Posts Tagged TASK ANALYSIS

[Abstract + References] A Virtual Reality Serious Game for Hand Rehabilitation Therapy – IEEE Conference Publication

Abstract

The human hand is the body part most frequently injured in occupational accidents, accounting for one out of five emergency cases and often requiring surgery with subsequently long periods of rehabilitation. This paper proposes a Virtual Reality game to improve conventional physiotherapy in hand rehabilitation, focusing on resolving recurring limitations reported in most technological solutions to the problem, namely the limited diversity support of movements and exercises, complicated calibrations and exclusion of patients with open wounds or other disfigurements of the hand. The system was assessed by seven able-bodied participants using a semistructured interview targeting three evaluation categories: hardware usability, software usability and suggestions for improvement. A System Usability Score (SUS) of 84.3 and participants’ disposition to play the game confirm the potential of both the conceptual and technological approaches taken for the improvement of hand rehabilitation therapy.

References

1. A. Elnaggar and D. Reichardt, “Digitizing the Hand Rehabilitation Using Serious Games Methodology with User-Centered Design Approach”, 2016 International Conference on Computational Science and Computational Intelligence (CSCI), pp. 13-22, 2016. Show Context View Article Full Text: PDF (1150KB) Google Scholar 

2. L. S. Robinson, M. Sarkies, T. Brown and L. O’Brien, “Direct indirect and intangible costs of acute hand and wrist injuries: A systematic review”, Injury, vol. 47, no. 12, pp. 2614-2626, Dec. 2016. Show Context CrossRef  Google Scholar 

3. D. Johnson, S. Deterding, K.-A. Kuhn, A. Staneva, S. Stoyanov and L. Hides, “Gamification for health and wellbeing: A systematic review of the literature”, Internet Interv., vol. 6, pp. 89-106, Nov. 2016. Show Context CrossRef  Google Scholar 

4. C. Prahm, “PlayBionic Interactive rehabilitation after amputation or nerve injury of the upper extremity”, Christian Doppler Laboratory for Restoration of Extremity Function and Rehabilitation, 2019. Show Context Google Scholar 

5. M. K. Holden, “Virtual Environments for Motor Rehabilitation: Review”, CyberPsychology Behav., vol. 8, no. 3, pp. 187-211, Jun. 2005. Show Context CrossRef  Google Scholar 

6. D. Ganjiwale, R. Pathak, A. Dwivedi, J. Ganjiwale and S. Parekh, “Occupational therapy rehabilitation of industrial setup hand injury cases for functional independence using modified joystick in interactive computer gaming in Anand Gujarat”, Natl. J. Physiol. Pharm. Pharmacol., vol. 9, pp. 1, 2018. Show Context CrossRef  Google Scholar 

7. H. A. Hernández, A. Khan, L. Fay, Je.-S. Roy and E. Biddiss, “Force Resistance Training in Hand Grasp and Arm Therapy: Feasibility of a Low-Cost Videogame Controller”, Games Health J., vol. 7, no. 4, pp. 277-287, Aug. 2018. Show Context CrossRef  Google Scholar 

8. J. Broeren, L. Claesson, D. Goude, M. Rydmark and K. S. Sunnerhagen, “Virtual Rehabilitation in an Activity Centre for Community-Dwelling Persons with Stroke”, Cerebrovasc. Dis., vol. 26, no. 3, pp. 289-296, 2008. Show Context CrossRef  Google Scholar 

9. J. Broeren, M. Rydmark and K. S. Sunnerhagen, “Virtual reality and haptics as a training device for movement rehabilitation after stroke: A single-case study”, Arch. Phys. Med. Rehabil., vol. 85, no. 8, pp. 1247-1250, Aug. 2004. Show Context CrossRef  Google Scholar 

10. C. N. Walifio-Paniagua et al., “Effects of a Game-Based Virtual Reality Video Capture Training Program Plus Occupational Therapy on Manual Dexterity in Patients with Multiple Sclerosis: A Randomized Controlled Trial”, J. Healthc. Eng., vol. 2019, pp. 1-7, Apr. 2019. Show Context CrossRef  Google Scholar 

11. M. E. Gabyzon, B. Engel-Yeger, S. Tresser and S. Springer, “Using a virtual reality game to assess goal-directed hand movements in children: A pilot feasibility study”, Technol. Heal. Care, vol. 24, no. 1, pp. 11-19, Jan. 2016. Show Context CrossRef  Google Scholar 

12. M. King, L. Hale, A. Pekkari, M. Persson, M. Gregorsson and M. Nilsson, “An affordable computerised table-based exercise system for stroke survivors”, Disabil. Rehabil. Assist. Technol., vol. 5, no. 4, pp. 288-293, Jul. 2010. Show Context CrossRef  Google Scholar 

