Posts Tagged robot

[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

, , , , , , , , , , , , , ,

Leave a comment

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


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.


  1. 1.
    Yap, H.K., Lim, J.H., Goh, J.C.H., et al.: Design of a soft robotic glove for hand rehabilitation of stroke patients with clenched fist deformity using inflatable plastic actuators. J. Med. Devices 10(4), 044504 (2016)CrossRefGoogle Scholar
  2. 2.
    Kemna, S., Culmer, P.R., Jackson, A.E., et al.: Developing a user interface for the iPAM stroke rehabilitation system. In: IEEE International Conference on Rehabilitation Robotics, pp. 879–884. IEEE (2009)Google Scholar
  3. 3.
    Cai, Z., Tong, D., Meadmore, K.L., et al.: Design & control of a 3D stroke rehabilitation platform. In: IEEE International Conference on Rehabilitation Robotics (2011). 5975412Google Scholar
  4. 4.
    Lum, P.S., Burgar, C.G., Van der Loos, M.: MIME robotic device for upper-limb neurorehabilitation in subacute stroke subjects: a follow-up study. J. Rehabil. Res. Dev. 43(5), 631 (2006)CrossRefGoogle Scholar
  5. 5.
    Pehlivan, A.U., Celik, O., O’Malley, M.K.: Mechanical design of a distal arm exoskeleton for stroke and spinal cord injury rehabilitation. In: IEEE International Conference on Rehabilitation Robotics. IEEE (2011). 5975428Google Scholar
  6. 6.
    Polygerinos, P., Wang, Z., Galloway, K.C., et al.: Soft robotic glove for combined assistance and at-home rehabilitation. Robot. Auton. Syst. 73(C), 135–143 (2015)CrossRefGoogle Scholar
  7. 7.
    Yap, H.K., Lim, J.H., Nasrallah, F., et al.: MRC-glove: A fMRI compatible soft robotic glove for hand rehabilitation application. In: IEEE International Conference on Rehabilitation Robotics, pp. 735–740. IEEE (2015)Google Scholar
  8. 8.
    Yap, H.K., Lim, J.H., Nasrallah, F., et al.: A soft exoskeleton for hand assistive and rehabilitation application using pneumatic actuators with variable stiffness. In: IEEE International Conference on Robotics and Automation, pp. 4967–4972. IEEE (2015)Google Scholar
  9. 9.
    Yap, H.K., Khin, P.M., Koh, T.H., et al.: A fully fabric-based bidirectional soft robotic glove for assistance and rehabilitation of hand impaired patients. IEEE Robot. Autom. Lett. PP(99), 1 (2017)Google Scholar
  10. 10.
    Mosadegh, B., Polygerinos, P., Keplinger, C., et al.: Soft robotics: pneumatic networks for soft robotics that actuate rapidly. Adv. Funct. Mater. 24(15), 2109 (2014)CrossRefGoogle Scholar
  11. 11.
    Galloway, K.C., Polygerinos, P., Walsh, C.J., et al.: Mechanically programmable bend radius for fiber-reinforced soft actuators. In: International Conference on Advanced Robotics. IEEE (2014)Google Scholar
  12. 12.
    Yap, H.K., Lim, J.H., Nasrallah, F., et al.: Design and preliminary feasibility study of a soft robotic glove for hand function assistance in stroke survivors. Front. Neurosci. 11, 547 (2017)CrossRefGoogle Scholar
  13. 13.
    Yap, H.K., Ang, B.W., Lim, J.H., et al.: A fabric-regulated soft robotic glove with user intent detection using EMG and RFID for hand assistive application. In: IEEE International Conference on Robotics and Automation, pp. 3537–3542. IEEE (2016)Google Scholar
  14. 14.
    Yap, H.K., Lim, J.H., Nasrallah, F., et al.: Characterisation and evaluation of soft elastomeric actuators for hand assistive and rehabilitation applications. J. Med. Eng. Technol. 40, 1–11 (2016)CrossRefGoogle Scholar
  15. 15.
    Tong, M.: Design, Modeling and Fabrication of a Massage Neck Support Using Soft Robot Mechanis. The Ohio State University (2014)Google Scholar
  16. 16.
    Aubin, P.M., Sallum, H., Walsh, C., et al.: A pediatric robotic thumb exoskeleton for at-home rehabilitation: the Isolated Orthosis for Thumb Actuation (IOTA). In: Proceedings of IEEE International Conference on Rehabilitation Robotics, pp. 1–6 (2013)Google Scholar

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

, , , , , , ,

Leave a comment

[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,

, , , , , , , , ,

Leave a comment

[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?

