Posts Tagged Training

[Abstract + References] Multi-modal Intent Recognition Method for the Soft Hand Rehabilitation Exoskeleton

Abstract

Stroke has become the second most disabling disease in the world. Due to the intensive demand for physical therapists and the severe dependence on hospitals, the cost for the treatment of stroke patients is huge. As the most flexible limb of the human body, the hand faces more severe challenges, which has a much lower degree of recovery than the upper and lower limbs. In the face of these challenges, a new treatment, exoskeleton-based rehabilitation, has demonstrated new vitality. This paper proposes a novel design of the soft hand exoskeleton based on bionics and anatomy and the exoskeleton could help the users bend and extend their fingers, which would greatly improve the motor ability of stroke patients. Through the control of the six drive motors, the exoskeleton could achieve most of the hand’s freedom of training. At the same time, we propose a multi-modal intent recognition method based on machine vision and machine speech. Under specific rehabilitation training scenarios, both healthy subjects and patients could complete grasping tasks in the wearing of the exoskeleton, overcoming potential security risks caused by misidentification due to using the single-modal intent understanding method.

References

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5. J. Yi, X. Chen and Z. Wang, “A three-dimensional- printed soft robotic glove with enhanced ergonomics and force capability”, IEEE Robotics and Automation Letters, vol. 3, no. 1, pp. 242-248, 2017. Show Context View Article Full Text: PDF (676KB) Google Scholar 

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8. L. Gerez and M. Liarokapis, “An underactuated tendon-driven wearable exo-glove with a four-output differential mechanism”, 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 6224-6228, 2019. Show Context View Article Full Text: PDF (3125KB) Google Scholar 

9. T. Bützer, J. Dittli, J. Lieber, H. J. van Hedel, A. Meyer-Heim, O. Lambercy, et al., “Pexo- a pediatric whole hand exoskeleton for grasping assistance in task-oriented training”, 2019 IEEE 16th International Conference on Rehabilitation Robotics (ICORR), pp. 108-114, 2019. Show Context View Article Full Text: PDF (1216KB) Google Scholar 

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11. M. Suarez-Escobar and E. Rendon-Velez, “An overview of robotic/mechanical devices for post-stroke thumb rehabilitation”, Disability and Rehabilitation: Assistive Technology, vol. 13, no. 7, pp. 683-703, 2018. Show Context CrossRef  Google Scholar 

12. M. Li, Z. Liang, B. He, C.-G. Zhao, W. Yao, G. Xu, et al., “Attention-controlled assistive wrist rehabilitation using a low-cost eeg sensor”, IEEE Sensors Journal, vol. 19, no. 15, pp. 6497-6507, 2019. Show Context View Article Full Text: PDF (4401KB) Google Scholar 

13. D. Huggins-Daines, M. Kumar, A. Chan, A. W. Black, M. Ravishankar and A. I. Rudnicky, “Pocketsphinx: A free real-time continuous speech recognition system for hand-held devices”, 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings, vol. 1, pp. I-I, 2006. Show Context View Article Full Text: PDF (89KB) Google Scholar 

14. J. Redmon, S. Divvala, R. Girshick and A. Farhadi, “You only look once: Unified real-time object detection”, Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 779-788, 2016. Show Context View Article Full Text: PDF (1742KB) Google Scholar 

Source: https://ieeexplore.ieee.org/abstract/document/9189174

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[Abstract] A Longitudinal Investigation of the Efficacy of Supported In-Home Post-Stroke Rehabilitation – Full Text PDF

Abstract

Owing to the population aging and a change in life patterns, the number of stroke incidents in China has increased dramatically with three million new occurrences every year. While the demand for rehabilitation services in China is enormous, the existing rehabilitation resources are not only disparately distributed but also unable to cope with the ever-increasing number of patients. In this paper, we present a study that focuses on a long-term limb function recovery program that uses a wearable body area network (WBAN) for in-home people with stroke discharged from hospital. The investigation lasted for 60 weeks and involved 12 people with stroke sparsely located in Jiaxing, Zhejiang province, China. These patients, who were all within their first 3 months from stroke event, were evenly and randomly split into the experiment group and the control group. The Brunnstrom Stage (BS) and the Mobility Index (MI) were used to monitor the patients’ recovery processes. The results demonstrated that the patients from the experimental group had experienced a steady increase in MI throughout the program. They also had improved BS by at least one stage, which outperformed the control group in average. This longitudinal efficacy investigation indicates that the supported in-home rehabilitation system has the potential to reduce the cost and the effort for patients and doctors while still maintaining the quality and effectiveness of rehabilitation; hence could be a possible solution in alleviating the great stress that the healthcare system is currently experiencing in China nationwide.

via A Longitudinal Investigation of the Efficacy of Supported In-Home Post-Stroke Rehabilitation – IEEE Journals & Magazine

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[Abstract + References] Feasibility of Wearable Sensing for In-Home Finger Rehabilitation Early After Stroke

Abstract

Wearable grip sensing shows potential for hand rehabilitation, but few studies have studied feasibility early after stroke. Here, we studied a wearable grip sensor integrated with a musical computer game (MusicGlove). Among the stroke patients admitted to a hospital without limiting complications, 13% had adequate hand function for system use. Eleven subjects used MusicGlove at home over three weeks with a goal of nine hours of use. On average they achieved 4.1 ± 3.2 (SD) hours of use and completed 8627 ± 7500 grips, an amount comparable to users in the chronic phase of stroke measured in a previous study. The rank-order usage data were well fit by distributions that arise in machine failure theory. Users operated the game at high success levels, achieving note-hitting success >75% for 84% of the 1061 songs played. They changed game parameters infrequently (31% of songs), but in a way that logically modulated challenge, consistent with the Challenge Point Hypothesis from motor learning. Thus, a therapy based on wearable grip sensing was feasible for home rehabilitation, but only for a fraction of subacute stroke subjects. Subjects made usage decisions consistent with theoretical models of machine failure and motor learning.

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15. M. T. Jurkiewicz, S. Marzolini and P. Oh, “Adherence to a home-based exercise program for individuals after stroke”, Topics Stroke Rehabil., vol. 18, no. 3, pp. 277-284, May 2011.

16. A. Barzel et al., “Comparison of two types of constraint-induced movement therapy in chronic stroke patients: A pilot study”, Restorative Neurol. Neurosci., vol. 27, no. 6, pp. 675-682, 2009.

17. A. Barzel et al., “Enhancing activities of daily living of chronic stroke patients in primary health care by modified constraint-induced movement therapy (HOMECIMT): Study protocol for a cluster randomized controlled trial”, Trials, vol. 14, no. 1, pp. 334, 2013.

18. L. Dodakian et al., “A home-based telerehabilitation program for patients with stroke”, Neurorehabilitation Neural Repair, vol. 31, no. 10, pp. 923-933, Oct. 2017.

19. S. C. Cramer et al., “Efficacy of home-based telerehabilitation vs in-clinic therapy for adults after stroke: A randomized clinical trial”, J. Amer. Med. Assoc. Neurol., vol. 76, no. 9, pp. 1079, Sep. 2019.

20. K. E. Laver, D. Schoene, M. Crotty, S. George, N. A. Lannin and C. Sherrington, “Telerehabilitation services for stroke”, Cochrane Database Systematic Rev., vol. 2013, no. 12, pp. 1-48, 2013.

21. Y. Hara, S. Ogawa, K. Tsujiuchi and Y. Muraoka, “A home-based rehabilitation program for the hemiplegic upper extremity by power-assisted functional electrical stimulation”, Disability Rehabil., vol. 30, no. 4, pp. 296-304, Jan. 2008.

22. E. V. Donoso Brown, S. W. McCoy, A. S. Fechko, R. Price, T. Gilbertson and C. T. Moritz, “Preliminary investigation of an electromyography-controlled video game as a home program for persons in the chronic phase of stroke recovery”, Arch. Phys. Med. Rehabil., vol. 95, no. 8, pp. 1461-1469, Aug. 2014.

23. M. King, J. Hijmans, M. Sampson, J. Satherley and L. Hale, “Home-based stroke rehabilitation using computer gaming”, New Zeal. J. Physiother., vol. 40, no. 3, pp. 128-134, 2012.

24. A. Slijper, K. E. Svensson, P. Backlund, H. Engström and K. Sunnerhagen, “Computer game-based upper extremity training in the home environment in stroke persons: A single subject design”, J. NeuroEng. Rehabil., vol. 11, no. 1, pp. 35, 2014.

25. M. Villeneuve and A. Lamontagne, “Playing piano can improve upper extremity function after stroke: Case studies”, Stroke Res. Treat., vol. 2013, pp. 1-5, Feb. 2013.

26. J. M. Hijmans, L. A. Hale, J. A. Satherley, N. J. Mcmillan and M. J. King, “Bilateral upper-limb rehabilitation after stroke using a movement-based game controller”, J. Rehabil. Res. Develop., vol. 48, no. 8, pp. 1005, 2011.

27. J. Yoo, “The role of therapeutic instrumental music performance in hemiparetic arm rehabilitation”, Music Therapy Perspect., vol. 27, no. 1, pp. 16-24, Jan. 2009.

28. P. Rinne et al., “Democratizing neurorehabilitation: How accessible are low-cost mobile-gaming technologies for self-rehabilitation of arm disability in stroke?”, PLoS ONE, vol. 11, no. 10, 2016.

29. N. Friedman, V. Chan, D. Zondervan, M. Bachman and D. J. Reinkensmeyer, “MusicGlove: Motivating and quantifying hand movement rehabilitation by using functional grips to play music”, Proc. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc., pp. 2359-2363, Aug. 2011.

30. N. Friedman et al., “Retraining and assessing hand movement after stroke using the MusicGlove: Comparison with conventional hand therapy and isometric grip training”, J. NeuroEng. Rehabil., vol. 11, no. 1, pp. 76, 2014.

31. D. K. Zondervan et al., “Home-based hand rehabilitation after chronic stroke: Randomized controlled single-blind trial comparing the MusicGlove with a conventional exercise program”, J. Rehabil. Res. Develop., vol. 53, no. 4, pp. 457-472, 2016.

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37. P. Rinne et al., “Democratizing neurorehabilitation: How accessible are low-cost mobile-gaming technologies for self-rehabilitation of arm disability in stroke?”, PLoS ONE, vol. 11, no. 10, 2016.

38. X. L. Hu, K.-Y. Tong, R. Song, X. J. Zheng and W. W. F. Leung, “A comparison between electromyography-driven robot and passive motion device on wrist rehabilitation for chronic stroke”, Neurorehabilitation Neural Repair, vol. 23, no. 8, pp. 837-846, Oct. 2009.

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40. S. L. Norman et al., “Controlling pre-movement sensorimotor rhythm can improve finger extension after stroke”, J. Neural Eng., vol. 15, no. 5, Oct. 2018.

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via Feasibility of Wearable Sensing for In-Home Finger Rehabilitation Early After Stroke – IEEE Journals & Magazine

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[ARTICLE] Self-Support Biofeedback Training for Recovery From Motor Impairment After Stroke – Full Text

Abstract

Unilateral arm paralysis is a common symptom of stroke. In stroke patients, we observed that self-guided biomechanical support by the nonparetic arm unexpectedly triggered electromyographic activity with normal muscle synergies in the paretic arm. The muscle activities on the paretic arm became similar to the muscle activities on the nonparetic arm with self-supported exercises that were quantified by the similarity index (SI). Electromyogram (EMG) signals and functional near-infrared spectroscopy (fNIRS) of the patients (n=54) showed that self-supported exercise can have an immediate effect of improving the muscle activities by 40–80% according to SI quantification, and the muscle activities became much more similar to the muscle activities of the age-matched healthy subjects. Using this self-supported exercise, we investigated whether the recruitment of a patient’s contralesional nervous system could reactivate their ipsilesional neural circuits and stimulate functional recovery. We proposed biofeedback training with self-supported exercise where the muscle activities were visualized to encourage the appropriate neural pathways for activating the muscles of the paretic arm. We developed the biofeedback system and tested the recovery speed with the patients (n=27) for 2 months. The clinical tests showed that self-support-based biofeedback training improved SI approximately by 40%, Stroke Impairment Assessment Set (SIAS) by 35%, and Functional Independence Measure (FIM) by 20%.

Introduction

Stroke is the leading cause of long-term disability worldwide. Of more than 750,000 stroke victims in the United States each year [1], approximately two-thirds survive and require immediate rehabilitation to recover lost brain functions [2]. These stroke rehabilitation programs, of which direct and indirect costs were estimated to be 73.7 billion dollars in 2010 [3], aim to help survivors gain physical independence and better quality of life.

Stroke damage typically interrupts blood flow within one brain hemisphere, resulting in unilateral motor deficits, sensory deficits, or both. The preservation of long-term neural and synaptic plasticity is essential for the functional reorganization and recovery of neural pathways disrupted by stroke [4]–[5][6]. Stroke survivors typically require long-term, intensive rehabilitation training due to the length of time required for these recovery processes [7], [8]. The typical time course for partial recovery of arm movement after mild to moderate unilateral stroke damage is 2 to 6 months, depending on the severity of tissue damage and the latency of treatment initiation [9], [10]; however, patients with severe damage require additional months to years of rehabilitation. Given the economic burden on patients’ families and the medical system, novel rehabilitation methods that promote rapid and complete functional recovery are needed, along with a better understanding of the functional mechanisms and neural circuits that can participate in potential therapeutic processes. The identification of rehabilitation methods that can more effectively recover brain functions in the damaged hemisphere by re-engaging dormant motor functions should be a major global objective, from both economic and societal perspectives. Such an objective would require the interface of biology, medical research, and clinical practice [4].

Recently, candidate brain areas that become activated during stroke recovery have been identified in patients and animal models [7]. Brain imaging studies during stroke recovery suggest that the extent of functional motor recovery is associated with an increase in neuronal activity in the sensorimotor cortex of the ipsilesional hemisphere [10]–[11][12]. Other work has suggested that repetitive sensorimotor tasks may promote cortical reorganization and functional recovery in the ipsilesional area by increasing bilateral cortical activity to enhance neuroplasticity [13]. Activation in the contralesional hemisphere is also observed in the early stages of post-stroke patients. This activation has been explained by the emergence of communication in corticospinal projections that are silent in the healthy state [11], and it may also contribute to movement-related neural activity on the ipsilesional limb [14], [15]. Functional brain imaging studies show that activity of the contralesional hemisphere is increased early after stroke and gradually declines as recovery progresses [16]. The functional relevance of contralesional recruitment remains unclear [17], [18]. Some reported studies have linked high abnormal activity to a high inhibitory signaling drive onto the ipsilesional cortex [19], which may be a major contributor to motor impairment [6], [20]. Recent studies have also investigated the benefits of activating the contralesional and/or ipsilesional hemispheres in functional motor recovery using brain-computer interface (BCI) and transcranial magnetic stimulation (TMS) therapies [21], [22].

Current stroke rehabilitation approaches have largely focused on paretic limb rehabilitation interventions such as muscle strengthening and endurance training [23], forced-use therapy [24], constraint-induced exercise [25], robot therapy with biofeedback [26], nonparetic limb interventions (e.g., mirror-therapy [27], [28]), or bilateral/bimanual training [29], [30]. However, to date, none have clearly investigated how the use of a patient’s unaffected neural circuits in the healthy cortical hemisphere, or in the local peripheral circuit, affect the impaired limb in terms of functional rehabilitation of the bilateral cortical sensorimotor network [31].

In this study, we investigated a motor recovery approach for post-stroke unilateral arm impairment that combined sensory feedback, motor control, and motor intention. While observing a patient cohort with unilateral stroke damage and arm movement impairment, we found that a specific self-guided motion, which we termed self-supported exercise, surprisingly reactivated a healthy muscles pattern in the paretic arm. The key of the self-supported exercise is use of the nonparetic arm as a support to help move the paretic arm. First, we will show the observation of appropriate muscle recruitment and reduction of abnormal muscle synergies for post-stroke patients during the self-supported exercise, which are a common problem in stroke recovery [32]. Then, we conduct the experiments of functional imaging and electromyography recordings and characterized the neurobiology and physiology of this self-supported exercise. Based on this mechanism, we designed a rehabilitation program involving biofeedback-aided self-supported exercises that employ a patients’ self-initiated motor intention. The results of the comparative experiments between the feedback training cohorts and the control cohorts show that this method results in efficient recovery from post-stroke motion paralysis. Finally, we discuss the significance of our findings for the design of biologically-based stroke rehabilitation.[…]

via Self-Support Biofeedback Training for Recovery From Motor Impairment After Stroke – IEEE Journals & Magazine

FIGURE 2. - The four types of exercises.

FIGURE 2.The four types of exercises.

 

 

 

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[WEB SITE] Top 10 Virtual Reality Applications In Today’s World

The premise of virtual reality has always been exciting. Slip-on a pair of goggles or a headset, and you’re on your way to another world. Unlike at the cinema or in front of your TV screen, you’re free to interact with your surroundings and wander wherever you please.

VR exploded into public consciousness around the same time as the personal computer in the late ‘80s and early ‘90s. Back in those days, however, the difference between the real world and the pixel-heavy digital landscapes of the time was too great for everyone but for the most hardcore fans. As a result, VR was more or less put on the backburner for a couple of decades.

However, the birth of the Oculus Rift in 2012 and its subsequent purchase by Facebook in 2014 led to a renewal of interest in virtual reality applications. It also served as an uptick in virtual reality recruitment as companies cottoned on to the medium’s vast potential.

How Does Virtual Reality Work?

So, how does virtual reality work? Nowadays, virtual reality is implemented using computer technology via tools such as headsets, goggles, treadmills, and handsets. These tools stimulate our senses to create an illusion of reality. This is far more complicated than it seems: human physiology is calibrated to provide a finely synchronized experience, and if anything is ‘odd’, our bodies will usually let us know via unpleasant sensations such as nausea or motion sickness. A successful virtual reality experience involves careful synchronicity of software, hardware, and of our senses. The most memorable virtual reality uses are those that enable us to interact naturally with our surroundings with no latency or glitches that could create a feeling of artificiality.

This leads us to ask ourselves, “Why do we go to all that trouble to create these highly technical worlds that just aim to imitate reality?” The truth is, virtual reality applications are numerous and beneficial across many fields.

How Does Virtual Reality Work?
Photo by Sales on New Gen Apps

 

Ten Most Exciting Applications Of Virtual Reality

1.  Entertainment

Entertainment is an obvious application of virtual reality. Who wouldn’t want to slip on a headset and escape into another world?

The first thing that comes to mind is gaming. It is a historical virtual reality application that is still very much among the main VR uses today. Other entertainment forms are however hot on its heels. While 3D cinema has been around for quite a while now, the rise of VR headsets is providing users with immersive cinema experiences without them even having to leave the house. Apps such as Oculus Cinema enable viewers to watch movies on their very own virtual screen. At the same time, developers are working on software that will enable sports fans to cheer on their favorite teams from the comfort of their couches. An example is LiveLike VR’s virtual stadium.

Using virtual reality, music lovers can attend concerts and festivals taking place on the other side of the world. Moreover, those who have been bitten by the travel bug can wander sunny beaches without leaving their front yard. What’s not to love?

Virtual Reality In Entertainment
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2.  Training

It is essential for military personnel to gain first-hand experience of the terrain of their deployment. Likewise, when you get on a commercial flight, you assume your pilot has mastered the aircraft and can respond appropriately in any kind of emergency. But have you ever wondered how rookie soldiers and pilots get in their training hours without putting themselves in danger?

Some activities are just too dangerous, impractical, or expensive for beginners to be able to practice them from the get-go. This is where VR comes in. Virtual reality education companies offer software aimed at training new personnel. The US military uses virtual reality simulators to train soldiers before deployment. These VR simulators enable them to practice working together in the kind of environments they will come up against. Likewise, flight simulators are used to train new pilots or refresh their knowledge before they can get before the controls of a real-life plane.

Virtual Reality In Training
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 3. Healthcare

With elevated costs, a tendency towards personnel shortages, and people’s lives at stake, the healthcare industry is generally quick to adopt exciting new tech that can boost efficiency and improve performance. Virtual reality is no exception.

Medical institutions can use VR to make diagnoses and define treatments. Some VR simulators are now able to use images from MRIs or CAT scans to create 3D models of a patient’s anatomy. The training applications of virtual reality enable trainee doctors to practice surgery with no risk to patients. Moreover, they can help more experienced ones to determine the safest way to operate.

There are other interesting virtual reality applications in the healthcare industry beyond surgery and diagnoses. For rehabilitation of stroke and brain injury victims, healthcare industries use VR. It provides virtual exercises to help patients gain independence in everyday activities, aided by real-time feedback.

Virtual Reality In Healthcare
Photo by Reenita Das on Forbes

 

4. The Arts

Fans of the performing arts will probably find the idea of a screen between the artist and audience a strange one. However, there are many exciting virtual reality applications when it comes to theatre, opera, dance, circus, and other performing art forms that are characterized by their fleetingness. That is, you have to be there on the night, or else, it’s gone forever. VR enables you to watch a live performance at any time you please. You can even be in the best seat in your house. Why not even from the middle of the stage if you’re feeling adventurous?

There are many other virtual reality applications when it comes to the arts. Directors can create a stage set before they build it. Applications such as Tvori enable you to create 3D animations that you can walk around. The possibilities are endless!

Virtual Reality In The Arts
Photo by Mark Foster on Unilad

 

5. Meditation

According to the World Health Organization, stress is the health epidemic of the 21st century. Furthermore, many seem to think that tech is part of the problem. But what if it was also part of the solution? Apps along the lines of Calm and Headspace already enable you to take a break wherever you happen to be. Moreover, VR is promising to add an extra dimension to your meditation experience.

One of the hardest things about meditation for people who are just starting their meditation journey is learning to just “let go”. Virtual reality meditation apps make that all easier by allowing you to slip on your headset and instantly slip into another world.

Virtual Reality in Meditation
Photo by Michael Gollust on Health

 

6. Mental Health

Virtual reality applications help you relax and let go. Likewise, its applications include therapeutic tools for people who have been through traumatizing experiences or suffer from debilitating stress, PTSD, or phobias.

Virtual reality can provide a safe virtual environment. This enables patients to come into contact with the source of their phobias or fears without endangering themselves. Moreover, interesting advances have already made notably in the field of treatment for war veterans suffering from PTSD.

Benefits Of Using Virtual Reality For Mental Health
Photo by Abbie Arce on LabRoots

 

7. Marketing

AI-powered data analysis is enabling digital marketers to tailor experiences to fit individual tastes like never before. At the same time, consumers are constantly bombarded with advertising. It means that banner blindness is becoming a real problem – and that’s before we even mention adblockers.

VR is a gamechanger for marketers. It enables them to provide exciting, immersive experiences with high entertainment value. In the UK, the cheese manufacturer, Boursin, recently offered a delightful virtual reality exhibit. Users were taken on a journey through a fridge filled with tasty treats, complete with wind simulators for an even more immersive experience.

8. Shopping

Imagine that you’re wandering through a fashionable SoHo boutique looking to pick out a new accessory, and at the same time on your couch several hundred miles away in your pajamas.

Online retailers are now part and parcel of our day-to-day life and are looking to get a make-over. Thanks to the power of VR. The VR start-up Trillenium creates virtual stores for online retailers and has already partnered with the likes of ASOS, one of Europe’s biggest online retailers. Instead of clicking their way through online catalogs, shoppers can go on a virtual tour of a store for a real-time shopping experience. They can even share it with their friends.

9. Journalism

Another exciting virtual reality application is on journalism and online media. VR is enabling media outlets to create immersive storytelling experiences that give the viewer the impression of truly being part of the action. Major players such as the Washington Post and the New York Times are now entering the VR field by offering 360° reports and documentaries. The New York Times made a big splash in 2014 by sending Google Cardboard headsets to its subscribers for them to use with their smartphones.

 10. Architecture

Another exciting application of virtual reality is in architecture. This is for being able to offer their clients virtual walkthroughs, a great way for firms to showcase their projects compared to more traditional 3D projection. It is by giving clients a true sense of space and design.

The potential uses of virtual reality are widespread and diverse, spanning everything from entertainment to healthcare and from journalism to digital marketing. With technology becoming cheaper and more widely available, we can expect to see many more exciting virtual reality applications in the years to come. Stay tuned!

 

via Top 10 Virtual Reality Applications In Today’s World | Robots.net

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[Abstract] Remote Upper Limb Exoskeleton Rehabilitation Training System Based on Virtual Reality

Abstract

According to the present situation that the treatment means for apoplectic patients is lagging and weak, a set of long-distance exoskeleton rehabilitation training system with 5 DOF for upper limb was developed. First, the mechanical structure and control system of the training system were designed. Then a new kind of building method for virtual environment was proposed. The method created a complex model effectively with good portability. The new building method was used to design the virtual training scenes for patients’ rehabilitation in which the virtual human model can move following the trainer on real time, which can reflect the movement condition of arm of patient factually and increase the interest of rehabilitation training. Finally, the network communication technology was applied into the training system to realize the remote communication between the client-side of doctor and training system of patient, which makes it possible to product rehabilitation training at home.

via Remote Upper Limb Exoskeleton Rehabilitation Training System Based on Virtual Reality – IEEE Conference Publication

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

Abstract

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.

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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.

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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.

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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

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[Abstract + References] Electromyographic indices of muscle fatigue of a severely paralyzed chronic stroke patient undergoing upper limb motor rehabilitation

Abstract

Modern approaches to motor rehabilitation of severe upper limb paralysis in chronic stroke decode movements from electromyography for controlling rehabilitation orthoses. Muscle fatigue is a phenomenon that influences these neurophysiological signals and may diminish the decoding quality. Characterization of these potential signal changes during movement patterns of rehabilitation training could therefore help improve the decoding accuracy. In the present work we investigated how electromyographic indices of muscle fatigue in the Deltoid Anterior muscle evolve during typical forward reaching movements of a rehabilitation training in healthy subjects and a stroke patient. We found that muscle fatigue in healthy subjects changed the neurophysiological signal. In the patient, however, no consistent change was observed over several sessions.
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3. A. Sarasola-Sanz et al , “A hybrid brain-machine interface based on EEG and EMG activity for the motor rehabilitation of stroke patients,” IEEE Int Conf Rehabil Robot, vol. 2017, pp. 895–900, Jul. 2017.

4. R. M. Enoka and J. Duchateau , “Muscle fatigue: what, why and how it influences muscle function,” J Physiol, vol. 586, no. 1, pp. 11–23, Jan. 2008.

5. M. González-Izal , A. Malanda , E. Gorostiaga , and M. Izquierdo , “Electromyographic models to assess muscle fatigue,” J. Electromyogr. Kinesiol., vol. 22, no. 4, pp. 501–512, Aug. 2012.

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via Electromyographic indices of muscle fatigue of a severely paralyzed chronic stroke patient undergoing upper limb motor rehabilitation – IEEE Conference Publication

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[Abstract] The SonicHand Protocol for Rehabilitation of Hand Motor Function: a validation and feasibility study

Abstract

Musical sonification therapy is a new technique that can reinforce conventional rehabilitation treatments by increasing therapy intensity and engagement through challenging and motivating exercises. Aim of this study is to evaluate the feasibility and validity of the SonicHand protocol, a new training and assessment method for the rehabilitation of hand function. The study was conducted in 15 healthy individuals and 15 stroke patients. The feasibility of implementation of the training protocol was tested in stroke patients only, who practiced a series of exercises concurrently to music sequences produced by specific movements. The assessment protocol evaluated hand motor performance during pronation/supination, wrist horizontal flexion/extension and hand grasp without sonification. From hand position data, 15 quantitative parameters were computed evaluating mean velocity, movement smoothness and angular excursions of hand/fingers. We validated this assessment in terms of its ability to discriminate between patients and healthy subjects, test-retest reliability and concurrent validity with the upper limb section of the Fugl-Meyer scale (FM), the Functional Independence Measure (FIM) and the Box & Block Test (BBT). All patients showed good understanding of the assigned tasks and were able to correctly execute the proposed training protocol, confirming its feasibility. A moderate-to-excellent intraclass correlation coefficient was found in 8/15 computed parameters. Moderate-to-strong correlation was found between the measured parameters and the clinical scales. The SonicHand training protocol is feasible and the assessment protocol showed good to excellent between-group discrimination ability, reliability and concurrent validity, thus enabling the implementation of new personalized and motivating training programs employing sonification for the rehabilitation of hand function.

via The SonicHand Protocol for Rehabilitation of Hand Motor Function: a validation and feasibility study – IEEE Journals & Magazine

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[Abstract] A Greedy Assist-as-Needed Controller for Upper Limb Rehabilitation

Abstract

Previous studies on robotic rehabilitation have shown that subjects’ active participation and effort involved in rehabilitation training can promote the performance of therapies. In order to improve the voluntary effort of participants during the rehabilitation training, assist-as-needed (AAN) control strategies regulating the robotic assistance according to subjects’ performance and conditions have been developed. Unfortunately, the heterogeneity of patients’ motor function capability in task space is not taken into account during the implementation of these controllers. In this paper, a new scheme called greedy AAN (GAAN) controller is designed for the upper limb rehabilitation training of neurologically impaired subjects. The proposed GAAN control paradigm includes a baseline controller and a Gaussian RBF network that is utilized to model the functional capability of subjects and to provide corresponding a task challenge for them. In order to avoid subjects’ slacking and encourage their active engagement, the weight vectors of RBF networks evaluating subjects’ impairment level are updated based on a greedy strategy that makes the networks progressively learn the maximum forces over time provided by subjects. Simultaneously, a challenge level modification algorithm is employed to adjust the task challenge according to the task performance of subjects. Experiments on 12 subjects with neurological impairment are conducted to validate the performance and feasibility of the GAAN controller. The results show that the proposed GAAN controller has significant potential to promote the subjects’ voluntary engagement during training exercises.

via A Greedy Assist-as-Needed Controller for Upper Limb Rehabilitation – IEEE Journals & Magazine

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