Nigerian Innovator Develops Robotic Rehabilitation Glove For Stroke Survivors

 Ask your competent? doctor if spastic fingers can get in these.

Have your competent? doctor validate that this is better than all these other ones out there. Can't do that? PURE INCOMPETENCE! I take no prisoners in getting stroke solved to 100% recovery. Aren't even trying to get there, then REMOVE THAT DEAD WOOD!

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Nigerian Innovator Develops Robotic Rehabilitation Glove For Stroke Survivors

Max India: Antara Introduces Robotic Rehab at Bengaluru Care Home for Stroke Recovery

 You'll have to ask your competent? doctor how close this gets hand recovery to 100%, the only goal in stroke! If your doctor isn't working towards that; THEY NEED TO BE FIRED!

Max India: Antara Introduces Robotic Rehab at Bengaluru Care Home for Stroke Recovery

Antara Senior Care has deployed the ExoAtlet II robotic exoskeleton alongside advanced hand and finger retraining devices at its Bannerghatta centre in Bengaluru. This multidisciplinary program is supervised by Physical Medicine and Rehabilitation (PM&R) specialists to aid stroke recovery. Max India is backing this operational scale-up following strong Q4 FY26 results where consolidated revenues expanded 58.1% year-on-year.

Researcher develops an affordable helping hand for stroke recovery

 The keyword to focus on is 'remaining'! For me there is none; I need dead brain rehab and spasticity cured, WHERE IS THAT?

Researcher develops an affordable helping hand for stroke recovery

by Nathan Kahl, George Mason University

edited by Lisa Lock, reviewed by Andrew Zinin

For millions of stroke survivors, something as simple as picking up a glass of water or holding a sandwich is a daily challenge. Quentin Sanders wants to make those moments easier through wearable robotic technology designed to restore hand function.

The George Mason University assistant professor is developing a new generation of hand exoskeletons that may help people regain independence after stroke. His lab is testing wearable devices that amplify a user's remaining muscle activity, allowing them to open and close their hand more effectively while remaining affordable enough for broader use.

The latest prototype resembles a lightweight glove that fits over the hand and upper arm. As users attempt to move their hand, the device detects their remaining muscle activity and assists the motion.

"We instruct them to try to open their hand," Sanders explained. "When they try to open their hand, we sense whatever residual activity they have, and then the exoskeleton amplifies that."

One version of the device seeks to use wearable ultrasound technology developed in collaboration with George Mason researcher Siddhartha Sikdar, a professor in the Department of Bioengineering and director of the Center for Medtech Innovation. The system will use wearable ultrasound sensors to monitor how muscles deform as they contract, translating those signals into movement. Another, simpler version uses a button embedded in the glove that users press to control the device.

While brain-controlled devices remain the long-term aspiration for many in the field, Sanders is focused on a more practical solution that builds on the muscle activity people retain after a stroke. "I would say brain-controlled systems are kind of the holy grail. In my lab, we're going one level lower to see if we can use muscle activity to control it."

The need for better rehabilitation tools is growing. Sanders noted that roughly 800,000 people experience a stroke each year in the United States, while millions more live with its long-term effects. Advances in medicine mean more people survive strokes, but many struggle to access the lengthy rehabilitation needed to regain function.

"We're at this point where you're living a long life, but you have this need for rehab," Sanders said. "You need either consistent opportunities for movement or some type of device that can help improve your quality of life."

Commercial hand exoskeletons remain scarce in the United States, and many existing systems cost tens of thousands of dollars. Making the technology more affordable and accessible has become one of the driving goals of his research.

"You've got this large population of people who need these devices who aren't getting them," Sanders said. "I'm hoping we can make this kind of a low-cost, accessible version that people can use to improve some aspect of their life."

Sanders is also working to broaden who can benefit from rehabilitation technologies. Many research studies enroll only patients who meet narrow eligibility criteria, leaving others without solutions tailored to their needs.

"I think that's the other breakthrough people are working on that my lab is working on," he said. "How can we make these devices work for a larger population?"

