation therapy is indispensable for treating neurological disabilities. The use of robotic devices for rehabilitation holds high promise, since these devices can bear the physical burden of rehabilitation exercises during intense therapy sessions, while therapists are employed as decision makers. Robot-assisted rehabilitation devices are advantageous as they can be applied to patients with all levels of impairment, allow for easy tuning of the duration and intensity of therapies and enable customized, interactive treatment protocols. Moreover, since robotic devices are particularly good at repetitive tasks, rehabilitation robots can decrease the physical burden on therapists and enable a single therapist to supervise multiple patients simultaneously; hence, help to lower cost of therapies. While the intensity and quality of manually delivered therapies depend on the skill and fatigue level of therapists, high-intensity robotic therapies can always be delivered with high accuracy. Thanks to their integrated sensors, robotic devices can gather measurements throughout therapies, enable quantitative tracking of patient progress and development of evidence-based personalized rehabilitation programs. In this dissertation, we present the design, control, characterization and user evaluations of AssistOn-Arm, a powered, self-aligning exoskeleton for robotassisted upper-extremity rehabilitation. AssistOn-Arm is designed as a passive back-driveable impedance-type robot such that patients/therapists can move the device transparently, without much interference of the device dynamics on natural movements. Thanks to its novel kinematics and mechanically transparent design, AssistOn-Arm can passively self-align its joint axes to provide an ideal match between human joint axes and the exoskeleton axes, guaranteeing ergonomic movements and comfort throughout physical therapies. The self-aligning property of AssistOn-Arm not only increases the usable range of motion for robot-assisted upper-extremity exercises to cover almost the whole human arm workspace, but also enables the delivery of glenohumeral mobilization (scapular elevation/depression and protraction/retraction) and scapular stabilization exercises, extending the type of therapies that can be administered using upper-extremity exoskeletons. Furthermore, the self-alignment property of AssistOn-Arm signi cantly shortens the setup time required to attach a patient to the exoskeleton. As an impedance-type device with high passive back-driveability, AssistOn- Arm can be force controlled without the need of force sensors; hence, high delity interaction control performance can be achieved with open-loop impedance control. This control architecture not only simpli es implementation, but also enhances safety (coupled stability robustness), since open-loop force control does not su er from the fundamental bandwidth and stability limitations of force-feedback. Experimental characterizations and user studies with healthy volunteers con- rm the transparency, range of motion, and control performance of AssistOn- Ar

Improved spatial–temporal graph convolutional networks for upper limb rehabilitation assessment based on precise posture measurement

After regular rehabilitation training, paralysis sequelae can be significantly reduced in patients with limb movement disorders caused by stroke. Rehabilitation assessment is the basis for the formulation of rehabilitation training programs and the objective standard for evaluating the effectiveness of training. However, the quantitative rehabilitation assessment is still in the experimental stage and has not been put into clinical practice. In this work, we propose improved spatial-temporal graph convolutional networks based on precise posture measurement for upper limb rehabilitation assessment. Two Azure Kinect are used to enlarge the angle range of the visual field. The rigid body model of the upper limb with multiple degrees of freedom is established. And the inverse kinematics is optimized based on the hybrid particle swarm optimization algorithm. The self-attention mechanism map is calculated to analyze the role of each upper limb joint in rehabilitation assessment, to improve the spatial-temporal graph convolution neural network model. Long short-term memory is built to explore the sequence dependence in spatial-temporal feature vectors. An exercise protocol for detecting the distal reachable workspace and proximal self-care ability of the upper limb is designed, and a virtual environment is built. The experimental results indicate that the proposed posture measurement method can reduce position jumps caused by occlusion, improve measurement accuracy and stability, and increase Signal Noise Ratio. By comparing with other models, our rehabilitation assessment model achieved the lowest mean absolute deviation, root mean square error, and mean absolute percentage error. The proposed method can effectively quantitatively evaluate the upper limb motor function of stroke patients

Robot Assisted Shoulder Rehabilitation: Biomechanical Modelling, Design and Performance Evaluation

The upper limb rehabilitation robots have made it possible to improve the motor recovery in stroke survivors while reducing the burden on physical therapists. Compared to manual arm training, robot-supported training can be more intensive, of longer duration, repetitive and task-oriented. To be aligned with the most biomechanically complex joint of human body, the shoulder, specific considerations have to be made in the design of robotic shoulder exoskeletons. It is important to assist all shoulder degrees-of-freedom (DOFs) when implementing robotic exoskeletons for rehabilitation purposes to increase the range of motion (ROM) and avoid any joint axes misalignments between the robot and human’s shoulder that cause undesirable interaction forces and discomfort to the user. The main objective of this work is to design a safe and a robotic exoskeleton for shoulder rehabilitation with physiologically correct movements, lightweight modules, self-alignment characteristics and large workspace. To achieve this goal a comprehensive review of the existing shoulder rehabilitation exoskeletons is conducted first to outline their main advantages and disadvantages, drawbacks and limitations. The research has then focused on biomechanics of the human shoulder which is studied in detail using robotic analysis techniques, i.e. the human shoulder is modelled as a mechanism. The coupled constrained structure of the robotic exoskeleton connected to a human shoulder is considered as a hybrid human-robot mechanism to solve the problem of joint axes misalignments. Finally, a real-scale prototype of the robotic shoulder rehabilitation exoskeleton was built to test its operation and its ability for shoulder rehabilitation

Sensorimotor Control of 3D Arm Movement and Stability in Post-Stroke Hemiparesis

Deficits of the affected arm in people with post-stroke hemiparesis have been generally associated with decreased strength and increased spasticity. These deficits are varied in proximal (shoulder) and distal (elbow) joints which results in an overall impairment during movement or during stabilization of hand position in space. In this study, reaching of the hemiparetic arm in 3D workspace was characterized by a curved and non-smooth endpoint trajectory and a reduced functional range of motion, compared to the unimpaired arm. Smoother trajectories were observed in the acceleration phase more than the deceleration phase, which was common to both the stroke subjects and the neurologically intact controls. Decreased range of motion of the paretic arm in the proximal joint was associated with shoulder weakness, whereas limited range of motion in the elbow appeared to be due to increased antagonist muscle activation. In a task requiring subjects to stabilize their hand at different positions in space, arm weakness and movement synergy constraints may have contributed to stroke survivors generally decreasing the plane of elevation in order to maintain stable arm postures during movement and then stabilize the hand in space. The degree of decreased plane of elevation was negatively correlated with the Fugl-Meyer score. For a task when fine control movement was required simultaneously with a stable arm posture, stroke subjects demonstrated an inability to grade fine muscle control, resulting in larger range of the plane of elevation movements and larger endpoint error. These findings suggest that shoulder strength training might have important implications to the recovery of movement and ability to stabilize the hemiparetic arm during functional tasks

Superficial shoulder muscle synergy analysis in Facioscapulohumeral Dystrophy during humeral elevation tasks

Facioscapulohumeral Dystrophy (FSHD) is a progressive muscle-wasting disease which leads to a decline in upper extremity functionality. Although the scapulohumeral joint's stability and functionality are affected, evidence on the synergetic control of the shoulder muscles in FSHD individuals is still lacking. The aim of this study is to understand the neuromuscular changes in shoulder muscle control in people with FSHD. Upper arm kinematics and electromyograms (EMG) of eight upper extremity muscles were recorded during shoulder abduction-adduction and flexion-extension tasks in eleven participants with FSHD and eleven healthy participants. Normalized muscle activities were extracted from EMG signals. Non-negative matrix factorization was used to compute muscle synergies. Maximum muscle activities were compared using non-parametric analysis of variance. Similarities between synergies were also calculated using correlation. The Biceps Brachii was significantly more active in the FSHD group (25±2%) while Trapezius Ascendens and Serratus Anterior were less active (32±7% and 39±4% respectively). Muscle synergy weights were altered in FSHD individuals and showed greater diversity while controls mostly used one synergy for both tasks. The decreased activity by selected scapula rotator muscles and muscle synergy weight alterations show that neuromuscular control of the scapulohumeral joint is less consistent in people with FSHD compared to healthy participants. Assessments of muscle coordination strategies can be used to evaluate motor output variability and assist in management of the disease

Elbow exoskeleton mechanism for multistage poststroke rehabilitation.

More than three million people are suffering from stroke in England. The process of post-stroke rehabilitation consists of a series of biomechanical exercises- controlled joint movement in acute phase; external assistance in the mid phase; and variable levels of resistance in the last phase. Post-stroke rehabilitation performed by physiotherapist has many limitations including cost, time, repeatability and intensity of exercises. Although a large variety of arm exoskeletons have been developed in the last two decades to substitute the conventional exercises provided by physiotherapist, most of these systems have limitations with structural configuration, sensory data acquisition and control architecture. It is still difficult to facilitate multistage post-stroke rehabilitation to patients sited around hospital bed without expert intervention. To support this, a framework for elbow exoskeleton has been developed that is portable and has the potential to offer all three types of exercises (external force, assistive and resistive) in a single structure. The design enhances torque to weight ratio compared to joint based actuation systems. The structural lengths of the exoskeleton are determined based on the mean anthropometric parameters of healthy users and the lengths of upperarm and forearm are determined to fit a wide range of users. The operation of the exoskeleton is divided into three regions where each type of exercise can be served in a specific way depending on the requirements of users. Electric motor provides power in the first region of operation whereas spring based assistive force is used in the second region and spring based resistive force is applied in the third region. This design concept provides an engineering solution of integrating three phases of post-stroke exercises in a single device. With this strategy, the energy source is only used in the first region to power the motor whereas the other two modes of exercise can work on the stored energy of springs. All these operations are controlled by a single motor and the maximum torque of the motor required is only 5 Nm. However, due to mechanical advantage, the exoskeleton can provide the joint torque up to 10 Nm. To remove the dependency on biosensor, the exoskeleton has been designed with a hardware-based mechanism that can provide assistive and resistive force. All exoskeleton components are integrated into a microcontroller-based circuit for measuring three joint parameters (angle, velocity and torque) and for controlling exercises. A user-friendly, multi-purpose graphical interface has been developed for participants to control the mode of exercise and it can be managed manually or in automatic mode. To validate the conceptual design, a prototype of the exoskeleton has been developed and it has been tested with healthy subjects. The generated assistive torque can be varied up to 0.037 Nm whereas resistive torque can be varied up to 0.057 Nm. The mass of the exoskeleton is approximately 1.8 kg. Two comparative studies have been performed to assess the measurement accuracy of the exoskeleton. In the first study, data collected from two healthy participants after using the exoskeleton and Kinect sensor by keeping Kinect sensor as reference. The mean measurement errors in joint angle are within 5.18 % for participant 1 and 1.66% for participant 2; the errors in torque measurement are within 8.48% and 7.93% respectively. In the next study, the repeatability of joint measurement by exoskeleton is analysed. The exoskeleton has been used by three healthy users in two rotation cycles. It shows a strong correction (correlation coefficient: 0.99) between two consecutive joint angle measurements and standard deviation is calculated to determine the error margin which comes under acceptable range (maximum: 8.897). The research embodied in this thesis presents a design framework of a portable exoskeleton model for providing three modes of exercises, which could provide a potential solution for all stages of post- stroke rehabilitation