, , , , , ,

Leave a comment

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



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.


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.


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.


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.


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


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

, , , , , ,

Leave a comment

[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

, , , , , , ,

Leave a comment

[WEB SITE] RATULS Trial Using BIONIK InMotion Researches Robot-Assisted Stroke Therapy

Published on 


A landmark Robot Assisted Training for the Upper Limb after Stroke (RATULS) trial utilizing BIONIK Laboratories Corp’s InMotion Robotic Therapy Systems was completed recently, the Toronto-based company announces.

The RATULS trial, which began in 2014 and was completed at the end of 2018, compared the clinical effectiveness of robot-assisted training, enhanced upper limb therapy, and usual care for patients with moderate or severe upper limb functional limitation.

Results were presented recently at the European Stroke Organisation Conference (ESOC) in Milan, Italy, and published in The Lancet.

“We are pleased that the RATULS trial confirmed the finding of previous research studies which demonstrated that robot-assisted therapy improved upper limb impairment when compared with conventional care methods for stroke victims.

“The trial’s finding that robotic therapy is the only therapy to statistically maintain a significant impairment advantage at six months after treatment is a strong signal that robotic therapy is critical for achieving positive patient outcomes,” says Dr Eric Dusseux, CEO, BIONIK Laboratories, in a media release.

For the RATULS trial, the primary outcome for upper limb success was determined by Action Research Arm Test (ARAT), with four distinct success criteria that varied according to baseline severity, not used previously and developed by the RATULS trial team.

Although the findings demonstrated that robot-assisted therapy improved upper limb impairment, using this ARAT measurement, the trial was unable to conclude that robot-assisted therapy or enhanced upper limb therapy resulted in improved upper limb functionality after stroke compared with usual care provided to patients with stroke-related upper limb functional limitation. The attrition rate was also drastically reduced in patient population following either robotic therapy or enhanced upper limb therapy versus usual care only, and most of the withdrawals before 3 months in usual care were due to disappointment with treatment allocation, the release explains.

“The combination of evidenced-based medicine and real-world clinical feedback have led to the release of substantially improved versions of the InMotion ARM Robotic Therapy System announced in early 2018, and the InMotion ARM/HAND Robotic Therapy System announced beginning of 2019. These versions of our products include enhanced software applications with patient-centric configurable protocols to assist the therapist in providing specialized treatment of stroke and traumatic brain injury.”

[Source(s): BIONIK Laboratories Corp, Business Wire]


via RATULS Trial Using BIONIK InMotion Researches Robot-Assisted Stroke Therapy – Rehab Managment

, , , , , ,

Leave a comment

[Abstract] Control and Dynamic Manipulability of a Dual-Arm/Hand Robotic Exoskeleton System (EXO-UL8) for Rehabilitation Training in Virtual Reality

Author & Article Info

Every year there are about 800,000 new stroke patients in the US, and many of them suffer from upper limb neuromuscular disabilities including but not limited to: weakness, spasticity and abnormal synergy. Patients usually have the potential to rehabilitate (to some extent) based on neuroplasticity, and physical therapy intervention helps accelerate the recovery. However, many patients could not afford the expensive physical therapy after the onset of stroke, and miss the opportunity to get recovered. Robot-assisted rehabilitation thus might be the solution, with the following unparalleled advantages:

  1. 24/7 capability of human arm gravity compensation;
  2. multi-joint movement coordination/correction, which could not be easily done by human physical therapists;
  3. dual-arm training, either coupled in joint space or task space;
  4. quantitative platform for giving instructions, providing assistance, exerting resistance, and collecting real-time data in kinematics, dynamics and biomechanics;
  5. potential training protocol personalization; etc.

However, in the rehabilitation robotics field, there are still many open problems. I am especially interested in:

  1. compliant control, in high-dimensional multi-joint coordination condition;
  2. assist-as-needed (AAN) control, in quantitative model-based approach and model-free approach;
  3. dual-arm training, in both symmetric and asymmetric modes;
  4. system integration, e.g., virtual reality (VR) serious games and graphical user interfaces (GUIs) design and development.

