Posts Tagged Force

[Abstract] Assessment of the Ipsilesional Hand Function in Stroke Survivors: The Effect of Lesion Side 

Background

The aim of this study was to examine the effect of the side of brain lesion on the ipsilesional hand function of stroke survivors.

Methods

Twenty-four chronic stroke survivors, equally allocated in 2 groups according to the side of brain lesion (right or left), and 12 sex- and age-matched healthy controls performed the Jebsen-Taylor Hand Function Test (JTHFT), the Nine-Hole Peg Test (9HPT), the maximum power grip strength (PwGSmax) test, and the maximum pinch grip strength (PnGSmax) test. Only the ipsilesional hand of the stroke survivors and both hands (left and right) of the controls were assessed.

Results

PwGS max and PnGS max were similar among all tested groups. Performances in JTHFT and 9HPT were affected by the brain injury. Individuals with left brain damage showed better performance in 9HPT than individuals with right brain damage, but performance in JTHFT was similar.

Conclusions

Individuals after a brain injury have the capacity to produce maximum strength preserved when using their ipsilesional hand. However, the dexterity of their hands and digits is affected, in particular for stroke individuals with right brain lesion.

Source: Assessment of the Ipsilesional Hand Function in Stroke Survivors: The Effect of Lesion Side – Journal of Stroke and Cerebrovascular Diseases

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[Abstract] Biofeedback Signals for Robotic Rehabilitation: Assessment of Wrist Muscle Activation Patterns in Healthy Humans

Abstract:

Electrophysiological recordings from human muscles can serve as control signals for robotic rehabilitation devices. Given that many diseases affecting the human sensorimotor system are associated with abnormal patterns of muscle activation, such biofeedback can optimize human-robot interaction and ultimately enhance motor recovery. To understand how mechanical constraints and forces imposed by a robot affect muscle synergies, we mapped the muscle activity of 7 major arm muscles in healthy individuals performing goal-directed discrete wrist movements constrained by a wrist robot. We tested 6 movement directions and 4 force conditions typically experienced during robotic rehabilitation. We analyzed electromyographic (EMG) signals using a space-by-time decomposition and we identified a set of spatial and temporal modules that compactly described the EMG activity and were robust across subjects. For each trial, coefficients expressing the strength of each combination of modules and representing the underlying muscle recruitment, allowed for a highly reliable decoding of all experimental conditions. The decomposition provides compact representations of the observable muscle activation constrained by a robotic device. Results indicate that a low-dimensional control scheme incorporating EMG biofeedback could be an effective add-on for robotic rehabilitative protocols seeking to improve impaired motor function in humans.

Source: Biofeedback Signals for Robotic Rehabilitation: Assessment of Wrist Muscle Activation Patterns in Healthy Humans – IEEE Xplore Document

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[Abstract] Design of a spring-assisted exoskeleton module for wrist and hand rehabilitation

Abstract:

This paper reports on the development of a low-profile exoskeleton module to enable training of the fingers and thumb in grasp and release tasks. The design has been made as an add-on module for use with the ArmAssist arm rehabilitation system (Tecnalia, Spain). Variable-position springs and adjustable link lengths provide adaptability to fit a variety of users. Additive manufacturing has been utilized for the majority of components allowing easy modifications. A few structural components were machined from aluminum or steel to produce a functional prototype with sufficient strength for direct evaluation. The design includes independent and adjustable assistance in finger and thumb extension using various width elastic bands, and measurement of user grasp/release forces in finger flexion/extension, thumb flexion/extension, and thumb adduction/abduction using low-profile force sensitive resistors.

Source: IEEE Xplore Document – Design of a spring-assisted exoskeleton module for wrist and hand rehabilitation

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[Abstract] Impairment of muscle force transmission in spastic-paretic muscles of stroke survivors

Abstract:

Hemispheric stroke survivors tend to have persistent motor impairments, with muscle weakness and muscle spasticity observed concurrently in the affected muscles.
The objective of this preliminary study was to identify whether impairment of muscle force transmission could contribute to weakness in spastic-paretic muscles of chronic stroke survivors. To characterize the efficiency of the transmission of muscle forces to the tendon, we activated biceps brachii muscle electrically by stimulating the musculocutaneous nerve with maximum current. The ratio between the elicited maximum twitch force amplitude and the maximum M-wave peak-peak amplitude was calculated as a measure of the efficiency of force transmission.
Based on the preliminary results of two stroke survivors, we show that the Force/M-wave ratio was reduced in the affected biceps brachii muscles in comparison with the contralateral muscles, indicating a potential impairment in the muscle force transmission in the affected muscles.
Our findings suggest that disrupted muscle force transmission to the tendon could contribute to weakness in spastic muscles of chronic stroke survivors.

Source: IEEE Xplore Document – Impairment of muscle force transmission in spastic-paretic muscles of stroke survivors

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[Abstract] Benefits of using a voice and EMG-driven actuated glove to support occupational therapy for stroke survivors.

Many mechatronic devices exist to facilitate hand rehabilitation, however few directly address deficits in muscle activation patterns while also enabling functional task practice.

We developed an innovative voice and electromyography-driven actuated (VAEDA) glove, which is sufficiently flexible/portable for incorporation into hand-focused therapy post-stroke. The therapeutic benefits of this device were examined in a longitudinal intervention study. Twenty-two participants with chronic, moderate hand impairment (Chedoke-McMaster Stroke Assessment Stage of Hand (CMSA-H=4)) enrolled >8 months post-stroke for 18 one-hour training sessions (3x/week) employing a novel hand-focused occupational therapy paradigm, either with (VAEDA) or without (No-VAEDA) actuated assistance.

Outcome measures included CMSA-H, Wolf Motor Function Test (WMFT), Action Research Arm Test, Fugl-Meyer Upper Extremity Motor Assessment (FMUE), grip and pinch strength and hand kinematics. All outcomes were recorded at baseline and endpoint (immediately after and 4 weeks post-training).

Significant improvement was observed following training for some measures for the VAEDA group (n=11) but for none of the measures for the No-VAEDA group (n=11). Specifically, statistically significant gains were observed for CMSA-H (p=0.038) and WMFT (p=0.012) as well as maximum digit aperture subset (p=0.003, n=7), but not for the FMUE or grip or pinch strengths.

In conclusion, therapy effectiveness appeared to be increased by employment of the VAEDA glove, which directly targets deficits in muscle activation patterns.

Source: IEEE Xplore Abstract (Abstract) – Benefits of using a voice and EMG-driven actuated glove to support occupational therapy for stroke s…

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[Abstract] A fabric-regulated soft robotic glove with user intent detection using EMG and RFID for hand assistive application. 

Abstract

This paper presents a soft robotic glove designed to assist individuals with functional grasp pathologies in performing activities of daily living. The glove utilizes soft fabric-regulated pneumatic actuators that are low-profile and require lower pressure than previously developed actuators. They are able to support fingers and thumb motions during hand closure. Upon pressurization, the actuators are able to generate sufficient force to assist in hand closing and grasping during different manipulation tasks. In this work, experiments were conducted to evaluate the performances of the actuators as well as the glove in terms of its kinetic and kinematic assistance on a healthy participant. Additionally, surface electromyography and radio-frequency identification techniques were adopted to detect user intent to activate or deactivate the glove. Lastly, we present preliminary results of a healthy participant performing different manipulation tasks with the soft robotic glove controlled by surface electromyography and radio-frequency identification techniques.

Source: IEEE Xplore Abstract (Abstract) – A fabric-regulated soft robotic glove with user intent detection using EMG and RFID for hand assisti…

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[REVIEW] Hand synergies: Integration of robotics and neuroscience for understanding the control of biological and artificial hands

Highlights

  • Synergies have been extensively studied by neuroscientists to understand the control of multi-joint movements.
  • Synergies are thought to emerge from the interaction of neural and biomechanical factors.
  • The field of robotics has recently exploited the concept of synergies to design and build artificial hands.
  • Neuroscience has leveraged recent advances in robotics research by using novel tools and approaches to study hand control.

Abstract

The term ‘synergy’ – from the Greek synergia – means ‘working together’. The concept of multiple elements working together towards a common goal has been extensively used in neuroscience to develop theoretical frameworks, experimental approaches, and analytical techniques to understand neural control of movement, and for applications for neuro-rehabilitation. In the past decade, roboticists have successfully applied the framework of synergies to create novel design and control concepts for artificial hands, i.e., robotic hands and prostheses. At the same time, robotic research on the sensorimotor integration underlying the control and sensing of artificial hands has inspired new research approaches in neuroscience, and has provided useful instruments for novel experiments.

The ambitious goal of integrating expertise and research approaches in robotics and neuroscience to study the properties and applications of the concept of synergies is generating a number of multidisciplinary cooperative projects, among which the recently finished 4-year European project “The Hand Embodied” (THE). This paper reviews the main insights provided by this framework. Specifically, we provide an overview of neuroscientific bases of hand synergies and introduce how robotics has leveraged the insights from neuroscience for innovative design in hardware and controllers for biomedical engineering applications, including myoelectric hand prostheses, devices for haptics research, and wearable sensing of human hand kinematics. The review also emphasizes how this multidisciplinary collaboration has generated new ways to conceptualize a synergy-based approach for robotics, and provides guidelines and principles for analyzing human behavior and synthesizing artificial robotic systems based on a theory of synergies.

Source: Hand synergies: Integration of robotics and neuroscience for understanding the control of biological and artificial hands

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[Abstract] Hand synergies: Integration of robotics and neuroscience for understanding the control of biological and artificial hands

Highlights

Cover imageSynergies have been extensively studied by neuroscientists to understand the control of movements involving multiple muscles and joints.

The overarching theoretical framework underlying the concept of synergies is that they emerge from the interaction of neural and biomechanical factors. This interaction leads to a reduction in the number of independent degrees of freedom to be controlled.

The field of robotics has recently exploited the concept of synergies to design and build artificial systems, in particular artificial hands, and reduce the complexity associated with controlling a large number of degrees of freedom.

Neuroscience has leveraged recent advances in robotics research by using novel tools and methodological approaches to study how the central nervous system integrates sensory feedback with motor commands for hand control.


Abstract

The term ‘synergy’ – from the Greek synergia – means ‘working together’. The concept of multiple elements working together towards a common goal has been extensively used in neuroscience to develop theoretical frameworks, experimental approaches, and analytical techniques to understand neural control of movement, and for applications for neuro-rehabilitation. In the past decade, roboticists have successfully applied the framework of synergies to create novel design and control concepts for artificial hands, i.e., robotic hands and prostheses. At the same time, robotic research on the sensorimotor integration underlying the control and sensing of artificial hands has inspired new research approaches in neuroscience, and has provided useful instruments for novel experiments.

The ambitious goal of integrating expertise and research approaches in robotics and neuroscience to study the properties and applications of the concept of synergies is generating a number of multidisciplinary cooperative projects, among which the recently finished 4-year European project “The Hand Embodied” (THE). This paper reviews the main insights provided by this framework. Specifically, we provide an overview of neuroscientific bases of hand synergies and introduce how robotics has leveraged the insights from neuroscience for innovative design in hardware and controllers for biomedical engineering applications, including myoelectric hand prostheses, devices for haptics research, and wearable sensing of human hand kinematics. The review also emphasizes how this multidisciplinary collaboration has generated new ways to conceptualize a synergy-based approach for robotics, and provides guidelines and principles for analyzing human behavior and synthesizing artificial robotic systems based on a theory of synergies.

 

Source: Hand synergies: Integration of robotics and neuroscience for understanding the control of biological and artificial hands

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[ARTICLE] Reinforcement learning neural network (RLNN) based adaptive control of fine hand motion rehabilitation robot.

Recent neural science research suggests that a robotic device can be an effective tool to deliver the repetitive movement training that is needed to trigger neuroplasticity in the brain following neurologic injuries such as stroke and spinal cord injury (SCI).

In such scenario, adaptive control of the robotic device to provide assistance as needed along the intended motion trajectory with exact amount of force intensity, though complex, is a more effective approach. A critic-actor based reinforcement learning neural network (RLNN) control method is explored to provide adaptive control during post-stroke fine hand motion rehabilitation training.

The effectiveness of the method is verified through computer simulation and implementation on a hand rehabilitation robotic device.

Results suggest that the control system can fulfil the assist-as-needed (AAN) control with high performance and reliability. The method demonstrates potential to encourage active participation of the patient in the rehabilitation process and to improve the efficiency of the process.

Source: IEEE Xplore Abstract (Abstract) – Reinforcement learning neural network (RLNN) based adaptive control of fine hand motion rehabilitati…

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[ARTICLE] A novel method for the quantification of key components of manual dexterity after stroke – Full Text HTML

Abstract

Background

A high degree of manual dexterity is a central feature of the human upper limb. A rich interplay of sensory and motor components in the hand and fingers allows for independent control of fingers in terms of timing, kinematics and force. Stroke often leads to impaired hand function and decreased manual dexterity, limiting activities of daily living and impacting quality of life. Clinically, there is a lack of quantitative multi-dimensional measures of manual dexterity. We therefore developed the Finger Force Manipulandum (FFM), which allows quantification of key components of manual dexterity. The purpose of this study was (i) to test the feasibility of using the FFM to measure key components of manual dexterity in hemiparetic stroke patients, (ii) to compare differences in dexterity components between stroke patients and controls, and (iii) to describe individual profiles of dexterity components in stroke patients.

Methods

10 stroke patients with mild-to-moderate hemiparesis and 10 healthy subjects were recruited. Clinical measures of hand function included the Action Research Arm Test and the Moberg Pick-Up Test. Four FFM tasks were used: (1) Finger Force Tracking to measure force control, (2) Sequential Finger Tapping to measure the ability to perform motor sequences, (3) Single Finger Tapping to measure timing effects, and (4) Multi-Finger Tapping to measure the ability to selectively move fingers in specified combinations (independence of finger movements).

Results

Most stroke patients could perform the tracking task, as well as the single and multi-finger tapping tasks. However, only four patients performed the sequence task. Patients showed less accurate force control, reduced tapping rate, and reduced independence of finger movements compared to controls. Unwanted (erroneous) finger taps and overflow to non-tapping fingers were increased in patients. Dexterity components were not systematically related among each other, resulting in individually different profiles of deficient dexterity. Some of the FFM measures correlated with clinical scores.

Conclusions

Quantifying some of the key components of manual dexterity with the FFM is feasible in moderately affected hemiparetic patients. The FFM can detect group differences and individual profiles of deficient dexterity. The FFM is a promising tool for the measurement of key components of manual dexterity after stroke and could allow improved targeting of motor rehabilitation.

Continue —>  JNER | Full text | A novel method for the quantification of key components of manual dexterity after stroke.

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