13. J. Shin et al., “Effects of virtual reality-based rehabilitation on distal upper extremity function and health-related quality of life: a single-blinded randomized controlled trial”, J. Neuroeng. Rehabil., vol. 13, no. 1, pp. 17, Dec. 2016. Show Context CrossRef  Google Scholar 

14. R. Lipovsky and H. A. Ferreira, “Hand therapist: A rehabilitation approach based on wearable technology and video gaming”, 2015 IEEE 4th Portuguese Meeting on Bioengineering (ENBENG), pp. 1-2, February 2015. Show Context View Article Full Text: PDF (1901KB) Google Scholar 

15. C. Schuster-Amft et al., “Using mixed methods to evaluate efficacy and user expectations of a virtual reality-based training system for upper-limb recovery in patients after stroke: a study protocol for a randomised controlled trial”, Trials, vol. 15, no. 1, pp. 350, Dec. 2014. Show Context CrossRef  Google Scholar 

16. Y. A. Rahman, M. M. Hoque, K. I. Zinnah and I. M. Bokhary, “Helping-Hand: A data glove technology for rehabilitation of monoplegia patients”, 2014 9th International Forum on Strategic Technology (IFOST), pp. 199-204, 2014. Show Context View Article Full Text: PDF (625KB) Google Scholar 

17. M. da Silva Cameirão, S. Bermúdez, I Badia, E. Duarte and P. F. M. J. Verschure, “Virtual reality based rehabilitation speeds up functional recovery of the upper extremities after stroke: A randomized controlled pilot study in the acute phase of stroke using the Rehabilitation Gaming System”, Restor. Neurol. Neurosci., vol. 29, no. 5, pp. 287-298, 2011. Show Context CrossRef  Google Scholar 

18. M. R. Golomb et al., “In-Home Virtual Reality Videogame Telerehabilitation in Adolescents With Hemiplegic Cerebral Palsy”, Arch. Phys. Med. Rehabil., vol. 91, no. 1, pp. 1-8, Jan. 2010. Show Context CrossRef  Google Scholar 

19. R. Proffitt, M. Sevick, C.-Y. Chang and B. Lange, “User-Centered Design of a Controller-Free Game for Hand Rehabilitation”, Games Health J., vol. 4, no. 4, pp. 259-264, Aug. 2015. Show Context CrossRef  Google Scholar 

20. N. Arman, E. Tarakci, D. Tarakci and O. Kasapcopur, “Effects of Video Games-Based Task-Oriented Activity Training (Xbox 360 Kinect) on Activity Performance and Participation in Patients with Juvenile Idiopathic Arthritis: A Randomized Clinical Trial”, Am. J. Phys. Med. Rehabil., vol. 98, no. 3, pp. 174-181, 2019. Show Context CrossRef  Google Scholar 

21. S. Cho, W.-S. Kim, N.-J. Paik and H. Bang, “Upper-Limb Function Assessment Using VBBTs for Stroke Patients”, IEEE Comput. Graph. Appl., vol. 36, no. 1, pp. 70-78, Jan. 2016. Show Context View Article Full Text: PDF (4692KB) Google Scholar 

22. E. Tarakci, N. Arman, D. Tarakci and O. Kasapcopur, “Leap Motion Controller-based training for upper extremity rehabilitation in children and adolescents with physical disabilities: A randomized controlled trial”, J. Hand Ther., pp. 1-9, Apr. 2019. Show Context CrossRef  Google Scholar 

23. Y.-T. Wu, K.-H. Chen, S.-L. Ban, K.-Y. Tung and L.-R. Chen, “Evaluation of leap motion control for hand rehabilitation in burn patients: An experience in the dust explosion disaster in Formosa Fun Coast”, Burns, vol. 45, no. 1, pp. 157-164, Feb. 2019. Show Context CrossRef  Google Scholar 

24. T. Vanbellingen, S. J. Filius, T. Nyffeler and E. E. H. van Wegen, “Usability of Videogame- Based Dexterity Training in the Early Rehabilitation Phase of Stroke Patients: A Pilot Study”, Front. Neurol., vol. 8, no. DEC, pp. 1-9, Dec. 2017. Show Context CrossRef  Google Scholar 

25. M. Iosa et al., “Leap motion controlled videogame-based therapy for rehabilitation of elderly patients with subacute stroke: a feasibility pilot study”, Top. Stroke Rehabil., vol. 22, no. 4, pp. 306-316, Aug. 2015. Show Context CrossRef  Google Scholar 

26. A. M. D. C. Souza and S. R. Dos Santos, “Handcopter Game: A Video-Tracking Based Serious Game for the Treatment of Patients Suffering from Body Paralysis Caused by a Stroke”, 2012 14th Symposium on Virtual and Augmented Reality, pp. 201-209, 2012. Show Context View Article Full Text: PDF (795KB) Google Scholar 

