To evaluate the effects of home-based rehabilitation on improving physical function in home-dwelling patients after a stroke.
To compare participation and subjective experience of participants in both home-based multi-user VR therapy and home-based single-user VR therapy.
Crossover, randomized trial
Initial training and evaluations occurred in a rehabilitation hospital; the interventions took place in participants’ homes
Stroke survivors with chronic upper extremity impairment (n=20)
4 weeks of in-home treatment using a custom, multi-user virtual reality system (VERGE): two weeks of both multi-user (MU) and single-user (SU) versions of VERGE. The order of presentation of SU and MU versions was randomized such that participants were divided into two groups, first multi-user (FMU) and first single-user (FSU).
We measured arm displacement during each session (meters) as the primary outcome measure. Secondary outcome measures include: time participants spent using each MU and SU VERGE, and Intrinsic Motivation Inventory (IMI) scores. Fugl-Meyer Upper-Extremity (FMUE) score and compliance with prescribed training were also evaluated. Measures were recorded before, midway, and after the treatment. Activity and movement were measured during each training session.
Arm displacement during a session was significantly affected the mode of therapy (MU: 414.6m, SU: 327.0m, p=0.019). Compliance was very high (99% compliance for MU mode and 89% for SU mode). Within a given session, participants spent significantly more time training in the MU mode than in the SU mode (p=0.04). FMUE score improved significantly across all participants (Δ3.2, p=0.001).
Multi-user VR exercises may provide an effective means of extending clinical therapy into the home.
Effective home rehabilitation is important for recovery of hand grip ability in post-stroke individuals. This paper presents BIGHand, a bilateral, integrated, and gamified handgrip stroke rehabilitation system for independent at-home exercise. BIGHand consists of affordable sensor-integrated hardware (Vernier hand dynamometers, Arduino Uno, interface shield) used to obtain real-time grip force data, and a set of exergames designed as parts of an interactive structural rehabilitation program. This program pairs targeted difficulty progression with user-ability scaled controls to create an adaptive, challenging, and enticing rehabilitation environment. This training prepares users for the many activities of daily living (ADLs) by targeting strength, bilateral coordination, hand-eye coordination, speed, endurance, precision, and dynamic grip force adjustment. Multiple measures are taken to engage, motivate, and guide users through the at-home rehabilitation process, including “smart” post-game feedback and in-game goals.
Wrist and hand rehabilitation are common as people suffer injuries during work and exercise. Typically, the rehabilitation involves the patient and the therapist, which is both time consuming and cost burdening. It is desirable to use advanced telemedicine technologies such that the patient is able to enjoy the freedom of performing the required exercise at their own time and pace, while the healthcare system can operate more efficiently. The Leap Motion Controller (LMC), an inexpensive motion detection device, seems to be a good candidate for remote wrist rehabilitation. In this paper, the functionality and capability of the LMC are examined. Experiments are carried out with a total of twelve people performing twelve different movements. From the experimental results, the feasibility of using the LMC as a rehabilitation device is discussed.
High-dosage rehabilitation therapy enhances neuroplasticity and motor recovery after neurologic injuries such as stroke and spinal cord injury. The optimal exercise dosage necessary to promote upper extremity (UE) recovery is unknown. However, occupational and physical therapy sessions are currently orders of magnitude too low to optimally drive recovery. Taking therapy outside of the clinic and into the living environment using sensing and computer technologies is attractive because it could result in a more cost efficient and effective way to extend therapy dosage. This dissertation developed innovative wearable sensing algorithms and a novel robotic system to enhance hand rehabilitation. We used these technologies to provide on-demand exercise in the living environment in ways not previously achieved, as well as to gain new insights into UE use and recovery after neurologic injuries.
Currently, the standard-of-practice for wearable sensing of UE movement after stroke is bimanual wrist accelerometry. While this approach has been validated as a way to monitor amount of UE activity, and has been shown to be correlated with clinical assessments, it is unclear what new information can be obtained with it. We developed two new kinematic metrics of movement quality obtainable from bimanual wrist accelerometry. Using data from stroke survivors, we applied principal component analysis to show that these metrics encode unique information compared to that typically carried by conventional clinical assessments. We presented these results in a new graphical format that facilitates the identification of limb use asymmetries.
