Posts Tagged Kinematics.

[Abstract] The Role of Robotic Path Assistance and Weight Support in Facilitating 3D Movements in Individuals With Poststroke Hemiparesis

Background. High-intensity repetitive training is challenging to provide poststroke. Robotic approaches can facilitate such training by unweighting the limb and/or by improving trajectory control, but the extent to which these types of assistance are necessary is not known.

Objective. The purpose of this study was to examine the extent to which robotic path assistance and/or weight support facilitate repetitive 3D movements in high functioning and low functioning subjects with poststroke arm motor impairment relative to healthy controls.

Methods. Seven healthy controls and 18 subjects with chronic poststroke right-sided hemiparesis performed 300 repetitions of a 3D circle-drawing task using a 3D Cable-driven Arm Exoskeleton (CAREX) robot. Subjects performed 100 repetitions each with path assistance alone, weight support alone, and path assistance plus weight support in a random order over a single session. Kinematic data from the task were used to compute the normalized error and speed as well as the speed-error relationship.

Results. Low functioning stroke subjects (Fugl-Meyer Scale score = 16.6 ± 6.5) showed the lowest error with path assistance plus weight support, whereas high functioning stroke subjects (Fugl-Meyer Scale score = 59.6 ± 6.8) moved faster with path assistance alone. When both speed and error were considered together, low functioning subjects significantly reduced their error and increased their speed but showed no difference across the robotic conditions.

Conclusions. Robotic assistance can facilitate repetitive task performance in individuals with severe arm motor impairment, but path assistance provides little advantage over weight support alone. Future studies focusing on antigravity arm movement control are warranted poststroke.


via The Role of Robotic Path Assistance and Weight Support in Facilitating 3D Movements in Individuals With Poststroke Hemiparesis – Preeti Raghavan, Seda Bilaloglu, Syed Zain Ali, Xin Jin, Viswanath Aluru, Megan C. Buckley, Alvin Tang, Arash Yousefi, Jennifer Stone, Sunil K. Agrawal, Ying Lu, 2020

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[Abstract] Movement kinematics and proprioception in post-stroke spasticity: assessment using the Kinarm robotic exoskeleton – Full Text PDF



Motor impairment after stroke interferes with performance of everyday activities. Upper limb spasticity may further disrupt the movement patterns that enable optimal function; however, the specific features of these altered movement patterns, which differentiate individuals with and without spasticity, have not been fully identified. This study aimed to characterize the kinematic and proprioceptive deficits of individuals with upper limb spasticity after stroke using the Kinarm robotic exoskeleton.


Upper limb function was characterized using two tasks: Visually Guided Reaching, in which participants moved the limb from a central target to 1 of 4 or 1 of 8 outer targets when cued (measuring reaching function) and Arm Position Matching, in which participants moved the less-affected arm to mirror match the position of the affected arm (measuring proprioception), which was passively moved to 1 of 4 or 1 of 9 different positions. Comparisons were made between individuals with (n = 35) and without (n = 35) upper limb post-stroke spasticity.


Statistically significant differences in affected limb performance between groups were observed in reaching-specific measures characterizing movement time and movement speed, as well as an overall metric for the Visually Guided Reaching task. While both groups demonstrated deficits in proprioception compared to normative values, no differences were observed between groups. Modified Ashworth Scale score was significantly correlated with these same measures.


The findings indicate that individuals with spasticity experience greater deficits in temporal features of movement while reaching, but not in proprioception in comparison to individuals with post-stroke motor impairment without spasticity. Temporal features of movement can be potential targets for rehabilitation in individuals with upper limb spasticity after stroke.