27. A. L. Borstad et al., “In-Home Delivery of Constraint-Induced Movement Therapy via Virtual Reality Gaming”, J. Patient-Centered Res. Rev., vol. 5, no. 1, pp. 6-17, Jan. 2018. Show Context CrossRef  Google Scholar 

28. N. J. Seo, J. Arun Kumar, P. Hur, V. Crocher, B. Motawar and K. Lakshminarayanan, “Usability evaluation of low-cost virtual reality hand and arm rehabilitation games”, J. Rehabil. Res. Dev., vol. 53, no. 3, pp. 321-334, Jul. 2016. Show Context CrossRef  Google Scholar 

29. G. C. Burdea, A. Jain, B. Rabin, R. Pellosie and M. Golomb, “Long-term hand tele-rehabilitation on the playstation 3: Benefits and challenges”, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 1835-1838, 2011. Show Context View Article Full Text: PDF (479KB) Google Scholar 

30. M. R. Golomb, M. Barkat-Masih, B. Rabin, M. Abdelbaky, M. Huber and G. Burdea, “Eleven Months of home virtual reality telerehabilitation – Lessons learned”, 2009 Virtual Rehabilitation International Conference, pp. 23-28, 2009. Show Context View Article Full Text: PDF (1370KB) Google Scholar 

31. X. Huang, F. Naghdy, G. Naghdy and H. Du, “Clinical effectiveness of combined virtual reality and robot assisted fine hand motion rehabilitation in subacute stroke patients”, 2017 International Conference on Rehabilitation Robotics (ICORR), pp. 511-515, 2017. Show Context View Article Full Text: PDF (1865KB) Google Scholar 

32. G. Tieri, G. Morone, S. Paolucci and M. Iosa, “Virtual reality in cognitive and motor rehabilitation: facts fiction and fallacies”, Expert Rev. Med. Devices, vol. 15, no. 2, pp. 107-117, Feb. 2018. Show Context CrossRef  Google Scholar 

33. B. Garrett, T. Taverner, D. Gromala, G. Tao, E. Cordingley and C. Sun, “Virtual Reality Clinical Research: Promises and Challenges”, JMIR Serious Games, vol. 6, no. 4, pp. e10839, Oct. 2018. Show Context CrossRef  Google Scholar 

34. P. Lankoski, Game Research Methods: An Overview, 2015. Show Context Google Scholar 

Source: https://ieeexplore.ieee.org/abstract/document/9201789

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[Abstract] RehabFork: An Interactive Game-assisted Upper Limb Stroke Rehabilitation System – IEEE Conference Publication

Abstract

In this paper, we present the design and development of a game-assisted stroke rehabilitation system RehabFork that allows a user to train their upper-limb to perform certain functions related to the task of eating.

The task of eating is divided into several components: (i) grasping the eating utensils such as a fork and knife; (ii) lifting the eating utensils; (iii) using the eating utensils to cut a piece of food; (iv) transferring the food to the mouth; and (v) chewing the food. The RehabFork supports the user through sub-tasks (i)–(iii).

The hardware components of RehabFork consist of an instrumented fork and knife, and a 3D printed pressure pad, that measure and communicate information on user performance to a gaming environment to render an integrated rehabilitation system.

The gaming environment consists of an interactive game that utilizes sensory data as well as user information about the severity of their disability and current level of progress to adjust the difficulty levels of the game to maintain user motivation. Information pertaining to the user, including performance data, is stored and can be shared with care providers for ongoing oversight.

Source: https://ieeexplore.ieee.org/abstract/document/9176168

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[Abstract] A Wearable Hand Rehabilitation System with Soft Gloves

Abstract

Hand paralysis is one of the most common complications in stroke patients, which severely impacts their daily lives. This paper presents a wearable hand rehabilitation system that supports both mirror therapy and task-oriented therapy. A pair of gloves, i.e., a sensory glove and a motor glove, was designed and fabricated with a soft, flexible material, providing greater comfort and safety than conventional rigid rehabilitation devices. The sensory glove worn on the non-affected hand, which contains the force and flex sensors, is used to measure the gripping force and bending angle of each finger joint for motion detection. The motor glove, driven by micromotors, provides the affected hand with assisted driving-force to perform training tasks. Machine learning is employed to recognize the gestures from the sensory glove and to facilitate the rehabilitation tasks for the affected hand. The proposed system offers 16 kinds of finger gestures with an accuracy of 93.32%, allowing patients to conduct mirror therapy using fine-grained gestures for training a single finger and multiple fingers in coordination. A more sophisticated task-oriented rehabilitation with mirror therapy is also presented, which offers six types of training tasks with an average accuracy of 89.4% in real-time.

via A Wearable Hand Rehabilitation System with Soft Gloves – IEEE Journals & Magazine

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[ARTICLE] Adaptive Treadmill-Assisted Virtual Reality-Based Gait Rehabilitation for Post-Stroke Physical Reconditioning—a Feasibility Study in Low-Resource Settings – Full Text

