Posts Tagged Wearable device

[Abstract] Design and Implementation of a Wearable Device for Motivating Patients With Upper and/or Lower Limb Disability Via Gaming and Home Rehabilitation

Abstract

Stroke survivors often suffer from a permanent or partial disability that restricts the movement of the hands, arms and/or legs. To help patients recover, rehabilitation should be at an earlier stage of the injury. Without motivation, it would be challenging for patients to successfully engage in the recovery process which can sometimes be painful of inconvenient. The application of wearable devices, games and Internet-of-Things (IoT) can create a motivating atmosphere to facilitate the rehabilitation process of patients while enabling remote monitoring of their health and progress. This paper presents the design and implementation of a rehabilitation system for aimed at helping stroke patients suffering from upper limb disability that exploits IoT by integrating gaming and wearable technology.

via Design and Implementation of a Wearable Device for Motivating Patients With Upper and/or Lower Limb Disability Via Gaming and Home Rehabilitation – IEEE Conference Publication

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[NEWS] Video Game-Integrated Training Device Helps Stroke Survivors Regain Arm Function

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A new video game-led training device called a myoelectric computer interface (MyoCI), invented by Northwestern Medicine scientists, is enabling severely impaired stroke survivors to regain function in their arms after sometimes decades of immobility.

When integrated with a customized video game, the device helped retrain stroke survivors’ arm muscles into moving more normally. Most of the 32 study participants experienced increased arm mobility and reduced arm stiffness while using it, and retained their arm function a month after finishing the training, according to a study published recently in Neurorehabilitation and Neural Repair.

Many stroke survivors can’t extend their arm forward with a straight elbow because the muscles act against one another in abnormal ways, called “abnormal co-activation” or “abnormal coupling.”

The Northwestern device identifies which muscles are abnormally coupled and retrains the muscles into moving normally by using their electrical muscle activity (called electromyogram, or EMG) to control a cursor in a customized video game. The more the muscles decouple, the higher the person’s score, a media release from Northwestern University explains.

“We gamified the therapy into an ’80s-style video game,” says senior author Dr Marc Slutzky, associate professor of neurology and of physiology at Northwestern University Feinberg School of Medicine and a Northwestern Medicine neurologist. “It’s rather basic graphics by today’s standards, but it’s entertaining enough.”

“The beauty of this is even if the benefit doesn’t persist for months or years, patients with a wearable device could do a ‘tune-up’ session every couple weeks, months or whenever they need it,” adds Slutzky, whose team designed the original device. “Long-term, I envision having flexible, fully wireless electrodes that an occupational therapist could quickly apply in their office, and patients could go home and train by themselves.”

Slutzky also is studying this method on stroke patients in the hospital, starting within a week of their stroke.

Abnormal coupling of muscles leaves many stroke patients with a bent elbow, which makes it difficult to benefit from typical task-based stroke-rehabilitation therapies, such as training on bathing, getting dressed and eating.

Only about 30% of stroke patients in the United States receive therapy after their initial in-patient rehabilitation stay, often because their injury is too severe to benefit from standard therapy, it costs too much, or they’re too far from a therapist. This small, preliminary study lays the groundwork for inexpensive, wearable, at-home therapy options for severely impaired stroke survivors, the release continues.

“We’re still in the very early stages, but I’m hopeful this may be an effective new type of stroke therapy,” Slutzky states. “The goal is to one day let patients buy the training device inexpensively, potentially without even needing insurance and use it wirelessly in their home.”

Patients in the study were severely impaired – could only slightly move their arm and extend their elbow – and had had their stroke at least 6 months prior to beginning the study. The average patient was more than 6 years out from their stroke, and some were decades out.

After Slutzky’s intervention, study participants could, on average, extend their elbow angle by 11 degrees more than before the intervention, which was a pleasant surprise, Slutzky comments.

This type of treatment only requires a small amount of muscle activation, which is advantageous for severely impaired stroke patients who typically can’t move enough to even begin standard physical therapy. It also gives feedback to the patient if they’re activating their muscles properly.

To identify which muscles were abnormally coupled, study participants attempted to reach out to multiple different targets while the scientists recorded the electrical activity in eight of their arm muscles using electrodes attached to the skin. For example, the biceps and anterior deltoid muscles in the arm often activated together in stroke participants, while they normally shouldn’t.

Then, to retrain the muscles into moving normally (ie, without abnormally co-activating), the participants used their electrical muscle activity to control a cursor in a customized video game. The two abnormally coupled muscles moved the cursor in either horizontal or vertical directions, in proportion to their EMG amplitude, the release continues.