Effects of semi-independent bedside rehabilitation of the upper limb assisted with a mobile wrist-hand exoneuromusculoskeleton (ENMS) for inpatients with subacute stroke: a randomized controlled trial (RCT)

 NO protocol written or locatable; so everything you did is fucking useless! You're fired!

And using Bobath just compounded your incompetence!

Who still uses NDT(Bobath) in stroke rehab when it should have been shitcanned since 2003? Physiotherapy Based on the Bobath Concept for Adults with Post-Stroke Hemiplegia: A Review of Effectiveness Studies 2003)) 

The latest here:

Effects of semi-independent bedside rehabilitation of the upper limb assisted with a mobile wrist-hand exoneuromusculoskeleton (ENMS) for inpatients with subacute stroke: a randomized controlled trial (RCT)

• Yueyan Wang,
• Yanhuan Huang,
• Yurong Mao,
• Haojun Chen,
• Yijie Huang,
• Dongfeng Huang &
• Xiaoling Hu 

We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.

Abstract

Background

Robotic-assisted rehabilitation has been used to release the labor burden in manual practice for early stroke rehabilitation. However, its broader application is constrained by high costs, limited accessibility, and high dependence on professional operation. This study aimed to investigate the feasibility and effectiveness of semi-independent, bedside upper limb rehabilitation assisted by a wrist-hand exoneuromusculoskeleton (WH-ENMS) for individuals with subacute stroke via a randomized controlled trial compared with the outcomes achieved by conventional therapy and training programs assisted by an interlimb-coordinated (IC) robot.

Methods

Fifty-four participants with subacute stroke were randomized into three groups: ENMS, IC, or conventional therapy. All groups underwent 21 rehabilitation sessions (60 min/day for 21 consecutive days), each including 30 min of standardized Bobath therapy. During the additional 30 min of training, the ENMS group engaged in semi-independent bedside training with minimal supervision, the IC group received supervised IC cycling, and the conventional group received proprioceptive neuromuscular facilitation. The rehabilitative effects were evaluated via clinical scores. The primary outcome was the Fugl-Meyer Assessment-Upper Extremity (FMA-UE), and the secondary outcomes included the action research arm test (ARAT), modified Ashworth scale (MAS), and modified Barthel index (MBI). The manpower consumption was quantified on the basis of the professional hours required during training.

Results

The ENMS group required significantly less total occupational therapist (OT) involvement (11.6 h) compared with the IC (22.9 h) and conventional (21.0 h) groups, representing a 49.4% reduction in professional manpower demand. All groups showed significant improvements in FMA-UE, ARAT, and MBI scores. Compared with the other groups, the ENMS group achieved superior gains in voluntary motor function (FMA-UE and ARAT, P < 0.05). The ENMS group also obtained significant reductions in muscle spasticity across multiple joints (MAS, P < 0.05). The IC group demonstrated the largest MBI improvements, while the conventional group showed greater gains in hand function than the IC group (P < 0.05).

Conclusions

Semi-independent bedside rehabilitation with WH-ENMS is feasible, safe, and effective for inpatients with subacute stroke. It reduced the demand for professional manpower while promoting upper limb recovery, particularly wrist–hand function, during the critical subacute neuroplasticity window. This approach could optimize rehabilitation resource allocation and facilitate more personalized interventions.

Trial registration Chinese Clinical Trial Registry Identifier: ChiCTR2300074469. Registered on 2023/08/08.

Stroke rehab goes high-tech: UP study reveals robotic therapy may restore lost grip

 Come back when you finish the job correctly! Will restore using these EXACT PROTOCOLS! 'May' is not a valid result!

What will you do about those like me that have dead brain there and no longer have any signals coming thru? Do you actually have any brains at all that might be useful in solving stroke to 100% recovery?

Stroke rehab goes high-tech: UP study reveals robotic therapy may restore lost grip

MARCH 29, 2026 — The University of the Philippines has just dropped a study that could change how we look at stroke recovery in the country. Researchers Micah Angelo Bacani and Manuel Ramos Jr. from UP Diliman’s Electrical and Electronics Engineering Institute tested a robotic hand orthosis powered by surface electromyography (sEMG) signals — and achieved an impressive 86% response accuracy rate. In plain language: the device listens to muscle signals from the arm and translates them into movement, giving stroke survivors a shot at regaining control of their grip.