Our dual-arm/hand robotic exoskeleton system, EXO-UL8, is in its 4th generation, with seven (7) arm degrees-of-freedom (DOFs) and one (1) DOF hand gripper enabling hand opening and closing on each side. While developing features on this research platform, I contributed to the robotics research field in the following aspects:

(1) I designed and developed a series of eighteen (18) serious VR games and GUIs that could be used for interactive post-stroke rehabilitation training. The VR environment, together with the exoskeleton robot, provides patients and physical therapists a quantitative rehabilitation training platform with capability in real-time human performance data collection and analysis.

(2) To provide better compliant control, my colleagues and I proposed and implemented two new admittance controllers, based on the work done by previous research group alumni. Both the hyper parameter-based and Kalman Filter-based admittance controllers have satisfactory heuristic performance, and the latter is more promising in future adaptation. Unlike many other upper-limb exoskeletons, our current system utilizes force and torque (F/T) sensors and position encoders only, no surface electromyography (sEMG) signals are used. It brings convenience to practical use, as well as technical challenges.

(3) To provide better AAN control, which is still not well understood in the academia, I worked out a redundant version of modified dynamic manipulability ellipsoid (DME) model to propose an Arm Postural Stability Index (APSI) to quantify the difficulty heterogeneity of the 3D Cartesian workspace. The theoretical framework could be used to teach the exoskeleton where and when to provide assistance, and to guide the virtual reality where to add new minimal challenges to stroke patients. To the best of my knowledge, it is also for the first time that human arm redundancy resolution was investigated when arm gravity is considered.

(4) For the first time, my colleagues and I have done a pilot study on asymmetric dual-arm training using the exoskeleton system on one (1) post-stroke patient. The exoskeleton on the healthy side could trigger assistance for that on the affected side, and validates that the current mechanism/control is eligible for asymmetric dual-arm training.

(5) Other works of mine include: activities of daily living (ADLs) data visualization for VR game difficulty design; human arm synergy modeling; dual-arm manipulation taxonomy classification (on-going work).

via Control and Dynamic Manipulability of a Dual-Arm/Hand Robotic Exoskeleton System (EXO-UL8) for Rehabilitation Training in Virtual Reality

, , , , , ,

Leave a comment

[Abstract + References] eConHand: A Wearable Brain-Computer Interface System for Stroke Rehabilitation


Brain-Computer Interface (BCI) combined with assistive robots has been developed as a promising method for stroke rehabilitation. However, most of the current studies are based on complex system setup, expensive and bulky devices. In this work, we designed a wearable Electroencephalography(EEG)-based BCI system for hand function rehabilitation of the stroke. The system consists of a customized EEG cap, a small-sized commercial amplifer and a lightweight hand exoskeleton. In addition, visualized interface was designed for easy use. Six healthy subjects and two stroke patients were recruited to validate the safety and effectiveness of our proposed system. Up to 79.38% averaged online BCI classification accuracy was achieved. This study is a proof of concept, suggesting potential clinical applications in outpatient environments.

2. E. Donchin , K. Spencer and R. Wijesinghe , “The mental prosthesis: assessing the speed of a P300-based brain-computer interface”, IEEE Transactions on Rehabilitation Engineering, vol. 8, no. 2, pp. 174-179, 2000.

3. D. McFarland and J. Wolpaw , “Brain-Computer Interface Operation of Robotic and Prosthetic Devices”, Computer, vol. 41, no. 10, pp. 52-56, 2008.

4. Xiaorong Gao , Dingfeng Xu , Ming Cheng and Shangkai Gao , “A bci-based environmental controller for the motion-disabled”, IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 11, no. 2, pp. 137-140, 2003.

5. A. Ramos-Murguialday , D. Broetz , M. Rea et al “Brain-machine interface in chronic stroke rehabilitation: A controlled study”, Annals of Neurology, vol. 74, no. 1, pp. 100-108, 2013.

6. F. Pichiorri , G. Morone , M. Petti et al “Brain-computer interface boosts motor imagery practice during stroke recovery”, Annals of Neurology, vol. 77, no. 5, pp. 851-865, 2015.

7. M. A. Cervera , S. R. Soekadar , J. Ushiba et al “Brain-computer interfaces for post-stroke motor rehabilitation: a meta-analysis”, Annals of Clinical and Translational Neurology, vol. 5, no. 5, pp. 651-663, 2018.