Wrist accelerometry has the limitation that it cannot isolate functional use of the hand. Previously, we had developed a sensing system, the Manumeter, that quantifies finger movement by sensing magnetic field changes induced by movement of a ring worn on the finger, using a magnetometer array worn at the wrist. We developed, optimized, and validated a calibration-free algorithm, the “HAND” algorithm, for real-time counting of isolated, functional hand movements with the Manumeter. Using data from a robotic wrist simulator, unimpaired volunteers and stroke survivors, we showed that HAND counted movements with ~85% accuracy, missing mainly smaller, slower movements. We also showed that HAND counts correlated strongly with clinical assessments of hand function, indicating validity across a range of hand impairment levels.
To date, there have been few attempts to increase hand use and recovery of individuals with a stroke by providing real-time feedback from wearable sensors. We used HAND and the Manumeter to perform a first-of-its-kind randomized controlled trial of the effect of real-time hand movement feedback on hand use and recovery after chronic stroke. We found that real-time feedback on hand movement was ineffective in increasing hand use intensity and improving hand function. We also showed for the first time the non-linear relationship between hand capacity, measured in the laboratory, and actual hand use, measured at-home. Even people with a moderate level of clinical hand function exhibit very low hand use at home.
Finally, the challenge of improving hand function for people with moderate to severe injuries highlights the need for novel approaches to rehabilitation. One emerging technique is regenerative rehabilitation, in which regenerative therapies, such as stem cell engraftment, are coupled with intensive rehabilitation. In collaboration with the Department of Veteran Affairs Gordon Mansfield Spinal Cord Injury Translational Collaborative Consortium, we developed a robot for promoting on-demand, hand rehabilitation in a non-human primate model of hemiparetic spinal cord injury that is being used to synergize hand rehabilitation with novel regenerative therapies. Using an innovative bimanual manipulation paradigm, we show that subjects engaged with the device at a similar rate before and after injury across a range of hand impairment severity. We also demonstrate that we could shape relative use of the arm and increase the number of exercise repetitions per reward by changing parameters of the robot. We then evaluated how the peak grip force that the subjects applied to the robot decreased after SCI, demonstrating that it can serve as a potential marker of recovery.
These developments provide a foundation for future work in technologies for therapeutic movement rehabilitation in the living environment by establishing: 1) new metrics of upper extremity movement quality; 2) a validated algorithm for achieving a “pedometer for the hand” using wearable magnetometry; 3) a negative clinical trial result on the therapeutic effect of real-time hand feedback after stroke, which begs the question of what can be improved in future trials; 4) the nonlinear relationship between hand movement ability and at-home use, supporting the concept of learned non-use; and 5) the first example of robotic regenerative rehabilitation.
The rehabilitation after wrist surgery is extremely important. An instructed therapy in hospital is widely practiced. However, a dependent aging society and rush life style in younger generation have precluded patients to access to the frequent formal therapy. With the advancement in telecommunication technology, we have invented an application for smartphone for home-based wrist motion rehabilitation.
Twenty participants were included in four-week wrist motion rehabilitation programme after wrist surgery. Participants were instructed to use the application by physical therapist and informed details of home-based wrist rehabilitation. The feasibility of application was evaluated by satisfaction level in various aspects and the adherence to the therapy was monitored by function provided in the application. The degrees of motion were compared at the end of prescribed programme.
Patient satisfaction was consistently high in every aspects. Also, the adherence to the therapy was high (90.42%). Ranges of motion significantly gained in every plane of wrist motion ([Formula: see text]).
This novel smartphone application seems to be a promising and convenient alternative for patients who need to gain wrist motion without formal rehabilitation in the hospital. Adherence to the therapy is also easily traced with this application.
A small propeller plane operated by Allen DeNiear climbs into the sky. With a stark desert valley, seemingly inhabited by nothing more than cacti, visible on the red earth below, DeNiear makes a small motion with his hand. The plane pitches downward and dives.
DeNiear is not a pilot.
Nevertheless, his eyes focus on his targets, a series of orbs floating in the sky at various altitudes. His goal is simple enough: collect the orbs without crashing.
His reality, for the moment, is virtual.
Sitting in a lab at the Rutgers School of Health Professions in Newark, DeNiear moves his hand again. In his lap, a small infrared camera linked to the computer running the virtual reality (VR) flight simulator registers his hand movement. The plane ascends, and he collects another orb.
Both the motion and the game seem deceptively easy, but for DeNiear, who is recovering from a stroke and has limited mobility in his right hand, arm, and shoulder, the move is more than meets the eye, and the game, which can be played from anywhere you can fit a laptop, could wind up revolutionizing physical therapy treatments for stroke victims.
Today, DeNiear is with his Rutgers recovery team. But tomorrow, when he needs to do therapeutic work on his hand and arm, he’ll do it from home. No appointment is needed, the trek to a rehab center is eliminated, and—because the games are a lot more fun than monotonous rehab programs—he is more likely to actually do his exercises, thanks to the at-home approach to rehabilitation being tested and refined by Rutgers researchers.
Standard at-home physical therapy regimens for stroke recovery are both boring and frustrating for patients. “The adherence to home exercise programs is incredibly low,” said Gerry Fluet, an associate professor in the rehabilitation and movement sciences department. A physical therapist by trade, Fluet’s research is focused on using VR games to increase patient participation in at-home therapy programs.
“We’re trying to create something that’s more pleasurable and interesting than opening your hand 50 times while you watch it and wish it would move more,” he said. “We set out to develop simulations that people would stop in the middle of their day to play, and tomorrow, pick them up and play them again, and then again the day after that.”
By building the physical rehabilitation regimen into a game, Fluet and his team are able to turn the monotony of stroke rehabilitation into something that is a lot more fun than it used to be. This is important, he said, because after a stroke, a person must put in a lot of work to regain control of the hand.
Without gaming therapy, people are exercising at home two or three times a week, for about five to 10 minutes, if they even bother doing it at all, Fluet said. Meanwhile, people in the lab’s study using the VR games at home are averaging anywhere from 60 to 90 minutes of exercise therapy in a week, with no additional reminders or encouragement. In the real world, this could translate into a much more cost effective and impactful form of stroke rehabilitation.
“We’re seeing tangible, measurable results,” he said.
The game library developed so far has a dozen titles. In addition to piloting a plane, players can drive a car, run through a maze, hit the keys of a piano, and more—all from a Windows-based application developed by Fluet’s long-time collaborator Qinyin Qiu, an assistant professor in the Department of Rehabilitation and Movement Sciences at Rutgers, and Amanda Cronce, a digital designer in the Department of Biomedical Engineering at New Jersey Institute of Technology (NJIT), as part of a collaboration between Rutgers Biomedical and Health Sciences and the Motor Control and Rehabilitation Lab led by Sergei Adamovich at NJIT.
The study has patients play the VR games at home for at least 15 minutes a day, every day for three months. The gaming application collects the patient data and transfers it to a remote data server so Qiu and Fluet can monitor the at-home progress. They can even use the application real-time chat with the patient.
“We’re really trying to turn this into tele-rehab,” said Qiu, adding that the team is exploring go-to-market strategies and commercialization opportunities for their project through an I-Corps grant.
DeNiear is, self-admittedly, “not a video game person,” but he embraces gaming as a method for recovering from his stroke. “As you’re playing the game, it breaks the monotony of what you’re supposed to be doing. If I didn’t have the games, it would be a lot slower,” he said of his recovery. “It helped speed the process up, I think.”
Today, DeNiear has regained enough mobility to be able to drive again, and write with his right hand. He is still not 100 percent recovered, and he acknowledges that he may never be totally back to his old self. But he is committed to making the most of his situation by taking advantage of opportunities to participate in studies like Fluet and Qiu’s at-home VR physical therapy program.
“You gotta work at it,” he said. “You gotta do it.”