Why does this matter? Stroke remains one of the leading causes of disability in the Philippines. Many survivors struggle with long-term loss of hand function, making everyday tasks — from holding a spoon to signing a document — frustratingly difficult. 

Traditional rehab often relies on repetitive, passive exercises. But this study argues that recovery is stronger when patients actively participate, engaging their neurological pathways rather than just following preset motions. 

That’s where robotics step in: machines that respond to the patient’s own muscle signals, not just programmed routines.

The researchers explained, “Using surface electromyography (sEMG) signals from the arm, the recovering stroke patient can control the robotic assistive device for rehabilitation. This is the myoelectric hand orthosis.” 

Additionally, the study found a direct, proportional relationship between the patient’s intended force and the device’s response. In other words, the harder the patient tries, the stronger the orthosis reacts.

Now, that’s not just science — it’s empowerment. Imagine the psychological boost of seeing your effort instantly translated into movement, instead of waiting months for uncertain progress.

Of course, this raises bigger questions for us. Will our healthcare system embrace robotic rehab, or will it remain locked in academic journals? Can public hospitals afford such technology, or will it be another innovation reserved for private clinics and the wealthy? And most importantly, will patients trust machines to help them heal?This study is a reminder that science is racing ahead, but policy and accessibility often lag behind. If robotics can truly help stroke survivors reclaim their independence, then the challenge is no longer technological — it’s social, economic, and political.

When machines can help us heal, will the Philippines let technology be a lifeline for the many, or a luxury for the few?

Intra- and inter-day effects of novel robot-assisted hand movement training in individuals with post-stroke hemiparesis: a single-arm pilot study

So, failure to recover occurred! And no acknowledgement of that failure!

 Intra- and inter-day effects of novel robot-assisted hand movement training in individuals with post-stroke hemiparesis: a single-armpilot study 

y Kazuki Ushizawa, OTR, MS1,2, Shintaro Uehara, RPT, PhD3, Akiko Yuasa, RPT, PhD1,4, Taiki Yoshida, OTR, PhD3, Kyoichi Tomita1, Takayuki Ohtomo, PhD1, Shigeo Tanabe, RPT, PhD3, Yohei Otaka, MD, PhD1 1Department of Rehabilitation Medicine, School of Medicine, Fujita Health University, Toyoake, Aichi, Japan, 2 Graduate School of Health Sciences, Fujita Health University, Toyoake, Aichi, Japan, 3 Faculty of Rehabilitation, School of Health Sciences, Fujita Health University, Toyoake, Aichi, Japan, 4Japan Society for the Promotion of Science, Chiyoda, Tokyo, Japan 

Abstract 

Objectives: To investigate the feasibility of robot-assisted hand movement training using a novel end-effector robot in individuals after stroke. Methods: Eleven individuals with subacute stroke with hand motor impairment underwent robot-assisted repetitive finger flexion/extension for 20 min daily and repeated this training on 7 non-consecutive days. The robot was designed to allow the flexion and extension of the metacarpophalangeal and proximal interphalangeal joints of the index to the little fingers, and to provide assistive torque if the movement did not reach the target angle within a limited time. We assessed the co-contraction index (CCI) of the flexor digitorum superficialis and extensor digitorum muscles and assessed the active range of motion (AROM) of the index finger before and after training each day (intra-day effect). We performed clinical assessments of motor function and spasticity and evaluated the CCI and AROM before and immediately after the 7-day training (inter-day effect). 

Results: 

Ten participants completed the 7-day training. For the intra-day effect, the CCI was significantly decreased immediately after training, particularly during active finger flexion, and the AROM tended to improve from the middle of the training days. For the inter-day effect, there were no significant changes in the Stroke Impairment Assessment Set for Finger Function, modified Ashworth scale, CCI, or AROM after the 7-day training. 

Conclusions: 

Repetitive finger movement training with the assistance of the novel robot improves(NOT GOOD ENOUGH! Survivors want full recovery and YOU FAILED THEM!muscle activation patterns, reducing co-activation between the agonist and antagonist muscles immediately after training

Soft Pneumatic Robot Modulates Graph Theory Metrics of Brain Network for Hand Rehabilitation After Stroke

 From the picture there is no clue if this will work on spastic hands.

Soft Pneumatic Robot Modulates Graph Theory Metrics of Brain Network for Hand Rehabilitation After Stroke

DOI:

10.3791/68588

⸱

October 10th, 2025

Ze-Jian Chen 1, 

Jun-Wen Xia 1, 

Nan Xia 1, 

Ming-Hui Gu 1, 

Jia-Hui Bian 2, 

Zhi-Bing Dong 1, 

Sheng-Qiang Wang 1, 

Qiong Yang 1, 

Zhi-Wei Tang 1, 

Jiang Xu 1, 

Yong Chen 1

1  Department of Rehabilitation Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 

2  College of Sports Medicine, Wuhan Sports University

Summary

This study explores the effects of a configurable soft pneumatic robot on enhancing whole-brain network topology post-stroke. Graph theory analysis indicates significant improvements in clustering coefficient, path length, and global efficiency. Findings highlight the potential of programmable robotic protocols to modulate neuroplasticity and optimize functional recovery in stroke rehabilitation.

Abstract

Functional restoration of the cerebral cortex relies on activity-dependent neuroplasticity following stroke. However, optimizing robotic rehabilitation for hand recovery remains a significant challenge. This proof-of-concept study investigates the feasibility of a configurable soft pneumatic robot in modulating the brain network among ten participants with hand motor impairments after stroke. The programmable robotic intervention was administered in a randomized sequence for resting-state assessment, slow-mode, and fast-mode robotic therapy by adjusting working mode, action time, and interaction duration. Functional near-infrared spectroscopy (fNIRS) was employed to measure cortical activity and functional connectivity. Additionally, graph theory metrics, including clustering coefficient, average path length, small-world index, global efficiency, degree centrality, and eigenvector centrality, were derived from fNIRS-based functional connectivity matrices. The results demonstrated that robotic intervention significantly improved clustering coefficient (P = 0.034), average path length (P = 0.007), and global efficiency (P = 0.001). While small-world index, degree centrality, and eigenvector centrality showed an increasing trend, these differences did not reach statistical significance. Moreover, fast-mode therapy induced more substantial changes in clustering coefficient compared to slow-mode therapy, suggesting a potentially stronger effect on neural reorganization. These findings preliminarily support the use of soft robotics with adjustable paradigms to enhance brain network connectivity and facilitate neuroplasticity following stroke. The observed improvements in global efficiency and small-world properties indicate that robotic therapy optimizes cortical organization, promoting functional recovery. Further studies with larger sample sizes and personalized intervention protocols are needed to confirm these results and explore their long-term effects.

Introduction

Stroke remains a leading cause of mortality and long-term disability for adults worldwide1. Among its debilitating sequelae, hand motor impairments significantly compromise essential daily activities and functional independence, thereby diminishing social participation and life quality in stroke survivors2,3. While physical medicine and rehabilitation are critical for motor recovery, conventional therapeutic approaches often yield inconsistent outcomes due to the complexity and interindividual variability of post-stroke neural reorganization4.

In recent years, robotic-assisted therapy (RAT) has gained significant attention for its ability to deliver high-intensity, repetitive, and task-specific training to facilitate motor recovery5. To tailor such interventions more effectively, a better understanding of the neural biomarkers is crucial for direct modulation by robotic interventions. This can ultimately help optimize therapeutic strategies and promote hand dexterity. In a previous study, voxel-based lesion symptom mapping has indicated that post-stroke proximal impairments of the upper limb could be associated with deficits in descending tracts from corticomotor areas and striatum. Distinct from this, hand impairments result from isolated injuries to the brain cortex6. However, clinical outcomes for hand function remain variable, and the neural mechanisms underlying motor recovery are not yet fully elucidated7,8,9. Moreover, most commercial robotic training follows predefined protocols with limited adaptability. It fails to address patient-specific needs and potentially constrains their clinical effectiveness10.

Over the past decades, neuroimaging studies have highlighted the pivotal role of brain network reorganization in stroke recovery, characterized by alterations in functional activity and connectivity. Functional near-infrared spectroscopy (fNIRS), a non-invasive technique for monitoring cortical hemodynamics, has emerged as a valuable tool for assessing neural dynamics and guiding rehabilitation settings11. Its portability and robustness against motion artifacts make it particularly well-suited for real-time monitoring and information transfer during robotic therapy12. While previous studies have documented changes in cortical interconnectivity following motor training, the effects of RAT on whole-brain network topology remain insufficiently explored13.

Characterizing changes in brain network topology offers a valuable window into the neuroplastic processes that underlie functional recovery after brain ischemic or hemorrhagic injuries. The human brain operates as a complex network, where neurological function or repair relies on both regional integrity and the dynamic interactions among multiple areas14. Under these circumstances, graph theory provides a robust framework for quantifying large-scale brain network architecture, offering insights into cortical organization at nodal levels15. By computing key network metrics such as clustering coefficient, average path length, and global efficiency, researchers may evaluate how RAT influences neural information processing and transfer capacity. As shown in previous literature, stroke-induced disruptions to network integrity often manifest as alterations in small-world properties and overall topological organization16,17. Rehabilitation, particularly task-specific and repetitive training, has been associated with cortical reorganization. However, the precise impact of varied robotic interventions on these network dynamics remains inadequately understood18.

This study aims to investigate the feasibility of a programmable soft pneumatic robot in modulating brain network dynamics in people who have had a stroke. Compared with hand flexion/extension exercise in existing robotic paradigms, the current pneumatic robot has advantages in customizing the working mode, action time, and interaction duration for the desired movements, such as grasp, release, and pinch19,20. These diverse modes of human-robot interaction may provide more targeted stimulation to the sensorimotor cortical areas. Such a paradigm can potentially promote neuroplasticity in regions associated with hand function recovery13. In addition, fNIRS-based graph theory analysis across diverse therapeutic conditions was utilized to assess how individualized robotic strategies may optimize hand motor recovery. Moreover, the neural mechanisms underlying soft robotic rehabilitation could be preliminarily elucidated. Understanding these network-level adaptations can inform the development of targeted, neurophysiology-driven rehabilitation protocols, ultimately enhancing hand recovery in stroke survivors.

Protocol

This study was approved by the Ethics Committee of the Tongji Hospital, Wuhan, China. All research protocols adhered to the principles outlined in the Declaration of Helsinki. Written informed consent was obtained from all participants before their inclusion in the study. The equipment and software used are listed in the Table of Materials.

1. Participants

1. Consider the following inclusion criteria:
   1. Age between 18 and 75 years.
   2. First-ever ischemic or hemorrhagic stroke confirmed by CT or MRI.
   3. Medically stable condition.
   4. Sign informed consent in person or from their authorized representative(s).
2. Consider the following exclusion criteria:
   1. Severe aphasia, cognitive impairments, consciousness disorders, or inability to cooperate with the intervention or assessment protocol.
   2. Inability to remain seated for at least 20 min.
   3. Severe muscle tone abnormalities (Modified Ashworth Scale score ≥2). (So, leaving survivors behind, which is a no-no in rehab. Cherry picking to make your intervention look better should not be allowed!)

2. Soft pneumatic robotic system

1. Equipment connection and startup
   1. Plug the power cable of the robotic system into an electrical outlet.
   2. Turn on the computer by pressing the button located on the computer tower.
   3. Connect the two air pump power cables to the power outlet and the soft pneumatic glove.
   4. Press the green power button on the back of the robotic system to activate the device.
2. Wearing the soft pneumatic robot
   1. Assist the participant in wearing the soft pneumatic robot on the affected hand, ensuring a secure fit around the palm and fingers.
   2. Fasten the robot using the Velcro strap, ensuring it is securely positioned from the dorsal side of the thumb to the thenar eminence on the palmar side for optimal stability.
3. Robotic control and mode selection
   1. Press the green Connect button below the imaging device.
   2. On the computer interface, select the Robotic Inhalation Mode.
   3. Configure the communication port (Com 7) to ensure proper device connectivity.
   4. Select the appropriate inflation-deflation mode and confirm parameter settings without causing discomfort to patients. Slow Mode: 2000 ms action time (0.5 Hz) with 75% interaction duration. Fast Mode: 200 ms action time (5Hz) with 75% interaction duration21 (Figure 1).

Figure 1: Experimental equipment and configuration. (A) Experimental setup. (B) Soft robotic configuration. (C) Soft pneumatic robot. (D) fNIRS configuration and calibration. Please click here to view a larger version of this figure.

3. Functional Near-Infrared Spectroscopy (fNIRS) system

1. Equipment connection and initialization
   1. Use the continuous wave fNIRS system to record data from all participants. The system emits near-infrared light at wavelengths of 690 nm and 830 nm, penetrating 2-3 cm beneath the cerebral cortex, with a 

Clinicians Support the Use of Novel Dual-Purpose Robotic Hand Orthosis for Post-Stroke Rehab

 But will this device cure spasticity of the hand? From an earlier post this seems to be it, but does not correspond to the picture below.

Can it get the hand fully recovered while still in the hospital?

Clinicians Support the Use of Novel Dual-Purpose Robotic Hand Orthosis for Post-Stroke Rehab

A majority of clinicians who work with stroke patients (64.9%) said they would use a novel robotic arm orthosis for both rehabilitation and assistance in performing daily activities. Most thought the prototype could be used at every stage of stroke rehabilitation.

Only 20% of participants had used robotic devices in the clinic, despite the large number of robotics available for use post-stroke.

• Involving clinicians in the development of devices may facilitate their adoption in clinical practice.

A new study in Disability and Rehabilitationexplores clinician perspectives about an experimental robotic arm orthosis developed by NewYork-Presbyterian physicians for stroke patients with upper limb weakness. Occupational and physical therapists in the study recognized both a rehabilitative and assistive role for the novel device, called MyHand, and provided input into its on-going development.

Below, Joel Stein, MD, chair of the Department of Rehabilitation and Regenerative Medicine at NewYork-Presbyterian and Columbia and senior author of the study, shares his insights from the study and on the role of robotics in rehabilitation and home settings.

Designing a Device to Address Upper Limb Weakness

Most stroke survivors with upper limb weakness have more difficulty opening their hand than closing it. We envisioned a robotic device that would allow them to use their preserved ability to bend the fingers but assists them with opening them. This could help people to pick up objects and manipulate them better. For example, they might be able to prepare a meal or pick up a bite of food and bring it to their mouth. 

Our device uses sensors to pick up electrical signals from the muscles in the forearm to elicit movement. Recently, we have been using machine learning and artificial intelligence to anticipate what the user intends to do in real time and equip the device to assist them with that action.

The Role of Clinicians in Device Design

As a clinician involved in research and technology, I have been approached in the past by engineering teams saying, “We built this impressive new device Do you think you could find a good use for it? Would this help your patients?” That is disappointing on the clinician side because the engineers missed the opportunity to hear from us earlier on in the design phase. As a result, a lot of these devices are technologically impressive but not that appealing clinically.

With this device, clinicians were involved from the beginning of the design process. We then expanded our understanding of clinician perspective by surveying a large national group of occupational and physical therapists to gather their perceptions of the MyHand prototype to guide future development. We wanted to understand their perceptions about the prototype, how they would use it with patients during treatment, and their opinions about the cost and other factors.

Study Results

The majority of the survey participants (64.9%) reported that they would have patients use the device for both rehabilitation and daily assistance performing household tasks, compared to those who would use it only for rehabilitation (28.6%) or assistance (6.6%). The respondents thought that the device could be used in outpatient (85.1%), acute rehabilitation (61.7%), and subacute rehabilitation (57.8% ) settings, and most perceived it useful in all stages of stroke. More than one-third of participants (37.5%) thought the device could be used in home settings.

One of our key findings was that less than 20% of the participating therapists have used robotic devices with patients. That's startling given that robotics started entering into the commercial realm for rehabilitation medicine in the late 1990s. Some of the challenges to adoption include device design and financial barriers.

MyHand device and EMG armband

Next Steps

In addition to clinicians’ views on the device, we have collected data on patients’ perspective about the prototype, which we are still analyzing. We’re also working on a number of technological improvements to enhance the function of the device.

One of the issues that has been a huge barrier in this field is that patients develop spasticity and that can worsen when they try and move the arm. We are partnering with colleagues at other institutions to try and find ways to address this so that people are not fighting the device.

When we do testing, our graduate students and engineering graduate students are present. The interaction between engineers and clinicians with patients in the room—the ability to troubleshoot in real time and get feedback from the patient—is priceless.

— Dr. Joel Stein

What sets us apart are our academic partnerships between NewYork-Presbyterian and Columbia, NewYork-Presbyterian and Weill Cornell Medicine, and Cornell University. When we perform testing, our medicine graduate students and engineering graduate students are present. The interaction between engineers and clinicians with patients in the room—the ability to troubleshoot in real time and get feedback from the patient—is priceless.

• Intro
• Designing a Device
• Role of Clinicians
• Study Results
• Next Steps

Learn More

Winterbottom L, Chen A, Mendonca R, Nilsen DM, Ciocarlie M, Stein J. Clinician perceptions of a novel wearable robotic hand orthosis for post-stroke hemiparesis. Disability and Rehabilitation. 2024 Jul 8:1-10. doi: 10.1080/09638288.2024.2375056.

Clinician perceptions of a novel wearable robotic hand orthosis for post-stroke hemiparesis

 I happen to think it is vastly more important to have stroke survivors evaluate the efficacy and protocols used with these interventions. Any clinician evaluations using the Rankin scale can't discriminate any useful improvements.

I consider the Rankin scale useless, not objective except for #6, dead?

Clinician perceptions of a novel wearable robotic hand orthosis for post-stroke hemiparesis

Lauren Winterbottom

,

Ava Chen

,

Rochelle Mendonca

,

Dawn M. Nilsen

,

Matei Ciocarlie

&

Joel Stein

Received 19 Oct 2023, Accepted 27 Jun 2024, Published online: 08 Jul 2024

• Cite this article
• https://doi.org/10.1080/09638288.2024.2375056
• CrossMark

 

Abstract

Purpose

Wearable robotic devices are currently being developed to improve upper limb function for individuals with hemiparesis after stroke. Incorporating the views of clinicians during the development of new technologies can help ensure that end products meet clinical needs and can be adopted for patient care.

Methods

In this cross-sectional mixed-methods study, an anonymous online survey was used to gather clinicians’ perceptions of a wearable robotic hand orthosis for post-stroke hemiparesis. Participants were asked about their clinical experience and provided feedback on the prototype device after viewing a video.

Results

154 participants completed the survey. Only 18.8% had previous experience with robotic technology. The majority of participants (64.9%) reported that they would use the device for both rehabilitative and assistive purposes. Participants perceived that the device could be used in supervised clinical settings with all phases of stroke. Participants also indicated a need for insurance coverage and quick setup time.

Conclusions

Engaging clinicians early in the design process can help guide the development of wearable robotic devices. Both rehabilitative and assistive functions are valued by clinicians and should be considered during device development. Future research is needed to understand a broader set of stakeholders’ perspectives on utility and design.

IMPLICATIONS FOR REHABILITATION

• Clinicians valued both assistive and rehabilitative uses of a wearable robotic hand orthosis designed for individuals with hemiparesis after stroke.

• Wearable robotic hand devices should have the capacity to engage in functional, real-world activities for both assistive and rehabilitative purposes.

• Pragmatic factors, such as set-up and training time, must be balanced with device complexity to enable implementation in clinical settings.

• Stakeholders, such as clinicians, play an important role in identifying design priorities for wearable robotic devices to ensure these devices can meet the needs of end-users.