8. K. Ang , K. Chua , K. Phua et al “A Randomized Controlled Trial of EEG-Based Motor Imagery Brain-Computer Interface Robotic Rehabilitation for Stroke”, Clinical EEG and Neuroscience, vol. 46, no. 4, pp. 310-320, 2014.

9. N. Bhagat , A. Venkatakrishnan , B. Abibullaev et al “Design and Optimization of an EEG-Based Brain Machine Interface (BMI) to an Upper-Limb Exoskeleton for Stroke Survivors”, Frontiers in Neuroscience, vol. 10, pp. 122, 2016.

10. J. Webb , Z. G. Xiao , K. P. Aschenbrenner , G. Herrnstadt , and C. Menon , “Towards a portable assistive arm exoskeleton for stroke patient rehabilitation controlled through a brain computer interface”, in Biomedical Robotics and Biomechatronics (BioRob), 2012 4th IEEE RAS & EMBS International Conference, pp. 1299-1304, 2012.

11. A. L. Coffey , D. J. Leamy , and T. E. Ward , “A novel BCI-controlled pneumatic glove system for home-based neurorehabilitation”, in Engineering in Medicine and Biology Society (EMBC), 2014 36th Annual International Conference of the IEEE, pp. 3622-3625, 2014.

12. D. Bundy , L. Souders , K. Baranyai et al “Contralesional Brain-Computer Interface Control of a Powered Exoskeleton for Motor Recovery in Chronic Stroke Survivors”, Stroke, vol. 48, no. 7, pp. 1908-1915, 2017.

13. X. Shu , S. Chen , L. Yao et al “Fast Recognition of BCI-Inefficient Users Using Physiological Features from EEG Signals: A Screening Study of Stroke Patients”, Frontiers in Neuroscience, vol. 12, pp. 93, 2018.

14. A. Delorme , T. Mullen , C. Kothe et al “EEGLAB, SIFT, NFT, BCILAB, and ERICA: New Tools for Advanced EEG Processing”, Computational Intelligence and Neuroscience, vol. 2011, pp. 1-12, 2011.

15. G. Schalk , D. McFarland , T. Hinterberger , N. Birbaumer and J. Wolpaw , “BCI2000: A General-Purpose Brain-Computer Interface (BCI) System”, IEEE Transactions on Biomedical Engineering, vol. 51, no. 6, pp. 1034-1043, 2004.

16. M. H. B. Azhar , A. Casey , and M. Sakel , “A cost-effective BCI assisted technology framework for neurorehabilitation”, The Seventh International Conference on Global Health Challenges, 18th-22nd November, 2018. (In Press)

17. C. M. McCrimmon , M. Wang , L. S. Lopes et al “A small, portable, battery-powered brain-computer interface system for motor rehabilitation”, Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 2776-2779, 2016.

18. J. Meng , B. Edelman , J. Olsoe et al “A Study of the Effects of Electrode Number and Decoding Algorithm on Online EEG-Based BCI Behavioral Performance”, Frontiers in Neuroscience, vol. 12, pp. 227, 2018.

19. T. Mullen , C. Kothe , Y. Chi et al “Real-time neuroimaging and cognitive monitoring using wearable dry EEG”, IEEE Transactions on Biomedical Engineering, vol. 62, no. 11, pp. 2553-2567, 2015.


via eConHand: A Wearable Brain-Computer Interface System for Stroke Rehabilitation – IEEE Conference Publication

, , , , , , , , , , , , ,

Leave a comment

[ARTICLE] Hand Rehabilitation Robotics on Poststroke Motor Recovery – Full Text


The recovery of hand function is one of the most challenging topics in stroke rehabilitation. Although the robot-assisted therapy has got some good results in the latest decades, the development of hand rehabilitation robotics is left behind. Existing reviews of hand rehabilitation robotics focus either on the mechanical design on designers’ view or on the training paradigms on the clinicians’ view, while these two parts are interconnected and both important for designers and clinicians. In this review, we explore the current literature surrounding hand rehabilitation robots, to help designers make better choices among varied components and thus promoting the application of hand rehabilitation robots. An overview of hand rehabilitation robotics is provided in this paper firstly, to give a general view of the relationship between subjects, rehabilitation theories, hand rehabilitation robots, and its evaluation. Secondly, the state of the art hand rehabilitation robotics is introduced in detail according to the classification of the hardware system and the training paradigm. As a result, the discussion gives available arguments behind the classification and comprehensive overview of hand rehabilitation robotics.