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via Movement kinematics and proprioception in post-stroke spasticity: assessment using the Kinarm robotic exoskeleton – Researcher | An App For Academics

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[Abstract + References] Design and Kinematics Analysis of a Bionic Finger Hand Rehabilitation Robot Mechanism


The rehabilitation process of human fingers is a coupling movement of wearable hand rehabilitation equipment and human fingers, and its design must be based on the kinematics of human fingers. In this paper, the forward kinematics and inverse kinematics models are established for the index finger. Kinematics analysis is carried out. Then a bionic finger rehabilitation robot is designed according to the movement characteristics of the finger, A parallelogram linkage mechanism is proposed to make the joint independent drive, realize the flexion/extension movement, and perform positive kinematics and inverse kinematics analysis on the mechanism. The results show that it conforms to the kinematics of the index finger and can be used as the mechanism model of the finger rehabilitation robot.
1. Ibrahim Yildiz, “A Low-Cost and Lightweight Alternative to Rehabilitation Robots: Omnidirectional Interactive Mobile Robot for Arm Rehabilitation” in Arabian Journal for Science & Engineering, Springer Science & Business Media B.V., vol. 43, no. 3, pp. 1053-1059, 2018.

2. Bai Shaoping, Gurvinder S. Virk, Thomas G. Sugar, Wearable Exoskeleton Systems: Design control and applications[M], Institution of Engineering and Technology Control, pp. 1-406, 2018.

3. Kai Zhang, Xiaofeng Chen et al., “System Framework of Robotics in Upper Limb Rehabilitation on Poststroke Motor Recovery”, Behavioural Neurology, vol. 12, pp. 1-14, 2018.

4. Yang Haile, Zhu Huiying, Lin Xingyu, “Review of Exoskeleton Wearable Rehabilitation System[J]”, Metrology and testing technology, vol. 46, no. 03, pp. 40-44, 2019.

5. Xiang Shichuan, Meng Qiaoling, Yu Hongliu, Meng Qingyun, “Research status of compliant exoskeleton rehabilitation manipulator [J]”, Chinese Journal of Rehabilitation Medicine, vol. 33, no. 04, pp. 461-465+474, 2018.

6. Wu Hongjian, Li Lina, Li Long, Liu Tian, Jue Wang, “Review of comprehensive intervention by hand rehabilitation robot after stroke [J]”, Journal of biomedical engineering, vol. 36, no. 01, pp. 151-156, 2019.

7. Yu Junwei, Xu Hongbin, Xu Taojin, Zhang Chengjie, Lu Shiqing, “Structure Design and Finite Element Analysis of a Rope Traction Upper Limb Rehabilitation Robot [J]”, Mechanical transmission, vol. 42, no. 12, pp. 93-97, 2018.

8. Chang Ying, Meng Qingyun, Yu Hongliu, “Research progress on the development of hand rehabilitation robot [J]”, Beijing Biomedical Engineering, vol. 37, no. 06, pp. 650-656, 2018.

9. N A I M Rosli, M A A Rahman, S A Mazlan et al., “Electrocardiographic (ECG) and Electromyographic (EMG) signals fusion for physiological device in rehab application[C]”, IEEE Student Conference on Research and Development, pp. 1-5, 2015.

10. K O Thielbar, K M Triandafilou, H C Fischer et al., “Benefits of using a voice and EMG- Driven actuated glove to support occupational therapy for stroke survivors”, IEEE Trans Neural Syst Rehabil Eng, vol. 25, no. 3, pp. 297-305, 2017.


via Design and Kinematics Analysis of a Bionic Finger Hand Rehabilitation Robot Mechanism – IEEE Conference Publication

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[Abstract] Development of a Compatible Exoskeleton (Co-Exos II) for Upper-Limb Rehabilitation


A key approach for reducing motor impairment and regaining independence after spinal cord injuries or strokes is frequent and repetitive functional training. A compatible exoskeleton (Co-Exos II) is proposed for the upper-limb rehabilitation. A compatible configuration was selected according to optimum configuration principles. Four passive translational joints were introduced into the connecting interfaces to adapt the glenohumeral joint (GH) movements and improve the compatibility of the exoskeleton. This configuration of the passive joints could reduce the influence of gravity of the exoskeleton device and the upper extremities. A Co-Exos II prototype was developed and still owned a compact volume. A new approach was presented to compensate the vertical GH movements. The shoulder closed-loop was simplified as a guide-bar mechanism. The compatible models of this loop were established based on the kinematic model of GH. The compatible experiments were completed to verify the kinematic models and analyze the human-machine compatibility of Co-Exos II. The theoretical displacements of the translational joints were calculated by the kinematic model of the shoulder loop. The passive joints exhibited good compensations for the GH movements through comparing the theoretical and measured results, especially vertical GH movements. Co-Exos II showed good human-machine compatibility for upper limbs.

via Development of a Compatible Exoskeleton (Co-Exos II) for Upper-Limb Rehabilitation* – IEEE Conference Publication

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[ARTICLE] Determining the Accuracy of Oculus Touch Controllers for Motor Rehabilitation Applications Using Quantifiable Upper Limb Kinematics: Validation Study – Full Text


Background: As commercial motion tracking technology becomes more readily available, it is necessary to evaluate the accuracy of these systems before using them for biomechanical and motor rehabilitation applications.

Objective: This study aimed to evaluate the relative position accuracy of the Oculus Touch controllers in a 2.4 x 2.4 m play-space.

Methods: Static data samples (n=180) were acquired from the Oculus Touch controllers at step sizes ranging from 5 to 500 mm along 16 different points on the play-space floor with graph paper in the x (width), y (height), and z (depth) directions. The data were compared with reference values using measurements from digital calipers, accurate to 0.01 mm; physical blocks, for which heights were confirmed with digital calipers; and for larger step sizes (300 and 500 mm), a ruler with hatch marks to millimeter units.

Results: It was found that the maximum position accuracy error of the system was 3.5 ± 2.5 mm at the largest step size of 500 mm along the z-axis. When normalized to step size, the largest error found was 12.7 ± 9.9% at the smallest step size in the y-axis at 6.23 mm. When the step size was <10 mm in any direction, the relative position accuracy increased considerably to above 2% (approximately 2 mm at maximum). An average noise value of 0.036 mm was determined. A comparison of these values to cited visual, goniometric, and proprioceptive resolutions concludes that this system is viable for tracking upper-limb movements for biomechanical and rehabilitation applications. The accuracy of the system was also compared with accuracy values from previous studies using other commercially available devices and a multicamera, marker-based professional motion tracking system.

Conclusions: The study found that the linear position accuracy of the Oculus Touch controllers was within an agreeable range for measuring human kinematics in rehabilitative upper-limb exercise protocols. Further testing is required to ascertain acceptable repeatability in multiple sessions and rotational accuracy.


Current gaming and virtual reality platforms [1] that use motion-controlled interfaces offer an affordable and accessible method of tracking human kinematics. However, given that consumer-grade platforms are originally intended for playing video games and to immerse players in virtual environments, their tracking performance should be evaluated before they are employed as tools for biomechanical or clinical analysis [2]. Previously tested rehabilitation protocols using commercial gaming technology such as Wii Motes (Nintendo Co, Ltd, Kyoto, Japan) to provide positional feedback for trunk compensation [3] or a Kinect (Microsoft Corporation, Redmond, United States) to measure range and speed of motion for upper-limb exercises [4,5] have shown potential to be used as rehabilitation tools that could provide quantifiable changes in clients’ kinematic motor abilities to therapists. Other studies using accelerometers to track patterns in functional upper-limb movements were able to capture differences similar to those measured by clinical scales [6] and found benefits from objective quantitative evaluations of changes in motor ability during therapy regimens, which can be collected from in-game progress reports [7]. In addition, success has been found in translating kinematic upper-limb metrics to clinical Fugl-Meyer scoring [8] and in detecting exercise repetitions via kinematic monitoring for telerehabilitation and at-home programs [9]. Current clinical assessments for upper-limb motor function, such as the Fugl-Meyer Assessment and Wolf Motor Function Test, only provide low-resolution point-scores rated qualitatively by therapists, and kinematic analysis of upper-limb motion has been reported to be a useful addition to these clinical assessments [10]. When measuring range of motion in a clinical setting, the goniometer is considered a gold-standard clinical measurement tool used by therapists [11]. However, only static joint angles can be measured, and typically with some visual estimation and multiple testers [12].

One of the latest (released December 2016) devices to be developed for interacting with virtual environments is the Oculus Touch (Oculus VR, LLC, Menlo Park, CA, United States) controller set. The controllers are peripheral accessories of the Oculus Rift virtual reality headset and are employed to track users’ hand movements. Their tracking system employs a proprietary algorithm that collects data from infrared sensors via constellation tracking [13] and inertial measurement units (IMUs). Given that the controllers are wireless, lightweight, low-cost devices that can be used to track a user’s hand position and orientation in 3-dimensional (3D) space, they could have the potential to be employed in rehabilitative and biomechanical motion-tracking applications. At the time of this study, there was no sufficient information about the tracking performance of the controllers provided by the manufacturer, and there is currently a lack of scientific papers employing a systematic approach to test their potential application as tools for motion-tracking data capture. As a result, in this study, we evaluated the tracking accuracy of the Oculus Touch controllers to present a preliminary evaluation that could be informative to the biomechanical and rehabilitation research community. The specific aim of the experiment was to quantify the relative positional accuracy of the Oculus Touch controllers in 3 spatial dimensions. As the controllers are intended for hand-held motion control, the evaluation setup was centered around the movement size for standing/sitting upper-limb reaching tasks.


Technical Setup

An Oculus Touch controller (Figure 1), 2 Oculus Sensors, an Oculus Rift headset, and a computer running Windows 10 (Microsoft Corporation) were employed in this study.

A custom computer application was developed in Unity 2017 (Unity Technologies, San Francisco, United States) to capture and log the controller’s position during the experiment. The data capture was performed at the headset’s native frequency of approximately 90 Hz , using the Unity OVR Plugin package to access controller data. The virtual environment was set up over a 2.4 m x 2.4 m play-space in the x-z plane to be within the recommended manufacturer play area. This space consists of 16 commercial 600 mm square force/torque plates professionally installed on a subfloor of auto-levelling epoxy and flat to within 0.5 mm (Figure 2). The y-axis was only bounded by the camera sensors’ field of view limitations.

To ensure consistency, the Oculus Sensors were placed on the floor at 0.3 m along the front edge of the space and 1.2 m apart, equidistant from the centre line, for the entire experiment. The sensor heads were manually leveled and visually aligned to have parallel, front-facing fields of views. Both the sensors and controllers maintained an initial y-position of 0 at the floor—this would be equivalent to placing the sensors at table height and the controllers at hand height.

All measurements were taken by securing the right-hand Oculus Touch controller to a flat L-shaped jig (Figure 2) and resting it on the floor for 5 seconds. Initial calibration of floor height and play-space size and orientation was done through the official commercial Oculus setup client.

Figure 1. The right-side Oculus Touch controller. Left: front view. Right: top-down view.


Continue —>  JBME – Determining the Accuracy of Oculus Touch Controllers for Motor Rehabilitation Applications Using Quantifiable Upper Limb Kinematics: Validation Study | Shum | JMIR Biomedical Engineering

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[Abstract] Self-efficacy and Reach Performance in Individuals With Mild Motor Impairment Due to Stroke

Background: Persistent deficits in arm function are common after stroke. An improved understanding of the factors that contribute to the performance of skilled arm movements is needed. One such factor may be self-efficacy (SE).

Objective: To determine the level of SE for skilled, goal-directed reach actions in individuals with mild motor impairment after stroke and whether SE for reach performance correlated with actual reach performance.

Methods: A total of 20 individuals with chronic stroke (months poststroke: mean 58.1 ± 38.8) and mild motor impairment (upper-extremity Fugl-Meyer [FM] motor score: mean 53.2, range 39 to 66) and 6 age-matched controls reached to targets presented in 2 directions (ipsilateral, contralateral). Prior to each block (24 reach trials), individuals rated their confidence on reaching to targets accurately and quickly on a scale that ranged from 0 (not very confident) to 10 (very confident).

Results: Overall reach performance was slower and less accurate in the more-affected arm compared with both the less-affected arm and controls. SE for both reach speed and reach accuracy was lower for the more-affected arm compared with the less-affected arm. For reaches with the more-affected arm, SE for reach speed and age significantly predicted movement time to ipsilateral targets (R2 = 0.352), whereas SE for reach accuracy and FM motor score significantly predicted end point error to contralateral targets (R2 = 0.291).

Conclusions: SE relates to measures of reach control and may serve as a target for interventions to improve proximal arm control after stroke.

via Self-efficacy and Reach Performance in Individuals With Mild Motor Impairment Due to Stroke – Jill Campbell Stewart, Rebecca Lewthwaite, Janelle Rocktashel, Carolee J. Winstein, 2019

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[ARTICLE] Physiological and kinematic effects of a soft exosuit on arm movements – Full Text



Soft wearable robots (exosuits), being lightweight, ergonomic and low power-demanding, are attractive for a variety of applications, ranging from strength augmentation in industrial scenarios, to medical assistance for people with motor impairments. Understanding how these devices affect the physiology and mechanics of human movements is fundamental for quantifying their benefits and drawbacks, assessing their suitability for different applications and guiding a continuous design refinement.


We present a novel wearable exosuit for assistance/augmentation of the elbow and introduce a controller that compensates for gravitational forces acting on the limb while allowing the suit to cooperatively move with its wearer. Eight healthy subjects wore the exosuit and performed elbow movements in two conditions: with assistance from the device (powered) and without assistance (unpowered). The test included a dynamic task, to evaluate the impact of the assistance on the kinematics and dynamics of human movement, and an isometric task, to assess its influence on the onset of muscular fatigue.


Powered movements showed a low but significant degradation in accuracy and smoothness when compared to the unpowered ones. The degradation in kinematics was accompanied by an average reduction of 59.20±5.58% (mean ± standard error) of the biological torque and 64.8±7.66% drop in muscular effort when the exosuit assisted its wearer. Furthermore, an analysis of the electromyographic signals of the biceps brachii during the isometric task revealed that the exosuit delays the onset of muscular fatigue.


The study examined the effects of an exosuit on the characteristics of human movements. The suit supports most of the power needed to move and reduces the effort that the subject needs to exert to counteract gravity in a static posture, delaying the onset of muscular fatigue. We interpret the decline in kinematic performance as a technical limitation of the current device. This work suggests that a powered exosuit can be a good candidate for industrial and clinical applications, where task efficiency and hardware transparency are paramount.


In the never-ending quest to push the boundaries of their motor performance, humans have designed a wealth of wearable robotic devices. In one of the earliest recorded attempts to do so, in 1967, Mosher aspired to create a symbiotic unit that would have the “…alacrity of man’s information and control system coupled with the machine’s power and ruggedness” [1]. His design of the Hardiman, although visionary, ran into fundamental technological limitations.

Advances in materials science, electronics and energy storage have since enabled an exponential growth of the field, with state-of-the-art exoskeletons arguably accomplishing Mosher’s vision [2]. Wearable robotic technology has been successful in augmenting human strength during locomotion [3], reducing the metabolic cost of human walking [45], restoring ambulatory capabilities to paraplegic patients [6], assisting in rehabilitating stroke patients [789], harvesting energy from human movements [10] and helping to study fundamental principles underlying human motor control [1112].

These feats were achieved with machines made of rigid links of metal and capable of accurately and precisely delivering high forces to their wearer. While this is undeniably an advantage, it comes at a cost: 1) a significant inertia, which affects both the kinematics of human movement and the power requirements of the device; 2) the need for the joints of the robot to be aligned with the biological joints [13], resulting in increased mechanical complexity and size [14]; 3) a strong cosmetic impact, shown to be linked with psychological health and well-being [15].

The recent introduction of soft materials to transmit forces and torques to the human body [16] has allowed to design wearable robotic devices on the other side of the spectrum: lightweight, low-profile and compliant machines that sacrifice accuracy and magnitude of assistance for the sake of portability and svelteness.

Soft exoskeletons, or exosuits, are clothing-like devices made of fabric or elastomers that wrap around a person’s limb and work in parallel with his/her muscles [1718]. Characteristic of exosuits is that they rely on the structural integrity of the human body to transfer reaction forces between body segments, rather than having their own frame, thus acting more like external muscles than an external skeleton. Their intrinsic compliance removes the need for alignment with the joints and their low-profile allows to wear them underneath everyday clothing.

Exosuits actively transmit power to the human body either using cables, moved by electric motors, or soft pneumatic actuators, embedded in the garment. The latter paradigm was probably among the first to be proposed [19] and has been explored to assist stroke patients during walking [20], to increase shoulder mobility in subjects with neuromuscular conditions [21], to help elbow movements [22] and for rehabilitation purposes to train and aid grasping [232425].

Cable-driven exosuits, instead, include a DC motor that transmits power to the suit using Bowden cables. This flexible transmission allows to locate the actuation stage where its additional weight has the least metabolic impact on its wearer. Using this paradigm to provide assistance to the lower limbs has resulted in unprecedented levels of walking economy in healthy subjects [26] and improved symmetry and efficiency of mobility in stroke patients [27]. Similar principles were used to provide active support to hip and knee extension, reducing activation of the gluteus maximus in sit-to-stand and stand-to-sit transitions [28].

Cable-driven exosuits seem to work particularly well for lower-limbs movements, where small bursts of well-timed assistance can have a big impact on the dynamics and metabolic cost of locomotion [29]. Yet, Park et al. have shown that they have the potential for assisting the upper-limbs in quasi-static movements too: using a tendon-driving mechanism, a textile interface and an elastic component they found a significant reduction in the activity of the deltoid muscle when supporting the weight of the arm [30].

Similar results were reported by Chiaradia et al., where a soft exosuit for the elbow was shown to reduce the activation of the biceps brachii muscle in dynamic movements [31], and by Khanh et al., where the same device was used to improve the range of motion of a patient suffering from bilateral brachial plexus injury [32].

While there is extensive work on the analysis of the effects of wearing a soft exosuit on the kinematics, energetics and muscular activation during walking [33], the authors are unaware of comparable studies on movements of the upper limbs, whose variety of volitional motions is fundamentally different from the rhythmic nature of walking.

Understanding how these devices affect the physiology and mechanics of human movements is fundamental for quantifying their benefits and drawbacks, assessing their suitability for different applications and guiding a continuous data-driven design refinement.

In this study we investigate the kinematic and physiological effects of wearing a cable-driven exosuit to support elbow movements. We hypothesize that the low inertia and soft nature of the exosuit will allow it to work in parallel with the user’s muscles, delaying the onset of fatigue while having little to no impact on movement kinematics.

We propose a variation of the design and controller presented in [3234] and introduce a controller that both detects the wearer’s intention, allowing the suit to quickly shadow the user’s movements, and compensates for gravitational forces acting on the limb, thus reducing the muscular effort required for holding a static posture. We collect kinematic, dynamic and myoelectric signals from subjects wearing the device, finding that the exosuit affects motion smoothness, significantly reduces muscular effort and delays the onset of fatigue. The analysis offers interesting insights on the viability of using this technology for human augmentation/assistance and medical purposes.


Exosuit design

An exosuit is a device consisting of a frame made of soft material that wraps around the human body and transmits forces to its wearer’s skeletal structure. In a cable-driven exosuit, artificial tendons are routed along a targeted joint and attached to anchor points on both of its sides. When the tendons are tensioned they deliver an assistive moment to the joint.


Continue —>  Physiological and kinematic effects of a soft exosuit on arm movements | Journal of NeuroEngineering and Rehabilitation | Full Text

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[Abstract + References] Synergy-Based FES for Post-Stroke Rehabilitation of Upper-Limb Motor Functions


Functional electrical stimulation (FES) is capable of activating muscles that are under-recruited in neurological diseases, such as stroke. Therefore, FES provides a promising technology for assisting upper-limb motor functions in rehabilitation following stroke. However, the full benefits of FES may be limited due to lack of a systematic approach to formulate the pattern of stimulation. Our preliminary work demonstrated that it is feasible to use muscle synergy to guide the generation of FES patterns.In this paper, we present a methodology of formulating FES patterns based on muscle synergies of a normal subject using a programmable multi-channel FES device. The effectiveness of the synergy-based FES was tested in two sets of experiments. In experiment one, the instantaneous effects of FES to improve movement kinematics were tested in three patients post ischemic stroke. Patients performed frontal reaching and lateral reaching tasks, which involved coordinated movements in the elbow and shoulder joints. The FES pattern was adjusted in amplitude and time profile for each subject in each task. In experiment two, a 5-day session of intervention using synergy-based FES was delivered to another three patients, in which patients performed task-oriented training in the same reaching movements in one-hour-per-day dose. The outcome of the short-term intervention was measured by changes in Fugl–Meyer scores and movement kinematics. Results on instantaneous effects showed that FES assistance was effective to increase the peak hand velocity in both or one of the tasks. In short-term intervention, evaluations prior to and post intervention showed improvements in both Fugl–Meyer scores and movement kinematics. The muscle synergy of patients also tended to evolve towards that of the normal subject. These results provide promising evidence of benefits using synergy-based FES for upper-limb rehabilitation following stroke. This is the first step towards a clinical protocol of applying FES as therapeutic intervention in stroke rehabilitation.