For example, if the biceps would contract in isolation, the cursor would move up. If the anterior muscles would contract in isolation, the cursor would move to the side. But if the muscles would contract together, the cursor would move diagonally.

The goal was to move the cursor only vertically or horizontally – not diagonally – to acquire targets in the game. To get a high score, participants had to learn to decouple the abnormally coupled muscles.

Muscles tend to produce more electrical muscle activity when contracting isometrically (without moving) compared to when moving the arm freely, but the ultimate goal of this training is to enable home use. One goal of this study was to see if participants could benefit without restraining the arm as much as with restraining the arm.

Participants were broken into three groups: 60 minutes of training with their arm restrained; 90 minutes of training with their arm restrained; and 90 minutes of training without arm restraints. Overall, arm function improved substantially, in all groups and there was no significant difference between the three groups, the release concludes.

[Source(s): Northwestern University, News-Medical Life Sciences]

 

via Video Game-Integrated Training Device Helps Stroke Survivors Regain Arm Function – Rehab Managment

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[Proceeding] Mobile, Exercise-agnostic, Sensor-based Serious Games for Physical Rehabilitation at Home – Full Text PDF

Serious games can improve the physical rehabilitation of patients with different conditions. By monitoring exercises and offering feedback, serious games promote the correct execution of exercises outside the clinic. Nevertheless, existing serious games are limited to specific exercises, which reduces their practical impact. This paper describes the design of three exercise-agnostic games, that can be used for a multitude of rehabilitation scenarios. The developed games are displayed on a smartphone and are controlled by a wearable device, containing inertial and electromyography sensors. Results from a preliminary evaluation with 10 users are discussed, together with plans for future work.

Full Text PDF

via Mobile, Exercise-agnostic, Sensor-based Serious Games for Physical Rehabilitation at Home

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[WEB SITE] Deep Learning Device Can Predict Epileptic Seizures

Vanessa Geneva Ahern
JANUARY 29, 2018
predict seizure,signs seizure,epilepsy prediction,hca news

Imagine going about your daily life, working, shopping, and driving, knowing that you might have a seizure at any moment. But relief is on the horizon, as researchers from the University of Melbourne in Victoria, Australia have developed a potentially life-saving deep learning tool that can predict when an epileptic seizure is about to happen.

Their study was published in the journal eBioMedicine last month. The deep learning-based prediction system “achieved mean sensitivity of 69% and mean time warning of 27%, significantly surpassing an equivalent random predictor for all patients by 42%,” according to the findings.

Dean Freestone, PhD, senior research fellow at the department of medicine at St. Vincent’s Hospital at the University of Melbourne, says the tech could be contained in a chip inside a wearable device such as a wristband or bracelet, “incorporating a person’s behavior, environment, and physiology.” He and fellow co-author Mark J. Cook, MD, chair of medicine at St. Vincent’s Hospital, have launched a company named Seer Medical to pursue this technology. They hope to implant patients with the technology later this year.

“The technology is now proven. We have shown seizure prediction is possible in our previous paper published in Brain and in a Kaggle contest. This new study is just further backup,” Freestone says.

The advance could change the lives of many people with epilepsy, who worry about looming seizures while they are doing everyday activities. Patients who have tested the technology reported that they felt more in control when they used the wearable device and were more confident doing novel activities. They also claimed to have benefited from improved sleep and decision making.

The new forecasting technology would be best suited for someone having seizures once per week, according to the architects. If someone has seizures every hour, or if the seizures are too infrequent, it is difficult to train the algorithms, Freestone notes.

The way the predictive technology works is similar to Facebook’s facial recognition software. Instead of people in photos, the researchers have trained the algorithms to recognize patterns in the electrical activity of the brain that preempt seizures. “It is software that learns from example. The electrical patterns are very subtle and are invisible to the human eye, but the computer algorithms can identify them. The circadian patterns then help to boost the algorithms performance,” Freestone says.

“Patients can take action to actually prevent seizures. This could be in the form of a medication or even just a change in behavioral. We have also learnt a lot about the mechanisms of seizure, such as the strong influence of circadian cycles,” he adds.

Although significant cost and risk comes with new trials of medical devices, researchers are excited about the changes they can make. “We are working toward a system that will constantly provide a person with a risk level of seizure susceptibility,” Freestone says. “It will be a gauge that outputs a probability. We will incorporate as many aspects of a person’s behavior, environment and physiology as we can acquire from wearable technologies and other sensors.”