1. Background

Stroke, caused by death of brain cells as a result of blockage of a blood vessel supplying the brain (ischemic stroke) or bleeding into or around the brain (hemorrhagic stroke), is a serious medical emergency []. Stroke can result in death or substantial neural damage and is a principal contributor to long-term disabilities []. According to the World Health Organization estimates, 15 million people suffer stroke worldwide each year []. Although technology advances in health care, the incidence of stroke is expected to rise over the next decades []. The expense on both caring and rehabilitation is enormous which reaches $34 billion per year in the US []. More than half of stroke survivors experience some level of lasting hemiparesis or hemiplegia resulting from the damage to neural tissues. These patients are not able to perform daily activities independently and thus have to rely on human assistance for basic activities of daily living (ADL) like feeding, self-care, and mobility [].

The human hands are very complex and versatile. Researches show that the relationship between the distal upper limb (i.e., hand) function and the ability to perform ADL is stronger than the other limbs []. The deficit in hand function would seriously impact the quality of patients’ life, which means more demand is needed on the hand motor recovery. However, although most patients get reasonable motor recovery of proximal upper extremity according to relevant research findings, recovery at distal upper extremity has been limited due to low effectivity []. There are two main reasons for challenges facing the recovery of the hand. First, in movement, the hand has more than 20 degree of freedom (DOF) which makes it flexible, thus being difficult for therapist or training devices to meet the needs of satiety and varied movements []. Second, in function, the area of cortex in correspondence with the hand is much larger than the other motor cortex, which means a considerable amount of flexibility in generating a variety of hand postures and in the control of the individual joints of the hand. However, to date, most researches have focused on the contrary, lacking of individuation in finger movements []. Better rehabilitation therapies are desperately needed.

Robot-assisted therapy for poststroke rehabilitation is a new kind of physical therapy, through which patients practice their paretic limb by resorting to or resisting the force offered by the robots []. For example, the MIT-Manus robot uses the massed training approach by practicing reaching movements to train the upper limbs []; the Mirror Image Movement Enabler (MIME) uses the bilateral training approach to train the paretic limb while reducing abnormal synergies []. Robot-assisted therapy has been greatly developed over the past three decades with the advances in robotic technology such as the exoskeleton and bioengineering, which has become a significant supplement to traditional physical therapy []. For example, compared with the therapist exhausted in training patients with manual labor, the hand exoskeleton designed by Wege et al. can move the fingers of patients dexterously and repeatedly []. Besides, some robots can also be controlled by a patient’s own intention extracted from biosignals such as electromyography (EMG) and electroencephalograph (EEG) signals []. These make it possible to form a closed-loop rehabilitation system with the robotic technology, which cannot be achieved by any conventional rehabilitation therapy [].

Existing reviews of hand rehabilitation robotics on poststroke motor recovery are insufficient, for most studies research on the application of robot-assisted therapy on other limbs instead of the hand []. Furthermore, current reviews focus on either the hardware design of the robots or the application of specific training paradigms [], while both of them are indispensable to an efficient hand rehabilitation robot. The hardware system makes the foundation of the robots’ function, while the training paradigm serves as the real functional parts in the motor recovery that decides the effect of rehabilitation training. These two parts are closely related to each other.

This paper focuses on the application of robot-assisted therapy on hand rehabilitation, giving an overview of hand rehabilitation robotics from the hardware systems to the training paradigms in current designs, for a comprehensive understanding is pretty meaningful to the development of an effective rehabilitation robotic system. The second section provides a general view of the robots in the entire rehabilitation robotic system. Then, the third section sums up and classifies hardware systems and the training paradigms in several crucial aspects on the author’s view. Last, the state of the art hand rehabilitation robotics is discussed and possible direction of future robotics in hand rehabilitation is predicted.[…]

Continue —-> Hand Rehabilitation Robotics on Poststroke Motor Recovery


An external file that holds a picture, illustration, etc.Object name is BN2017-3908135.003.jpg

Figure 3
Examples of different kinds of robots [].

, , , , , , , ,

Leave a comment

%d bloggers like this: