Posts Tagged robot

[ARTICLE] A comparison of the rehabilitation effectiveness of neuromuscular electrical stimulation robotic hand training and pure robotic hand training after stroke: A randomized controlled trial – Full Text

Highlights

The rehabilitation effects of the NMES robotic hand and robotic hand were compared.

Both training systems could significantly improve the motor function of upper limb.

The NMES robot was more effective than the pure robot.

NMES applied on distal muscle could benefit the recovery in the entire upper limb.

 

Abstract

Objective

To compare the rehabilitation effects of the electromyography (EMG)-driven neuromuscular electrical stimulation (NMES) robotic hand and EMG-driven robotic hand for chronic stroke.

Methods

This study was a randomized controlled trial with a 3-month follow-up. Thirty chronic stroke patients were randomly assigned to receive 20-session upper limb training with either EMG-driven NMES robotic hand (NMES group, n = 15) or EMG-driven robotic hand (pure group, n = 15). The training effects were evaluated before and after the training, as well as 3 months later, using the clinical scores of Fugl-Meyer Assessment (FMA), Modified Ashworth Scale (MAS), Action Research Arm Test (ARAT), and Functional Independence Measure (FIM). Session-by-session EMG parameters, including the normalized EMG activation level and co-contraction indexes (CIs) of the target muscles were applied to monitor the recovery progress in muscular coordination patterns.

Results

Both groups achieved significantly increased FMA and ARAT scores (p < 0.05), and the NMES group improved more (p < 0.05). A significant improvement in MAS was obtained in the NMES group (p < 0.05) but absence in the pure group. Meanwhile, better performance could be obtained in the NMES group in releasing the EMG activation levels and CIs than the pure group across the training sessions (p < 0.05).

Conclusion

Both training systems were effective in improving the long-term distal motor functions in upper limb, where the NMES robot-assisted training achieved better voluntary motor recovery and muscle coordination and more release in muscle spasticity.

Significance

This study indicated more effective distal rehabilitation using the NMES robot than the pure robot-assisted rehabilitation.

1. Introduction

Upper limb motor deficits are common after stroke, and observed in over 80% of stroke survivors [1,2]. Various rehabilitation devices have been purposed to assist human physical therapists to provide effective long-term rehabilitation programs [[3][4][5]]. Among them, rehabilitation robots and neuromuscular electrical stimulation (NMES) are most widely used in stroke rehabilitation practices. Rehabilitation robots have been recognized as efficient in such cases and could represent a cost-effective addition to conventional rehabilitation services because they provide highly intensive and repetitive training [[6][7][8][9]]. It has been reported that the integration of voluntary effort (e.g. electromyography, EMG) into robotic design could contribute significantly to motor recovery in stroke patients [6,10]. This is because an EMG-driven strategy can maximize the involvement of voluntary effort in the training, and its effectiveness at improving upper limb voluntary motor functions have been proved by many EMG-driven robot-assisted upper-limb training systems [[11][12][13]]. However, rehabilitation robots are unable to directly activate the desired muscle groups, which may only assist, or even dominate limb movement such as continuous passive motions (CPM) [14]. In addition, stroke patients usually cooperate with compensatory motions from other muscular activities to activate the target muscles, which may lead to ‘learned disuse’ [15]. However, NMES can effectively limit compensatory motions by stimulating specific muscles via cyclic electrical currents, which provides repetitive sensorimotor experiences [16]. With the advantage of precisely activating the target muscle, NMES has been reported to be effective in evoking sensory feedback, improving muscle force, and thus promoting motor function in stroke patients [17,18]. Nevertheless, training programs assisted by NMES alone are also suboptimal due to the difficulty of controlling movement trajectories and the early appearance of fatigue [19,20].

Accordingly, various NMES robot-assisted upper-limb training programs which combine these two unique techniques have been proposed to integrate the benefits and minimize the disadvantages [7,12,14,21,22]. The rehabilitation effectiveness of these combined systems has been investigated and reported to be effective in improving motor recovery. Several studies have compared the training outcomes of NMES robot-assisted training and other training programs. For example, Qian et al. [22] reported that NMES-robot-assisted upper-limb training could achieve better motor outcomes when compared with conventional therapies for subacute stroke patients. Meanwhile, another study which compared the training effects between robot-aided training with NMES and robot-aided training solely using the InMotion ARM™ Robot in the subacute period demonstrated that the active ranges of motion of the NMES robot-training group were significantly higher compared with the robot-training group [23]. Coincidentally, investigations into applications in chronic stroke patients have also been carried out. For instance, Hu et al. [14] proposed an EMG-driven NMES robot system for wrist training; this combined device improved muscle activation levels related to the wrist and reduced compensatory muscular activities at the elbow, while these training outcomes were absent for the EMG-driven robot-assisted training alone. Indeed, a similar study by another research group also achieved better rehabilitation outcomes on some clinical assessments using the combined system compared to robot-assisted therapy alone [21].

In the literature, most studies on current rehabilitation devices combining the NMES and robotic systems targeted the elbow and wrist joints [7,[21][22][23]], while very few focused on the hand and fingers [24]. In addition, a comparison of the training effects for hand rehabilitation between the NMES robot and other hand rehabilitation devices has not yet been adequately conducted. Indeed, the primary upper-limb disability post-stroke is the loss of hand function, and rehabilitation of the distal joints after stroke is much more difficult than the motor recovery of the proximal joints due to the compensatory motions from the proximal joints [25]. Hence, developing effective rehabilitation devices to minimize compensatory movements for hand motor recovery is especially meaningful for stroke rehabilitation. In our previous work, we developed an EMG-driven NMES robotic hand and suggested it for use in hand rehabilitation after stroke [26]. Our device provides fine control of hand movements and activates the target muscles selectively for finger extension/flexion, and its feasibility and effectiveness have been verified by a single group trial [12]. However, whether the long-term rehabilitation effect of this EMG-driven NMES robotic hand is comparable or even better than other hand rehabilitation devices are still unclear and need to be investigated quantitively. Therefore, the objective of this study is to compare the training effects of hand rehabilitation assisted by an NMES robotic hand and by a pure robotic hand though a randomized controlled trial with a 3-month follow-up (3MFU).

2. Methodology

2.1. Participants

This work was approved by the Human Subjects Ethics Sub-Committee of the Hong Kong Polytechnic University. A total of 53 stroke survivors were screened for the training from local districts. 30 participants with chronic stroke satisfied the following inclusion criteria: (1) The participants were at least 6 months after the onset of a singular and unilateral brain lesion due to stroke, (2) both the metacarpophalangeal (MCP) and proximal interphalangeal (PIP) joints could be extended to 180° passively, (3) muscle spasticity during extension at the finger joints and the wrist joint was below 3 as measured by the Modified Ashworth Scale (MAS) [27], ranged from 0 (no increase in muscle tone) to 4 (affected part rigid), (4) detectable voluntary EMG signals from the driving muscle on the affected side (three times of the standard deviation (SD) above the EMG baseline), and (5) no visual deficit and able to understand and follow simple instructions as assessed by the Mini-Mental State Examination (MMSE > 21) [28].

This work involved a randomized controlled trial with a 3-month follow-up (3MFU). The potential participants were first told that the training program they would receive could be either NMES robotic hand training or pure robotic hand training, and all recruited participants submitted their written consent before randomization. Then, the recruited participants were randomly assigned into two groups according to a computer-based random number generator, i.e., the computer program generated either “1” (denoting the NMES robotic hand training group) or “2” (the pure robotic hand group) with an equal probability of 0.5 (Matlab, 2017, Mathworks, Inc.). Fig. 1 shows the Consolidated Standards of Reporting Trials flowchart of the training program.

Fig. 1

Fig. 1. The consolidated standards of reporting trials flowchart of the experimental design.

2.2. Interventions

For both groups, each participant was invited to attend a 20-session robotic hand training with an intensity of 3–5 sessions/week, completed within 7 consecutive weeks. The training setup of both groups is shown in Fig. 2. This robotic hand training system can assist with finger extension and flexion of the paretic limb for patients after stroke. In this work, real-time voluntary EMG detected from the abductor pollicis brevis (APB) and extensor digitorum (ED) muscles were used to control the respective hand closing and opening movements, and the threshold level of each motion phase was set at three times the SD above the EMG baseline at resting state [12]. For example, during the motions of finger flexion, once the EMG activation level of the APB muscle reached a preset threshold, the robotic hand would provide mechanical assistance for hand closing. Similarly, during the motions of finger extension, the robotic hand would assist with hand opening when the EMG activation level of the ED muscle reached a preset threshold. For the NMES robot group, synchronized support from the NMES and the robot were both provided. The NMES electrode pair (30 mm diameter; Axelgaard Corp., Fallbrook, CA, USA) was attached over the ED muscle to provide stimulation during finger extension. The outputs of NMES were square pulses with a constant amplitude of 70 V, a stimulation frequency of 40 Hz, and a manually adjustable pulse width in the range 0–300 μs. Before the training, the pulse width was set at the minimum intensity, which achieved a fully extended position of the fingers in each patient. During the training, NMES would be triggered by the EMG from the ED muscle first and then provided stimulation to the ED muscle to assist hand-opening motions for the entire phase of finger extension, while no assistance from NMES was provided during finger flexion to avoid the possible increase of finger spasticity after stimulation [29]. For the pure robot group, the difference between the two groups was that no NMES was applied in the pure robot group. A detailed account of the working principles of the robotic hand have been described in our previous work [12,30,31].

Fig. 2

Fig. 2. The experimental setup of the robotic hand training: (A) pure robotic hand group; (B) neuromuscular electrical stimulation (NMES) robotic hand group.

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[Abstract] Robot-Assisted Arm Training in Chronic Stroke: Addition of Transition-to-Task Practice

Abstract

Background. Robot-assisted therapy provides high-intensity arm rehabilitation that can significantly reduce stroke-related upper extremity (UE) deficits. Motor improvement has been shown at the joints trained, but generalization to real-world function has not been profound.

Objective. To investigate the efficacy of robot-assisted therapy combined with therapist-assisted task training versus robot-assisted therapy alone on motor outcomes and use in participants with moderate to severe chronic stroke-related arm disability.

Methods. This was a single-blind randomized controlled trial of two 12-week robot-assisted interventions; 45 participants were stratified by Fugl-Meyer (FMA) impairment (mean 21 ± 1.36) to 60 minutes of robot therapy (RT; n = 22) or 45 minutes of RT combined with 15 minutes therapist-assisted transition-to-task training (TTT; n = 23). The primary outcome was the mean FMA change at week 12 using a linear mixed-model analysis. A subanalysis included the Wolf Motor Function Test (WMFT) and Stroke Impact Scale (SIS), with significance P <.05.

Results. There was no significant 12-week difference in FMA change between groups, and mean FMA gains were 2.87 ± 0.70 and 4.81 ± 0.68 for RT and TTT, respectively. TTT had greater 12-week secondary outcome improvements in the log WMFT (-0.52 ± 0.06 vs -0.18 ± 0.06; P = .01) and SIS hand (20.52 ± 2.94 vs 8.27 ± 3.03; P = .03).

Conclusion. Chronic UE motor deficits are responsive to intensive robot-assisted therapy of 45 or 60 minutes per session duration. The replacement of part of the robotic training with nonrobotic tasks did not reduce treatment effect and may benefit stroke-affected hand use and motor task performance.

 

via Robot-Assisted Arm Training in Chronic Stroke: Addition of Transition-to-Task Practice. – PubMed – NCBI

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[Systematic Review] Exoskeletons With Virtual Reality, Augmented Reality, and Gamification for Stroke Patients’ Rehabilitation: Systematic Review – Full Text

ABSTRACT

Background: Robot-assisted therapy has become a promising technology in the field of rehabilitation for poststroke patients with motor disorders. Motivation during the rehabilitation process is a top priority for most stroke survivors. With current advancements in technology there has been the introduction of virtual reality (VR), augmented reality (AR), customizable games, or a combination thereof, that aid robotic therapy in retaining, or increasing the interests of, patients so they keep performing their exercises. However, there are gaps in the evidence regarding the transition from clinical rehabilitation to home-based therapy which calls for an updated synthesis of the literature that showcases this trend. The present review proposes a categorization of these studies according to technologies used, and details research in both upper limb and lower limb applications.

Objective: The goal of this work was to review the practices and technologies implemented in the rehabilitation of poststroke patients. It aims to assess the effectiveness of exoskeleton robotics in conjunction with any of the three technologies (VR, AR, or gamification) in improving activity and participation in poststroke survivors.

Methods: A systematic search of the literature on exoskeleton robotics applied with any of the three technologies of interest (VR, AR, or gamification) was performed in the following databases: MEDLINE, EMBASE, Science Direct & The Cochrane Library. Exoskeleton-based studies that did not include any VR, AR or gamification elements were excluded, but publications from the years 2010 to 2017 were included. Results in the form of improvements in the patients’ condition were also recorded and taken into consideration in determining the effectiveness of any of the therapies on the patients.

Results: Thirty studies were identified based on the inclusion criteria, and this included randomized controlled trials as well as exploratory research pieces. There were a total of about 385 participants across the various studies. The use of technologies such as VR-, AR-, or gamification-based exoskeletons could fill the transition from the clinic to a home-based setting. Our analysis showed that there were general improvements in the motor function of patients using the novel interfacing techniques with exoskeletons. This categorization of studies helps with understanding the scope of rehabilitation therapies that can be successfully arranged for home-based rehabilitation.

Conclusions: Future studies are necessary to explore various types of customizable games required to retain or increase the motivation of patients going through the individual therapies.

Introduction

Background

Stroke refers to a sudden, often catastrophic neurological event that can lead to long-term adult disability. The American Heart Association (AHA) is responsible for providing up-to-date statistics related to heart disease and stroke. According to Benjamin et al [1], the AHA released a 2017 statistics report on heart disease and stroke that stated that approximately 795,000 stroke episodes occur in the US each year. With current advancements in medical technology there has been a decrease in the rate of stroke incidents, but it can still cause paralysis and muscle weakness. Such impairments can result in motor deficits that disturb a stroke survivor’s capacity to live independently.

There are several reasons for stroke occurrence, which could be related to an increased risk of a collection of symptoms caused by disorders affecting the brain (eg, dementia) [2]. Various rehabilitation techniques have been used in the area of rehabilitation-based interactive technology to assist patients in recovering from impairments, and those techniques come under the umbrella of conventional therapy, exoskeleton or robot-aided therapy, virtual reality (VR) or augmented reality (AR) therapy, games-based therapy, or a combination of any of these. These forms of therapy can be done either in the clinic or in an in-home setting. In addition to these, there is a new technology known as telerehabilitation [3] that leverages the use of VR in home settings by providing patients access to real-time rehabilitation services through the internet while they sit at home.

One of the most effective techniques is robot-aided therapy, which has been gradually increasing in use primarily because patients may consider traditional rehabilitation therapy to be tiring and exhaustive. This may decrease their motivation and cohesion to the treatment, thus resulting in only minor improvement in the health of poststroke patients [46]. Various experimental evidence suggests that robot-assisted (or exoskeleton) rehabilitation has been effective in keeping patients motivated and interested in treatment for both upper or lower limb impairments [7,8]. With advancements in technology, there has also been an uptake of VR, AR, and Gamification for the purposes of rehabilitation [9], along with robotic rehabilitation [10,11], primarily to increase engagement, immersion and motivation on behalf of the patient. Both Colombo et al and Alankus et al [12,13] concluded and showed the positive effect of exoskeleton robots and games in poststroke rehabilitation. Wearable devices such as exoskeletons can also relay real-time feedback for any VR-based interactions [14].

Apart from these studies, Housman et al [15] showed user satisfaction survey results in which 90% of participants agreed to the fact that robot- or games-assisted therapies were less confusing, and improvements were very easy to track compared to traditional or conventional therapies. Further, it is thought that gamification can increase repetition, engagement, and range of care within the context of rehabilitation [16,17]. Games are not only useful for the field of rehabilitation, but they are also considered to be highly impactful and relevant in other medical and health fields. Russoniello et al [18] conducted a randomized controlled trial (RCT) study in which the effects of video games on stress-related disorders were tested, with the conclusion being that games were beneficial for their prevention and treatment. In another study, children who had cerebral palsy made use of a game (EyeToy) which was able to improve their upper extremity functions over time [19].

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Continue —->  JRAT – Exoskeletons With Virtual Reality, Augmented Reality, and Gamification for Stroke Patients’ Rehabilitation: Systematic Review | Mubin | JMIR Rehabilitation and Assistive Technologies

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[ARTICLE] Methods for an Investigation of Neurophysiological and Kinematic Predictors of Response to Upper Extremity Repetitive Task Practice in Chronic Stroke – Full Text PDF

Abstract

Objective

To demonstrate the feasibility of algorithmic prediction utilizing a model of baseline arm movement, genetic factors, demographic characteristics, and multi-modal assessment of the structure and function of motor pathways. To identify prognostic factors and the biological substrate for reductions in arm impairment in response to repetitive task practice.

Design

This prospective single-group interventional study seeks to predict response to a repetitive task practice program using an intent-to-treat paradigm. Response is measured as a change of ≥5 points on the Upper Extremity Fugl-Meyer from baseline to final evaluation (at the end of training).

Setting

General community

Participants

Anticipated enrollment of 96 community-dwelling adults with chronic stroke (onset ≥6 months) and moderate to severe residual hemiparesis of the upper limb as defined by a score of 10-45 points on the Upper Extremity Fugl-Meyer.

Intervention

The intervention is a form of repetitive task practice using a combination of robot-assisted therapy coupled with functional arm use in real-world tasks administered over 12 weeks.

Main outcome measures

Upper extremity Fugl-Meyer Assessment (primary outcome), Wolf Motor Function Test, Action Research Arm Test, Stroke Impact Scale, questionnaires on pain and expectancy, magnetic resonance imaging, transcranial magnetic stimulation, arm kinematics, accelerometry, and a saliva sample for genetic testing.

Results

Methods for this trial are outlined and an illustration of inter-individual variability is provided by example of two participants who present similarly at baseline but achieve markedly different outcomes.

Conclusion

This article presents the design, methodology, and rationale of an ongoing study to develop a predictive model of response to a standardized therapy for stroke survivors with chronic hemiparesis. Applying concepts from precision medicine to neurorehabilitation is practicable and needed to establish realistic rehabilitation goals and to effectively allocate resources.

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

Abstract

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 + References] A Soft Robotic Glove for Hand Rehabilitation Using Pneumatic Actuators with Variable Stiffness – Conference paper

Abstract

Traditional rigid robots exist many problems in rehabilitation training. Soft robotics is conducive to breaking the limitations of rigid robots. This paper presents a soft Rehabilitation training, Soft robot, Pneumatic actuator device for the rehabilitation of hands, including soft pneumatic actuators that are embedded in the device for motion assistance. The key feature of this design is the stiffness of each actuator at different positions is different, which results in the bending posture of the actuator is more accordant with the bending figure of human hand. In addition, another key point is the use of a fabric sleeves allow actuators to gain greater bending force when pressurized, which gives the hand greater bending force. We verified the feasibility of actuator through simulation, the performance of soft actuator and the device also are evaluated through experiments. Finally, the results show that this device can finish some of the hand rehabilitation tasks.

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via A Soft Robotic Glove for Hand Rehabilitation Using Pneumatic Actuators with Variable Stiffness | SpringerLink

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[Abstract] Robot-Assisted Arm Training in Chronic Stroke: Addition of Transition-to-Task Practice

Background. Robot-assisted therapy provides high-intensity arm rehabilitation that can significantly reduce stroke-related upper extremity (UE) deficits. Motor improvement has been shown at the joints trained, but generalization to real-world function has not been profound.

Objective. To investigate the efficacy of robot-assisted therapy combined with therapist-assisted task training versus robot-assisted therapy alone on motor outcomes and use in participants with moderate to severe chronic stroke-related arm disability.

Methods. This was a single-blind randomized controlled trial of two 12-week robot-assisted interventions; 45 participants were stratified by Fugl-Meyer (FMA) impairment (mean 21 ± 1.36) to 60 minutes of robot therapy (RT; n = 22) or 45 minutes of RT combined with 15 minutes therapist-assisted transition-to-task training (TTT; n = 23). The primary outcome was the mean FMA change at week 12 using a linear mixed-model analysis. A subanalysis included the Wolf Motor Function Test (WMFT) and Stroke Impact Scale (SIS), with significance P<.05.

Results. There was no significant 12-week difference in FMA change between groups, and mean FMA gains were 2.87 ± 0.70 and 4.81 ± 0.68 for RT and TTT, respectively. TTT had greater 12-week secondary outcome improvements in the log WMFT (−0.52 ± 0.06 vs −0.18 ± 0.06; P = .01) and SIS hand (20.52 ± 2.94 vs 8.27 ± 3.03; P = .03).

Conclusion. Chronic UE motor deficits are responsive to intensive robot-assisted therapy of 45 or 60 minutes per session duration. The replacement of part of the robotic training with nonrobotic tasks did not reduce treatment effect and may benefit stroke-affected hand use and motor task performance.

 

via Robot-Assisted Arm Training in Chronic Stroke: Addition of Transition-to-Task Practice – Susan S. Conroy, George F. Wittenberg, Hermano I. Krebs, Min Zhan, Christopher T. Bever, Jill Whitall,

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[WEB SITE] Will a robot look after you one day?

This is not a theoretical question. Robots are already widely used as “carers” in Japan, while the UK and other Western countries have rapidly ageing populations. In the UK, an average of 900 care workers per day are leaving the profession due to low wages and tough conditions and there is a severe lack of new care workers.

Enter the robots. The use of robotics and other technologies could help to alleviate these ever-increasing pressures by reducing staffing costs and relieving human carers of the physically demanding or more menial tasks, freeing up their time for face-to-face care of patients. Japan’s robot strategy claims that: “Robots will help release humans from cumbersome tasks and enrich interaction for a higher quality of life than ever.”

The potential is huge, and exciting. Robots, or robotic devices, can provide three types of assistance: physical, social and cognitive.

Physical assistance
One of the most strenuous tasks for carers, which they must do regularly, is lifting a patient from a bed into a wheelchair, which can quickly become a cause of lower back pain. ‘Robear’ is an experimental bear-shaped robot that lifts and carries elderly or frail patients from beds into wheelchairs, or into the bath.

‘Stevie’ is designed to look a bit (but not too much) like a human, with arms and a head but also wheels. This helps people realise that they can speak to it and perhaps ask it to do things for them, such as reminding them to take medication or turn off an oven. A room sensor on ‘Stevie’ can detect if someone has fallen over and a human operator can then take control of it to investigate the event and perhaps contact emergency services.  ‘Stevie’ can also regulate room temperatures and light levels to help to keep the user comfortable.

Then there is ‘Rex’, a robot for rehabilitation that can help people with multiple sclerosis or other neurological conditions to stand and to walk .

These are just examples of how robots can enable people to stay in their homes for longer rather than going into residential care. They can prevent hospitalisation through falls and can help keep people healthier for longer. And by doing the more mundane tasks of caring, robots can reduce social care costs and free up staff to do more personal caring.

‘Stevie’ deliberately only has a few human-like features, however ‘Chihira’ the robot very closely resembles a Japanese woman. ‘Chihira’ has been developed to do physical work with elderly people with conditions including dementia, but its human likeness is unsettling and raises interesting questions. Do all patients know they are dealing with a robot? Does the human likeness mean dementia patients are being deceived by a machine in some way? Or will it help dementia patients feel they have real company and help – a type of social assistance?

Social assistance
Robots can do more than simply detect and prevent falls, they can provide companionship and social engagement, monitor and improve wellbeing, or even help educate preschool children.

Paro’ a fluffy white robot seal is being integrated into care homes in the UK as a therapeutic intervention for people with dementia and learning disabilities. Pet therapy is widely used, so the idea of a robot pet is an obvious step. Research has shown that ‘Paro’ lessens stress and anxiety, promotes social interaction, facilitates emotional expression and improves mood and speech fluency.

Pepper’ is a widely used humanoid robot able to communicate at a very basic level with simple gestures. Cameras on ‘Pepper’ have shape-recognition software and microphones, allowing ‘Pepper’ to decipher voice tones and expressions in order to determine if people are happy. So in a residential home ‘Pepper’ can patrol around and seek out people to talk to. ‘Pepper’ has been trialled in Southend care homes already.

MiRo’ is another ‘pet’ robot, this time resembling a rabbit or small dog, specifically designed to engage emotionally with people, to combat loneliness and to offer reminders for tasks such as taking medicine.  The ‘Giraff ‘ robot developed at the University of Lincoln monitors the health of elderly people living alone, or those with dementia, and allows them to have contact through a screen with carers or family and friends. It has been trialled in isolated communities and homes in Scotland.

Some robots, with human features, have been designed to help children with autism who find it difficult to read emotions and interpret behaviour, by helping them to socialise and communicate. Probably best known is the child-sized ‘Kaspar’ robot, helping children to understand which tactile behaviours are socially acceptable and which are more inappropriate. ‘Kaspar’ effectively provides basic cognitive assistance.

Cognitive assistance
A doll-type robot, nodding ‘Kabochan’, has been found to improve users’ cognitive function and mental health in research trials in Japan.  Virtual robots have been successfully used to assess the cognitive abilities of children while ‘Zora’ has been shown to help the cognitive and communication skills of children with severe physical disabilities.

The benefits of robots are undoubtedly many and the push for their use in social and nursing care is powerful, but what are the costs?

Counting the cost
Currently, robots are expensive, which may present a practical barrier to their wider use in social care. However, they are still cheaper than people and can work 24 hours a day without contracts or complaining, and the outlay costs will fall over time.

So, what are other costs?  We can still only guess at this stage but here are some questions that need asking:

  • Will robots give us an excuse to palm off human care and interaction to machines? Will they make the next generation of older people more independent or more isolated?
  • Will the quality of social care diminish, or can robots fulfil the social and emotional needs of vulnerable care recipients?
  • Does mimicking the human form and actions deceive or help the elderly or patients with dementia? Or children with autism?
  • Will robots like Roho in Japan take over aspects of childcare? What do we think of our children making ‘friends’ with robot humanoids or robot pets?
  • What about autonomy, privacy, security and legal and regulatory concerns (such as the legal liability for decisions made by robots)? Or the risk of malicious hacking or cyber-attacks?
  • Who controls robots? What if a manufacturer or user (or a technical support engineer) creates or changes settings that move the robot’s behaviours outside of an ‘ethical envelope’?
  • What is the ethical governance of robots and their actions and purpose? Is any needed? Could we even create ‘ethical’ robots if we wanted?

Underneath all these questions lie the bigger ones on human dignity, care and community. Robots of the future will undoubtedly be able to perform many of the more basic, tedious and strenuous physical tasks currently done by humans. But human intervention will continue to be fundamental. There will always be a need for the calm, reassuring, empathetic, compassionate and caring bedside interactions and decision-making of professional humans, particularly when it comes to caring for patients, young and old, with complex needs. The high degree of social and emotional human intelligence is still beyond machines, and always will be.

The designer of ‘Stevie’ says:

None of this will mean we won’t need human carers anymore. Stevie won’t be able to wash or dress people, for example. Instead, we’re trying to develop technology that helps and complements human care. We want to combine human empathy, compassion and decision-making with the efficiency, reliability and continuous operation of robotics.’

Right now, the extent to which robotic innovations will assist or replace humans in the future remains unknown.

However, we must not allow inauthentic relationships with robots to replace human relationships or undermine human value or dignity in any way. The Bible is clear that we are made for relationships, first to God through Jesus Christ and then to each otherThe Bible teaches human interdependence.  A machine can never meet the emotional and spiritual relational needs of elderly citizens, patients or children, nor indeed any human.

It may seem obvious, but robots are not humans and humans are not robots. While robots may bring many benefits, any blurring of either of these boundaries may dangerously diminish the dignity and value that being made in God’s Image gives to every human being. We need to tread the path ahead carefully and wisely.

Philippa Taylor is Head of Public Policy at CMF. She has an MA in Bioethics from St Mary’s University College and a background in policy work on bioethics and family issues. Republished from the CMF blog with permission.

 

via Will a robot look after you one day?

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[ARTICLE] Influences of the biofeedback content on robotic post-stroke gait rehabilitation: electromyographic vs joint torque biofeedback – Full Text

Abstract

Background

Add-on robot-mediated therapy has proven to be more effective than conventional therapy alone in post-stroke gait rehabilitation. Such robot-mediated interventions routinely use also visual biofeedback tools. A better understanding of biofeedback content effects when used for robotic locomotor training may improve the rehabilitation process and outcomes.

Methods

This randomized cross-over pilot trial aimed to address the possible impact of different biofeedback contents on patients’ performance and experience during Lokomat training, by comparing a novel biofeedback based on online biological electromyographic information (EMGb) versus the commercial joint torque biofeedback (Rb) in sub-acute non ambulatory patients.

12 patients were randomized into two treatment groups, A and B, based on two different biofeedback training. For both groups, study protocol consisted of 12 Lokomat sessions, 6 for each biofeedback condition, 40 min each, 3 sessions per week of frequency. All patients performed Lokomat trainings as an add-on therapy to the conventional one that was the same for both groups and consisted of 40 min per day, 5 days per week. The primary outcome was the Modified Ashworth Spasticity Scale, and secondary outcomes included clinical, neurological, mechanical, and personal experience variables collected before and after each biofeedback training.

Results

Lokomat training significantly improved gait/daily living activity independence and trunk control, nevertheless, different effects due to biofeedback content were remarked. EMGb was more effective to reduce spasticity and improve muscle force at the ankle, knee and hip joints. Robot data suggest that Rb induces more adaptation to robotic movements than EMGb. Furthermore, Rb was perceived less demanding than EMGb, even though patient motivation was higher for EMGb. Robot was perceived to be effective, easy to use, reliable and safe: acceptability was rated as very high by all patients.

Conclusions

Specific effects can be related to biofeedback content: when muscular-based information is used, a more direct effect on lower limb spasticity and muscle activity is evidenced. In a similar manner, when biofeedback treatment is based on joint torque data, a higher patient compliance effect in terms of force exerted is achieved. Subjects who underwent EMGb seemed to be more motivated than those treated with Rb.

Background

Stroke is the leading cause of acquired disability throughout the world, with increasing survival rates as medical care and treatment techniques improve [1]. Post-stroke disability often affects mobility, balance, and walking [2]. The majority of stroke survivors rank walking recovery among their top rehabilitation goals [3,4,5]. Furthermore, the ability to walk is one of the most important determining factors for returning home after stroke [4].

Recovery of walking mainly occurs within the first 11 weeks after a stroke [6]; indeed, further recovery after that time is rare [7]. Overall, between 30 and 40% of stroke survivors are not able to regain a functional gait after rehabilitation [48]. These data have stimulated advances in many different innovative technological approaches to improve the gait rehabilitation efficacy.

Modern concepts favour task-specific repetitive rehabilitation approaches [9], with high intensity [10] and early multisensory stimulation [11]. These requirements are met by robot assisted gait training (RAGT) approaches. Recent studies on stroke patients have reported that when conventional therapy and RAGT are combined, compared to conventional therapy alone, gait recovery significantly improves [12] and patients are more likely to recover independent walking [13]. In particular, non-ambulatory patients in the sub-acute phase are the group most likely to benefit from this type of training [13].

This high interest in robotic therapy has attracted attention to human robot interactions in the rehabilitation framework, and a consensus is forming on the importance of top-down approaches in rehabilitation, particularly when dealing with robotic devices [14]. The critical aspects of top-down approaches are multifarious and include motivation, active participation [15], learning skills [16] and error-driven-learning [17], evidencing the key aspects of biofeedback information to guide and improve patient robot interactions.

Thus, biofeedback is, at present, the main approach to guide top-down control mechanisms, which represents a powerful tool to drive recovery. To this aim, the patient has to be aware of the differences between on-line performance and the desired performance [18]. In this scenario, many different error signals can be used, and at present, no indication exists for their specific effects on performances [1819]. Many biological parameters have been used to feed biofeedback information to patients in different stroke gait rehabilitation scenarios [20].

In general, in spite of the information content, biofeedback has been associated with improved outcomes in several gait pathologies [21,22,23,24]. Among diverse types of biofeedback, the most generally employed in gait rehabilitation paradigms have been electromyographic (EMG), kinematic as well as robot generated indexes [25], although no comparisons have been made among these approaches.

At present, many robotic devices for gait rehabilitation in stroke are commercially available [26]. Two main classes can be identified, those based on body weight support systems (BWSS) and over ground exoskeletons. Overall, BWSS are the most widely used in rehabilitation centres, with Lokomat, Gait Trainer and GEO systems being the most popular. The present study focuses on the biofeedback content effects during Lokomat gait training in stroke survivors. Commercially available Lokomat biofeedback tools are based either on navigational or robot-generated information. The latter approach focuses on the forces that assist patients to follow the predefined gait pattern due to force transducers built into the robot drives [25].

Generally effectiveness of Lokomat training is assessed with gait functional outcome measures. Specific data about spasticity effects of Lokomat training are rare, and mainly focused on spinal cord injury (SCI) patients and on ankle muscles. In this framework few studies addressed positive effects of Lokomat training on reducing spasticity and improving volitional control of the spastic ankle in persons with incomplete SCI [27], and on reducing the abnormal modulation of neuromuscular properties that arises as secondary effects after SCI [2829]. To our knowledge, as concern stroke population, a single study compared conventional rehabilitation versus Lokomat add-on training selecting spasticity as a secondary outcome, demonstrating no significant robotic gait training effects [30].

Furthermore, no studies have either analysed the use of an electromyographic -based biofeedback (EMGb) of hip, knee and ankle muscles during training with the Lokomat robot, or compared the impact of different biofeedback types on Lokomat robotic gait training. To this end, we designated a randomized controlled trial, because this type of study is the most rigorous and robust research method of determining whether a cause-effect relation exists between an intervention and an outcome [31]. In this pilot study we compared two different types of biofeedback: a robot generated joint torque biofeedback (Rb) versus a novel on-line EMGb. Thus, a randomized cross-over clinical trial using the Lokomat RAGT device, was conducted focusing on patients’ performances, personal experience and robot forces data in sub-acute non ambulatory patients. In particular the main outcome measure was considered the lower limb spasticity. Considering that in stroke population, spasticity may affect quality-of-life and can be highly detrimental to daily function [32], we also analysed patients’ personal experience related to training gait with the Lokomat system.[…]

 

Continue —> Influences of the biofeedback content on robotic post-stroke gait rehabilitation: electromyographic vs joint torque biofeedback | Journal of NeuroEngineering and Rehabilitation | Full Text

figure3

Representative image of visual biofeedback provided to the patient (PT6) according to on-line EMG activity during first (a) and last (b) EMGb training session. EMG data were displayed on the screen with 4 colour stripes partitioned into 16 stages within the gait cycle. First stripe referred to VL-RF, second stripe refers to BF, third stripe referred to GM-SOL and last stripe referred to TA. Coloured lines in the patient’s feedback were generated as follows: i) Red colour means that the signal is higher than in the template, or ii) Blue means that the signal is lower than in the template. From Fig. 3-b is evident a more physiological muscle activity during the whole gait cycle

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[Abstract] Physiological Responses and Perceived Exertion During Robot-Assisted and Body Weight–Supported Gait After Stroke

Introduction. Physiological responses are rarely considered during walking after stroke and if considered, only during a short period (3-6 minutes). The aims of this study were to examine physiological responses during 30-minute robot-assisted and body weight–supported treadmill and overground walking and compare intensities with exercise guidelines.

Methods. A total of 14 ambulatory stroke survivors (age: 61 ± 9 years; time after stroke: 2.8 ± 2.8 months) participated in 3 separate randomized walking trials. Patients walked overground, on a treadmill, and in the Lokomat (60% robotic guidance) for 30 minutes at matched speeds (2.0 ± 0.5 km/h) and matched levels of body weight support (BWS; 41% ± 16%). Breath-by-breath gas analysis, heart rate, and perceived exertion were assessed continuously.

Results. Net oxygen consumption, net carbon dioxide production, net heart rate, and net minute ventilation were about half as high during robot-assisted gait as during body weight–supported treadmill and overground walking (P < .05). Net minute ventilation, net breathing frequency, and net perceived exertion significantly increased between 6 and 30 minutes (respectively, 1.8 L/min, 2 breaths/min, and 3.8 units). During Lokomat walking, exercise intensity was significantly below exercise recommendations; during body weight–supported overground and treadmill walking, minimum thresholds were reached (except for percentage of heart rate reserve during treadmill walking).

Conclusion. In ambulatory stroke survivors, the oxygen and cardiorespiratory demand during robot-assisted gait at constant workload are considerably lower than during overground and treadmill walking at matched speeds and levels of body weight support. Future studies should examine how robotic devices can be Future studies should examine how robotic devices can be exploited to induce aerobic exercise.

 

via Physiological Responses and Perceived Exertion During Robot-Assisted and Body Weight–Supported Gait After Stroke – Nina Lefeber, Emma De Keersmaecker, Stieven Henderix, Marc Michielsen, Eric Kerckhofs, Eva Swinnen, 2018

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