The findings came about, in part, thanks to the University of Melbourne’s large, long-term data set, which is unique and apt for exploring deep learning for seizure forecasting.

via Deep Learning Device Can Predict Epileptic Seizures | Healthcare Analytics News

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[WEB SITE] SMARTmove – FES

Summary

SMARTmove is a £1.1 million Medical Research Council research project running for 30 months from September 2016 to February 2019, funded under the Development Pathway Funding Scheme (DPFS). The project brings together a multidisciplinary team with expertise in functional materials, direct printing fabrication, control algorithms, wireless electronics, sensors, and end user engagement to address stroke rehabilitation. Working together with the advisory board members from six institutions, we will deliver a personalised wearable device for home-based stroke upper limb rehabilitation.

     

The Need

Stroke is one of the largest causes of disability: 17 million strokes occur every year worldwide, meaning one stroke every two seconds. Half of stroke survivors lose the ability to perform everyday tasks with their upper limb, which affects their independence. The cost to society in the UK is nine billion pounds per year due to health and social care, informal care, productivity loss and benefit payments. As stroke is an age-related disease, these numbers are set to increase as the population ages.

Novelty

Current commercial devices using functional electrical stimulation (FES) have large electrodes that only stimulate a limited number of muscles, resulting in simple, imprecise movements and the rapid onset of fatigue. In addition, current commercial devices do not employ feedback control to account for the movement of patients, only reducing the level of precision in the resulting movements. In addition, devices are either bulky and expensive, or difficult to set-up due to trailing wires.

Our project uses bespoke screen printable pastes to print electrode arrays directly onto everyday fabrics, such as those used in clothing. The resulting garments will have cutting-edge sensor technologies integrated into them. Advanced control algorithms will then adjust the stimulation based on the patients’ limb motion to enable precise functional movements, such as eating, washing or dressing.

Impact

This project will deliver a fabric-based wearable FES for home based stroke rehabilitation. The beneficiaries include:

  1. Persons with stroke (PwS) and other neurological conditions. Stroke survivors are the direct beneficiaries of our research. The FES clothing can be adapted to also treat hand/arm disabilities resulting from other neurological conditions such as cerebral palsy, head injury, spinal cord injury, and multiple sclerosis. The use of the wearable training system increases the intensity of rehabilitation without an increase in clinical contact time. This leads to better outcomes such as reduced impairment, greater restoration of function, improved quality of life and increased social activity.
  2. The NHS. FES-integrated clothing is comfortable to wear and convenient to use for rehabilitation, enabling impaired people to benefit from FES at home. It will transfer hospital based professional care to home based self-care, and therefore will reduce NHS costs by saving healthcare professionals’ time and other hospital resources.
  3. Industry. Benefits include: bringing business to the whole supply chain; increasing the FES market demand by improving performance; benefiting other industry sectors such as rehabilitation for other neurological conditions.
  4. Research communities in related fields. Specifically, the fields of novel fabrication, control systems, design of medical devices, rehabilitation, smart fabrics, and remote healthcare will benefit from the highly transformative platform technology (e.g. direct write printing, fabric electrodes, iterative learning control systems) developed in this work.

What is FES?

Functional electrical stimulation (FES) is a technique used to facilitate the practice of therapeutic exercises and tasks. Intensive movement practice can restore the upper limb function lost following stroke. However, stroke patients often have little or no movement, so are unable to practice. FES activates muscles artificially to facilitate task practise and improve patients’ movement.

More…..

Source: SMARTmove

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[ARTICLE] Real-time and wearable functional electrical stimulation system for volitional hand motor function control using the electromyography bridge method. – Full Text

Abstract

Voluntary participation of hemiplegic patients is crucial for functional electrical stimulation therapy. A wearable functional electrical stimulation system has been proposed for real-time volitional hand motor function control using the electromyography bridge method. Through a series of novel design concepts, including the integration of a detecting circuit and an analog-to-digital converter, a miniaturized functional electrical stimulation circuit technique, a low-power super-regeneration chip for wireless receiving, and two wearable armbands, a prototype system has been established with reduced size, power, and overall cost. Based on wrist joint torque reproduction and classification experiments performed on six healthy subjects, the optimized surface electromyography thresholds and trained logistic regression classifier parameters were statistically chosen to establish wrist and hand motion control with high accuracy. Test results showed that wrist flexion/extension, hand grasp, and finger extension could be reproduced with high accuracy and low latency. This system can build a bridge of information transmission between healthy limbs and paralyzed limbs, effectively improve voluntary participation of hemiplegic patients, and elevate efficiency of rehabilitation training.

Introduction

Functional electrical stimulation (FES) has been introduced as a neurorehabilitation method to artificially activate sensory and motor systems following central nervous system disease or injury, such as spinal cord injury and stroke (Popović, 2014; Shen et al., 2016; Wade and Gorgey, 2016). The first noninvasive FES system was used for foot drop correction of hemiplegic patients by Liberson et al. (1961). Many novel FES systems have been designed as surface or implantable stimulation systems for controlling arms and hands (Saxena et al., 1995; Ijzerman et al., 1996; Kilgore et al., 1997; Knutson et al., 2012; Hara et al., 2013).

The NESS Handmaster (Ijzerman et al., 1996) and the FES system (Nathan, 1989) belong to the push-button controlled FES method (or switch-based FES). Both of these methods use on/off stimulation with pre-programmed sequences to help spinal cord injury patients recover hand grasp movements and other daily functions. Electromyography (EMG) has been used for on/off control in EMG-triggered FES (Cauraugh et al., 2005) or proportional EMG-controlled FES (Saxena et al., 1995; Thorsen et al., 2001; Hara et al., 2013) and capitalizes on the principle of intension-driven motion. Therapeutic effects were reduced by approximately half if FES was applied without voluntary recipient involvement (Barsi et al., 2008). Preliminary results (McGie et al., 2015) suggest that motor-evoked potential of brain computer interface-controlled FES (Pfurtscheller et al., 2003) and EMG-controlled FES can elicit greater neuroplastic changes than conventional therapy. However, EMG-controlled FES requires some residual movement of the affected arm or hand, so it is not applicable with severely disabled stroke survivors. Contralaterally controlled FES is a promising therapy designed to improve recovery of paretic limbs after stroke. Two case series pilot studies (Knutson et al., 2009, 2014) and an early-phase randomized controlled trial (Knutson et al., 2012) verified the efficiency of contralaterally controlled FES. However, it is important for the success of FES therapy to include the contralateral limb in volitional control of electrically induced contraction in the affected limb.

Based on the success of volitional control of FES, our group previously designed an FES system for restoring motor function in post-stroke hemiplegic patients (Huang et al., 2014). In that system, a frequency-modulation stimulation algorithm based on surface EMG (sEMG) and the support vector machine model were used. However, sEMG thresholds need to be carefully chosen and force reproduction performance has not yet been established. The system is also too difficult to wear and remove.

The specific objectives of this paper were: (1) to use statistical experiments and analyses to optimize the primary parameter “sEMG thresholds” of the frequency-modulation stimulation generation algorithm formerly proposed by our group and to verify the force reproduction performance; (2) to develop a low-complexity algorithm based on logistic regression for hand movement classification achieved by these sEMG thresholds; (3) to develop a wireless and wearable FES system using the EMG-bridge method for real-time volitional hand motor function control, and to assess the feasibility of this system in real-time control of four hand movements. This novel system is a wearable EMG-bridge system that is distributed via a contralateral sEMG-controlled FES system providing more convenience to use at home. The size, power, and overall cost have been significantly reduced compared with the previous prototype (Huang et al., 2014).

Continue —> Real-time and wearable functional electrical stimulation system for volitional hand motor function control using the electromyography bridge method Wang Hp, Bi Zy, Zhou Y, Zhou Yx, Wang Zg, Lv Xy – Neural Regen Res

Figure 3: Prototype wearable EMG-bridge system.(A) The prototype wearable EMG-bridge system. (B) The radio frequency receiver board. (C) The self-designed integrated super-regenerative receiver chip in 0.35-mm complementary metal oxide semiconductor. (1) Transmitting wearable band; (2) receiving wearable band; (3) on-off keying circuit (radio frequency transmitter); (4) super-regenerative receiver circuit (radio frequency receiver); (5) interface between sEMG electrodes and sEMG signal detecting circuit; (6) interface between functional electrical stimulation circuit and gelled stimulation electrodes; (7) surface Ag/AgCl electrocardiogram electrodes for sEMG signal acquisitions; (8) gelled stimulation electrodes (4 × 4 cm<sup>2</sup>). sEMG: Surface electromyography.

Figure 3: Prototype wearable EMG-bridge system. (A) The prototype wearable EMG-bridge system. (B) The radio frequency receiver board. (C) The self-designed integrated super-regenerative receiver chip in 0.35-mm complementary metal oxide semiconductor. (1) Transmitting wearable band; (2) receiving wearable band; (3) on-off keying circuit (radio frequency transmitter); (4) super-regenerative receiver circuit (radio frequency receiver); (5) interface between sEMG electrodes and sEMG signal detecting circuit; (6) interface between functional electrical stimulation circuit and gelled stimulation electrodes; (7) surface Ag/AgCl electrocardiogram electrodes for sEMG signal acquisitions; (8) gelled stimulation electrodes (4 × 4 cm2). sEMG: Surface electromyography.

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[Cochrane Review] Activity monitors for increasing physical activity in adult stroke survivors – Full Text

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Abstract

This is a protocol for a Cochrane Review (Intervention). The objectives are as follows:

To summarise the available evidence regarding the effectiveness of commercially available wearable devices and smart phone applications for increasing physical activity levels for people with stroke.

Background

Description of the condition

Between 1990 and 2010 absolute numbers of people living with stroke increased by 84% worldwide, and stroke is now the third leading cause of disability globally (Feigin 2014). As such, the disease burden of stroke is substantial. It has been estimated that 91% of the burden of stroke is attributable to modifiable risk factors such as smoking, poor diet, and low levels of physical activity (Feigin 2016). A low level of physical activity (less than four hours per week) is the second highest population-attributable risk factor for stroke, second only to hypertension (O’Donnell 2016). The promotion of physical activity, which has been defined as body movement produced by skeletal muscles resulting in energy expenditure (Caspersen 1985), is therefore an important health intervention for people with stroke.

The association between health and physical activity is well established. Prolonged, unbroken bouts of sitting is a distinct health risk independent of time engaged in regular exercise (Healy 2008). There is evidence from cross-sectional and longitudinal studies that high sitting time and low levels of physical activity contribute to poor glycaemic control (Owen 2010). Three systematic reviews and meta-analyses of observational studies have confirmed that, after adjusting for other demographic and behavioural risk factors, physical activity is inversely associated with all-cause mortality in men and women (Nocon 2008; Löllgen 2009; Woodcock 2011). Yet despite this knowledge, populations worldwide are becoming more sedentary, and physical inactivity has been labelled a global pandemic (Kohl 2012).

In addition to overcoming the sedentary lifestyles and habits prevalent in many modern societies, people with stroke have additional barriers to physical activity such as weakness, sensory dysfunction, reduced balance, and fatigue (Billinger 2014). Directly after a stroke, people should be admitted to hospital for co-ordinated care and commencement of rehabilitation (SUTC 2013). Early rehabilitation after stroke is frequently focused on the recovery of physical independence (Pollock 2014). Recovery after stroke is enhanced by active practice of specific tasks, and greater improvements are seen when people with stroke spend more time in active practice (Veerbeek 2014). Yet findings from research conducted around the world indicate that people in the first few weeks and months after stroke are physically inactive in hospital settings with around 80% of the day spent inactive (sitting or lying) (West 2012). These high levels of inactivity are concerning because recovering the ability to walk independently is an important goal of people with stroke. The reported paucity of standing and walking practice in the early phase after stroke potentially limits the opportunities of people with stroke to optimise functional recovery, particularly for standing and walking goals. Further, physical inactivity may lead to an increased risk of hospital-acquired complications, such as pressure ulcers, pneumonia, and cardiac compromise (Lindgren 2004).

Physical activity levels of people with stroke remain lower than their age-matched counterparts even when they return to living in the community (English 2016). Community-dwelling stroke survivors spend the vast majority of their waking time sitting down (English 2014). Promisingly, early research suggests that increasing physical activity in people with stroke is feasible, and that an increase in physical activity levels after stroke may have a positive impact on fatigue, mood, community participation, and quality of life (QoL) (Graven 2011; Duncan 2015).

Continue —> Activity monitors for increasing physical activity in adult stroke survivors – Lynch – 2017 – The Cochrane Library – Wiley Online Library

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[Abstract] GEAR: A Mobile Game-Assisted Rehabilitation System

Abstract:

Rehabilitation exercises are an important means for gaining mobility and strength after injuries or surgery. Self-exercising in between physio-therapy sessions is vital for effective rehabilitation. Yet, many people do not follow exercise regimes, which can hamper their recovery. This study proposes GEAR – a mobile GamE Assisted Rehabilitation system – to engage users in self-exercising and to improve adherence to their exercise regime. The system consists of a wearable wristband to monitor users’ movements, a mobile game that incorporates the exercises, and a dashboard to monitor and visualize users’ exercise performance. GEAR has advantages of portability and lower cost as compared to PC or Kinect-based rehabilitation systems. This study describes GEAR and reports on a pilot assessment of its interface and system. The pilot test demonstrates the feasibility of GEAR and provides feedback that is being used to enhance the system prior to full-scale evaluation.

Source: GEAR: A Mobile Game-Assisted Rehabilitation System – IEEE Xplore Document

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[ppt presentation] PhysiotherAPPy with KinoHaptics – PDF file

Did you know… Studies show that up to 63% of patients in physical therapy do not complete all of their prescribed exercises

2016-02-20

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