I. Introduction

Muscle activation during movement is commonly disrupted due to neural injuries from stroke. A major challenge for stroke rehabilitation is to re-establish the normal ways of muscle activation through a general restoration of motor control, otherwise impairments may be compensated by the motor system through a substitution strategy of task control [1]. In post-stroke intervention, new technologies such as neuromuscular electrical stimulation (NMES) or functional electrical stimulation (FES) offer advantages for non-invasively targeting specific groups of muscles [2]–[4] to restore the pattern of muscle activation. Nevertheless, their effectiveness is limited by lack of a systematic methodology to optimize the stimulation pattern, to implement the optimal strategy in clinical settings, and to design a protocol of training towards the goal of restoring motor functions. This pioneer study addresses these issues in clinical application with a non-invasive FES technology.

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1. M. F. Levin, J. A. Kleim, and S. L. Wolf, “What do motor ‘recovery’ and ‘compensation’ mean in patients following stroke?” Neurorehabilitation Neural Repair, vol. 23, no. 4, pp. 313–319, 2008.

2. G. Alon, A. F. Levitt, and P. A. McCarthy, “Functional electrical stimulation (FES) may modify the poor prognosis of stroke survivors with severe motor loss of the upper extremity: A preliminary study,” Amer. J. Phys. Med. Rehabil., vol. 87, no. 8, pp. 627–636, 2008.

3. W. Rong, “A neuromuscular electrical stimulation (NMES) and robot hybrid system for multi-joint coordinated upper limb rehabilitation after stroke,” J. Neuroeng. Rehabil., vol. 14, no. 1, p. 34, Dec. 2017.

4. J. J. Daly, “Recovery of coordinated gait: Randomized controlled stroke trial of functional electrical stimulation (FES) versus no FES, with weight-supported treadmill and over-ground training,” Neurorehabilitation Neural Repair, vol. 25, no. 7, pp. 588–596, Sep. 2011.

5. R. Nataraj, M. L. Audu, R. F. Kirsch, and R. J. Triolo, “Comprehensive joint feedback control for standing by functional neuromuscular stimulation—A simulation study,” IEEE Trans. Neural Syst. Rehabil. Eng., vol. 18, no. 6, pp. 646–657, Dec. 2010.

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16. G. Alon, A. F. Levitt, and P. A. McCarthy, “Functional electrical stimulation enhancement of upper extremity functional recovery during stroke rehabilitation: A pilot study,” Neurorehabilitation Neural Repair, vol. 21, no. 3, pp. 207–215, Jun. 2007.

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via Synergy-Based FES for Post-Stroke Rehabilitation of Upper-Limb Motor Functions – IEEE Journals & Magazine

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[Abstract] Configuration Optimization of a Dual-Arm Rehabilitation Robot – IEEE Conference Publication


Robotic rehabilitation devices have gained high popularity in upper limb physical therapy for stroke patients. Dual-Arm rehabilitation robot system has advantages in achieving coordinated motions for the upper arm and forearm segments. In this paper, an efficient method for the design and evaluation of the kinematics of a dual-arm robot for upper limb rehabilitation, is presented. First, requirements for an upper limb rehabilitation robot are analyzed and candidate manipulator structures are presented. Then, workspace and manipulability, which are served as the criterion of the optimization configuration of a dual-arm rehabilitation robot, are analyzed. Thereafter, the optimal configuration is modeled and simulated to verify the method. Finally, simulation results are shown.

via Configuration Optimization of a Dual-Arm Rehabilitation Robot – IEEE Conference Publication


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