Abstract

Objectives: Individuals with chronic stroke suffer from heterogeneous functional limitations, including cardiovascular dysfunction and gait disorders (associated with increased energy expenditure) besides psychological factors, e.g., motivation. To recondition their cardiovascular endurance and gait, rehabilitation exercises with gradually increasing exercise intensity suiting their individualized capabilities need to be offered. In principal accordance, here we (i) implemented an adaptive Virtual Reality (VR)-based treadmill-assisted platform sensitive to energy expenditure, (ii) investigated its safety and feasibility of use and (iii) examined the implications of gait exercise with this platform on cardiac and gait performance along with energy expenditure, clinical measures (to estimate physical reconditioning of subjects with stroke) and their views on community ambulation capabilities. Methods: Ten able-bodied subjects volunteered in a study to ensure its safety and feasibility of use. Nine subjects with chronic stroke underwent physical reconditioning over multiple exposures using our platform. We investigated the patients’ cardiac and gait performance prior and post exposure to our platform along with studying the clinical relevance of gait parameters in estimating their physical reconditioning. We collected the patients’ feedback. Results: We found statistical improvement in the gait parameters and reduction in energy expenditure during overground walk following ~1 month of gait exercise with our platform. They reported that the VR-based tasks were motivating. Conclusion: Results show that this platform can pave the way towards implementing home-based individualized exercise platform that can monitor one’s cardiac and gait performance capabilities while offering an adaptive and progressive gait exercise environment within safety thresholds suiting one’s exercise capabilities.

Physiological Cost Index sensitive Adaptive Response Technology (PCI-ART) for post-stroke physical reconditioning. Note: PCI- Physiological Cost Index; SST-Single Support Time; AL- Affected limb; UAL- Unaffected limb.

Physiological Cost Index sensitive Adaptive Response Technology (PCI-ART) for post-stroke physical reconditioning. Note: PCI- Physiological Cost Index; SST-Single Support Time; AL- Affected limb; UAL- Unaffected limb. 

SECTION I.

Introduction

Neurological disorders, such as stroke is a leading cause of disability with a prevalence rate of 424 in 100,000 individuals in India [1]. Often, these patients suffer from functional disabilities, heterogeneous physical deconditioning along with deteriorated cardiac functioning [2], [3] and a sedentary lifestyle immediately following stroke [4]. A deconditioned patient requires reconditioning of his/her cardiac capacity and ambulation capabilities that can be achieved through individualized rehabilitation [5]. This needs to be done under the supervision of a clinician who can monitor one’s functional capability, cardiac capacity and gait performance thereby recommending an appropriate dosage of the gait rehabilitation exercise intensity to the patient along with feedback. Such gait rehabilitation is crucial since about 80% of these patients have been reported to suffer from gait-related disorders [6] along with more energy expenditure than able-bodied individuals [7] often accompanied with reduced cardiac capacity [2], [4]. However, given the low doctor-to-patient ratio [8], lack of rehabilitation facilities and patients being released early from rehabilitation clinics followed by home-based exercise [9], particularly in developing countries like India, availing individualized rehabilitation services becomes difficult. Again, undergoing home-based exercises under clinician’s one-on-one supervision becomes difficult given the restricted healthcare resources, thereby limiting the rehabilitation outcomes [10]. Again, given the restricted healthcare resources, getting a clinician visiting the homes for delivering therapy sessions to patients is often costly causing the patients to miss the expert inputs on the exercise intensity suiting his/her exercise capability along with motivational feedback from the clinician [11]. This necessitates the use of a complementary technology-assisted rehabilitation platform that can be availed by the patient at his/her home [12] following a short stay at the rehabilitation clinic [13]. Again, it is preferred that this platform be capable of offering individualized gait exercise while varying the dosage of exercise intensity (based on the patient’s exercise capability) along with motivational feedback [14]. Additionally, exercise administered by this platform can be complemented with intermediate clinician-mediated assessments of rehabilitation outcomes, thereby reducing continuous demands on the restricted clinical resources. Thus, it is important to investigate the use of such technology-assisted gait exercise platforms that are capable of offering exercise based on one’s individualized capability along with motivational feedback.

Researchers have explored the use of technology-assisted solutions to offer rehabilitative gait exercises to these patients, along with presenting motivational feedback [15]–[16][17][18][19][20][21][22][23][24]. Specifically, investigators have used Virtual Reality (VR) coupled with a treadmill (having a limited footprint and making it suitable for home-based settings) while delivering individualized feedback [15] to the patient during exercise. Again, VR can help to project scenarios that can make the exercise engaging and interactive for a user [16]–[17][18][19]. In fact, Finley et al. have shown that the visual feedback offered by VR provides an optical flow that can induce changes in the gait performance (quantified in terms of gait parameters, e.g., Step Length, Step Symmetry, etc.) of such patients during treadmill-assisted walk [20]. Further, Jaffe et al. have reported positive implications of VR-based treadmill-assisted walking exercise on the gait performance of individuals with stroke [23], leading to improvement in their community ambulation [24]. These studies have shown the efficacy of the VR-based treadmill-assisted gait exercise platform to contribute towards gait rehabilitation of individuals suffering from stroke. Though promising, none of these platforms are sensitive to one’s individualized exercise capability and thus, in turn, could not decide an optimum dosage of exercise intensity suiting one’s capability, e.g., cardiac capacity and ambulation capability. This is particularly critical for individuals with stroke since they possess diminished exercise ability along with deteriorated cardiac functioning [2], [4].

From literature review, we find that after stroke, treadmill-assisted cardiac exercise programs can lead to one’s improved fitness and exercise capability [25]. For example, researchers have presented studies on Moderate-Intensity Continuous Exercise and High-Intensity Interval Training in which exercise protocols are individualized by a clinician based on one’s cardiac capacity while contributing to effective gait rehabilitation [26]–[27][28][29]. Though promising, these have not offered a progressive and adaptive exercise environment in which the dosage of exercise intensity is varied based on one’s cardiac capacity in real-time. Thus, the choice of optimum dosage of exercise intensity that can be individualized in real-time for a patient, still remains as inadequately explored [4]. For deciding the optimal dosage of rehabilitative exercise intensity, clinicians often refer to the guidelines recommended by the American College of Sports Medicine (ACSM) [30]. These guidelines suggest thresholds to decide the intensity of the exercise based on one’s metabolic energy consumption in terms of oxygen intake, heart rate, etc. Deciding the dosage of exercise intensity is crucial, particularly for individuals with stroke since their energy requirements have been reported to be 55-100% higher than that of their able-bodied counterparts [7]. Specifically, higher energy requirement often limits the capabilities of these patients and challenges their rehabilitation outcomes. This can be addressed if the technology-assisted gait exercise platform can offer individualized exercise (maintaining the safe exercise thresholds) based on the energy expenditure of the patients acquired in real-time during the exercise.

The energy expenditure can be defined as the cost of physical activity [4] and it is often expressed in terms of oxygen consumption or heart rate [31]. Thus, investigators have monitored the oxygen consumption and heart rate to estimate the energy expenditure of individuals with stroke during their walk [31], [32]. However, monitoring oxygen consumption during exercise requires a cumbersome setup [31], making it unsuitable for home-based rehabilitation. On the other hand, one’s heart rate (HR) can be monitored using portable solutions [33] that can be integrated with a treadmill in home-based settings. Researchers have explored treadmill-assisted gait exercise platforms that are sensitive to the user’s heart rate. For example, researchers have offered treadmill training to subjects with stroke in which some of them varied treadmill speed to achieve 45%-50% [34], while others varied speed to achieve 85% to 95% [35], [36] of one’s age-related maximum heart rate. Again, Pohl et al. have offered treadmill-assisted exercise to subjects with stroke while ensuring that the user’s heart rate settled to the respective resting-state heart rate [37]. Again of late, there had been advanced treadmills, available off-the-shelf, that can monitor one’s heart rate and vary the treadmill speed to maintain the user’s heart rate at a predefined level [38], [39]. Though one’s heart rate is an important indicator that needs to be considered during treadmill-assisted exercise, one’s walking speed while using the treadmill also offers important information on one’s exercise capability. This is because gait rehabilitation aims to improve one’s community ambulation that is related to one’s walking speed [40]. Thus, it would be interesting to explore the composite effect of one’s walking speed along with working and resting-state heart rates during treadmill-assisted gait exercise to study one’s energy expenditure, quantified in terms of a proxy index, namely Physiological Cost Index (PCI) [31].

Given that there are no existing studies that have used a treadmill-assisted gait exercise platform deciding the dosage of exercise intensity based on one’s PCI estimated in real-time during exercise, it might be interesting to explore the use of such an individualized gait exercise platform for individuals with stroke. Thus, we wanted to extend a treadmill-assisted gait exercise platform by making it adaptive to one’s individualized PCI. Additionally, we wanted to augment this platform with VR-based user interface to offer visual feedback to the user undergoing gait exercise. We hypothesized that such a gait exercise platform can recondition a patient’s exercise capability in terms of cardiac and gait performance to achieve improved community ambulation. The objectives of our research were three-fold, namely to (i) implement a novel PCI-sensitive Adaptive Response Technology (PCI-ART) offering VR-based treadmill-assisted gait exercise, (ii) investigate the safety and feasibility of use of this platform among able-bodied individuals before applying it to subjects with stroke and (iii) examine implications of undergoing gait exercise with this platform on the patients’ (a) cardiac and gait performance along with energy expenditure, (b) clinical measures estimating the physical reconditioning and (c) views on their community ambulation capabilities.

The rest of the paper is organized as follows: Section II presents our system design. Section III explains the experiments and procedures of this study. Section IV discusses the results. In Section V, we summarize our findings, limitations, and scope of future research.[…]

Continue —-> Adaptive Treadmill-Assisted Virtual Reality-Based Gait Rehabilitation for Post-Stroke Physical Reconditioning—a Feasibility Study in Low-Resource Settings – IEEE Journals & Magazine

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[ARTICLE] Portable Motion-Analysis Device for Upper-Limb Research, Assessment, and Rehabilitation in Non-Laboratory Settings – Full Text

Abstract

This study presents the design and feasibility testing of an interactive portable motion-analysis device for the assessment of upper-limb motor functions in clinical and home settings. The device engages subjects to perform tasks that imitate activities of daily living, e.g. drinking from a cup and moving other complex objects. Sitting at a magnetic table subjects hold a 3D printed cup with an adjustable magnet and move this cup on the table to targets that can be drawn on the table surface. A ball rolling inside the cup can enhance the task challenge by introducing additional dynamics. A single video camera with a portable computer tracks real-time kinematics of the cup and the rolling ball using a custom-developed, color-based computer-vision algorithm. Preliminary verification with marker-based 3D-motion capture demonstrated that the device produces accurate kinematic measurements. Based on the real-time 2D cup coordinates, audio-visual feedback about performance can be delivered to increase motivation. The feasibility of using this device in clinical diagnostics is demonstrated on 2 neurotypical children and also 3 children with upper-extremity impairments in the hospital, where conventional motion-analysis systems are difficult to use. The device meets key needs for clinical practice: 1) a portable solution for quantitative motor assessment for upper-limb movement disorders at non-laboratory clinical settings, 2) a low-cost rehabilitation device that can increase the volume of in-home physical therapy, and 3) the device affords testing and training a variety of motor tasks inspired by daily challenges to enhance self-confidence to participate in day-to-day activities.

SECTION I.

Introduction

An integral part of clinical care for individuals with motor disorders is to assess motor function to guide and evaluate medical treatment, surgical intervention or physical therapy. One of the challenges for assessing motor function is to define sensitive and quantitative measures that can be readily obtained in clinical practice. The objective of this study was to develop a device that affords quantitative assessment of motor impairments in non-laboratory settings. The specific focus is on individuals with upper-limb movement disorders. One central goal was to ground the task in scientific research to relate clinical measures to research and capitalize on insights from fundamental research.

This paper first lays out the need for such a device particularly for children with motor disorders and post-stroke rehabilitation. We then motivate the specific motor task that was originally conceived for basic research on motor control. We then detail the design of the prototype with all hardware and software components so that it can be replicated. One design goal was to make the device low-cost, so that it can be used in many clinical environments including at home for therapeutic exercises. We conclude with first results from pilot experiments acquired both in a traditional laboratory setting and in an Epilepsy Monitoring Unit. These first data were obtained from children with dystonia. However, the device is not limited to this population and is currently further modified for the assessment of stroke patients.

A. Clinical Assessments of Motor Disorders

A motor disorder manifests as an impaired ability to execute a movement with the intended spatial and temporal pattern. This includes abnormal posturing, presence of unintended excessive movement, and normal movements occurring at unintended or inappropriate times [1]. Patients with upper-limb impairments require special assistance to perform common motor tasks associated with self-care, such as feeding and dressing. Challenges in their movement control result in frustration, which leads to less engagement and practice, and thereby fewer opportunities to attenuate their motor disabilities and improve their movement control.

Motor disorder are observed also among children. Cerebral Palsy (CP) is a common cause of movement disorders among children, affecting 3 to 4 individuals per 1000 births in the US. The dyskinetic form of CP occurs in 15% of all cases [2]. Due to inflexible postures, caused by muscle spasms and contractures together with involuntary jerky movements, children with dyskinetic CP are often prevented from participation in many daily activities. This also prevents them from acquiring age-appropriate motor skills during critical periods of skill development [3], [4]. This is particularly aggravated when the condition affects the upper limbs.

For clinical motor assessments, the current standard tools are clinical scales. For cerebral palsy, typical tests are the gross motor function classification system (GMFCS) [5], the manual ability classification system (MACS) [6], the House Scale [7], the Melbourne Assessment [8], the Assisting Hand Assessment [9], the Hypertonia Assessment Tool (HAT) [10], the Barry-Albright Dystonia (BAD) scale [11], and the Shriners Hospital for Children Upper Extremity Evaluation [12]. These outcome measures were devised to satisfy the typical criteria for effective outcome measures, including reliability, validity, specificity, and responsiveness [13]. Although useful, these rating scales rely on subjective assessment and questionnaires that are vulnerable to inter-rater and test-retest reliability, nonlinearity, multi-dimensionality, and ceiling or floor effects [14]. These shortcomings need to be overcome by more quantitative outcome measures to provide a better evaluation of the individual’s motor functions and abilities, and potentially utilize such measures to objectvely assess and titrate interventions.

B. Quantitative Assessment of Motor Function

Motion tracking technologies have provided quantitative means of recording movements through a variety of sensing technology that tracks and stores movement. Camera-based motion capture, such as Vicon (Vicon Motion Systems, Oxford, UK) and Optitrak (Northern Digital Inc, Ontario, CA) requires external markers or sensors placed on key anatomical landmarks to reconstruct the skeletal model of human body parts. These state-of-the-art technologies track motion to very high precision with high sampling rates and they have been used for pre- and post-treatment assessment of upper- or lower-extremity pathologies. However, such data acquisition is limited to traditional laboratory settings because the multi-camera systems are expensive and not portable.

On the other hand, there are low-cost inertial measurement units (IMUs) that directly measure acceleration, rotational change and magnetic orientation. While these sensors have the advantage that they are self-contained and wearable, drawbacks are degraded accuracy due to drift, calibration errors and noise inherent to inertial sensors and the need to frequently recharge batteries for real-time data streaming [15]. Moreover, attaching sensors to body parts can be inconvenient or even impossible for certain clinical populations, and many children will not tolerate them.

In view of the above arguments, there is a strong need for less invasive devices that can provide quantitative measurements in tasks related to upper-extremeity motor function. Preferably, such a device should allow for portability and be low-cost to reach large populations.

C. Low-Cost Rehabilitation at Home

Rehabilitation follows standard practice and frequently requires one-on-one interaction with a therapist for extended periods of time. For these reasons, robotic devices have emerged to deliver higher-dosage and higher-intensity training for patients with movement disorders such as cerebral palsy and stroke [16]–[17][18][19]. However, while effective, robotic therapy is expensive and to date can only be used in clinical settings. To increase the volume in therapy, lower-cost devices that can be used at home are urgently needed.

Performance improvements with predominant home training are indeed possible. This was demonstrated by pediatric constraint-induced movement therapy (CIMT) for children with hemiparetic CP [20], [21]. Further, it was shown that even children with severe dystonia can improve their performance if they use an interface or device that enables and facilitates their severely handicapped movements [22].

A portable low-cost device for home use that is able to provide reliable quantitative measurements would help address the above shortcomings. Measurements could also be streamed to careproviders on a secure cloud protocol, for diagnosis of interventions, analysis of therapeutic outcomes, and further follow up.

D. Theoretically-Grounded Rehabilitation

Motor tasks for home therapy should be engaging to avoid boredom and attrition and should also have functional relevance. With this goal in mind, we developed a motor task that was motivated by the daily self-feeding activity of leading a cup of coffee or a spoon filled with soup to the mouth. The core challenge of actions of this kind is that moving such an object with sloshing liquid presents complex interaction forces: any force applied to the cup also applies a force to the liquid that then acts back on the hand. When such internal dynamics is present, interaction forces become quite complex, and the human performing the task needs to predict and preempt the internal dynamics of the moving liquid. Clearly, better understanding task is like guiding a cup of coffee to one’s mouth or a spoonful of soup has high functional relevance. While many such functional tasks have been developed for rehabiltation (e.g., the box-and-block and the pegboard task), the quantitative assessment should allow for more than descriptive outcome measures such as error or success rate. Monitoring the ‘process’ continuously should provide more detailed insight into coordinative challenges. This is indeed possible in the task of guiding a cup of coffee as we explain next.

In previous research, we abstracted a relevant, yet simplified model task, inspired by guiding a cup of coffee [23]–[24][25][26]. To reduce the complexity and afford theoretical analyses, the “cup of coffee” was simplified to a rigid object with a rolling ball inside. The rolling ball represents the moving liquid; this is also similar to the children’s game of transporting an egg in a spoon [27]. Fig.1A-C shows the transition from the real object to the simplified physical model. Importantly, the original task (Fig.1A) was reduced to a two-dimensional model, where the subject interacts with the object via a robotic manipulandum. The virtual model consists of a cart with a suspended pendulum, a well-known benchmark problem in control theory.

FIGURE 1.Model for the task of carrying a cup of coffee. A: The real object. B: The simplified physical model. C: The equivalent cart-and pendulum model implemented in the virtual task.

[…]

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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.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

 

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[Abstract + References] Self-paced movement intention recognition from EEG signals during upper limb robot-assisted rehabilitation

Abstract

Currently, one of the challenges in EEG-based brain-computer interfaces (BCI) for neurorehabilitation is the recognition of the intention to perform different movements from same limb. This would allow finer control of neurorehabilitation and motor recovery devices by end-users [1]. To address this issue, we assess the feasibility of recognizing two self-paced movement intentions of the right upper limb plus a rest state from EEG signals recorded during robot-assisted rehabilitation therapy. In addition, the work proposes the use of Multi-CSP features and deep learning classifiers to recognize movement intentions of the same limb. The results showed performance peaked greater at (80%) using a novel classification models implemented in a multiclass classification scenario. On the basis of these results, the decoding of the movement intention could potentially be used to develop more natural and intuitive robot assisted neurorehabilitation therapies
1. S. R. Soekadar , N. Birbaumer , M. W. Slutzky , and L. G. Cohen , “Brain machine interfaces in neurorehabilitation of stroke,” Neurobiology of Disease, vol. 83, pp. 172-179, 2015.

2. P. Ofner , A. Schwarz , J. Pereira , and G. R. Müller-Putz , “Upper limb movements can be decoded from the time-domain of low-frequency EEG,” PLoS One, vol. 12, no. 8, p. e0182578, Aug 2017, poNE-D- 17-04785[PII].

3. F. Shiman , E. Lopez-Larraz , A. Sarasola-Sanz , N. Irastorza-Landa , M. Spler , N. Birbaumer , and A. Ramos-Murguialday , “Classification of different reaching movements from the same limb using EEG,” Journal of Neural Engineering, vol. 14, no. 4, p. 046018, 2017.

4. J. Pereira , A. I. Sburlea , and G. R. Müller-Putz , “EEG patterns of self- paced movement imaginations towards externally-cued and internally- selected targets,” Scientific Reports, vol. 8, no. 1, p. 13394, 2018.

5. R. Vega , T. Sajed , K. W. Mathewson , K. Khare , P. M. Pilarski , R. Greiner , G. Sanchez-Ante , and J. M. Antelis , “Assessment of feature selection and classification methods for recognizing motor imagery tasks from electroencephalographic signals,” Artif. Intell. Research, vol. 6, no. 1, p. 37, 2017.

6. I. Figueroa-Garcia et al , “Platform for the study of virtual task- oriented motion and its evaluation by EEG and EMG biopotentials,” in 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Aug 2014, pp. 1174–1177.

7. B. Graimann and G. Pfurtscheller , “Quantification and visualization of event-related changes in oscillatory brain activity in the timefrequency domain,” in Event-Related Dynamics of Brain Oscillations, ser. Progress in Brain Research, C. Neuper and W. Klimesch , Eds. Elsevier, 2006, vol. 159, pp. 79 – 97.

8. G. Pfurtscheller and F. L. da Silva , “Event-related EEG/MEG synchronization and desynchronization: basic principles,” Clinical Neurophysiology, vol. 110, no. 11, pp. 1842 – 1857, 1999.

9. G. Dornhege , B. Blankertz , G. Curio , and K. Muller , “Boosting bit rates in noninvasive EEG single-trial classifications by feature combination and multiclass paradigms,” IEEE Transactions on Biomedical Engineering, vol. 51, no. 6, pp. 993–1002, 2004.

10. X. Yong and C. Menon , “EEG classification of different imaginary movements within the same limb,” PLOS ONE, vol. 10, no. 4, pp. 1–24, 04 2015.

11. L. G. Hernandez , O. M. Mozos , J. M. Ferrandez , and J. M. Antelis , “EEG-based detection of braking intention under different car driving conditions,” Frontiers in Neuroinformatics, vol. 12, p. 29, 2018. [Online]. Available: https://www.frontiersin.org/article/10.3389/fninf.2018.00029

12. L. G. Hernandez and J. M. Antelis , “A comparison of deep neural network algorithms for recognition of EEG motor imagery signals,” in Pattern Recognition, 2018, pp. 126–134.

13. M. Abadi et al , “TensorFlow: Large-scale machine learning on heterogeneous systems,” 2015, software available from tensorflow.org. [Online]. Available: https://www.tensorflow.org/

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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.

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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.

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[Abstract + References] Synergy-Based FES for Post-Stroke Rehabilitation of Upper-Limb Motor Functions

Abstract

Functional electrical stimulation (FES) is capable of activating muscles that are under-recruited in neurological diseases, such as stroke. Therefore, FES provides a promising technology for assisting upper-limb motor functions in rehabilitation following stroke. However, the full benefits of FES may be limited due to lack of a systematic approach to formulate the pattern of stimulation. Our preliminary work demonstrated that it is feasible to use muscle synergy to guide the generation of FES patterns.In this paper, we present a methodology of formulating FES patterns based on muscle synergies of a normal subject using a programmable multi-channel FES device. The effectiveness of the synergy-based FES was tested in two sets of experiments. In experiment one, the instantaneous effects of FES to improve movement kinematics were tested in three patients post ischemic stroke. Patients performed frontal reaching and lateral reaching tasks, which involved coordinated movements in the elbow and shoulder joints. The FES pattern was adjusted in amplitude and time profile for each subject in each task. In experiment two, a 5-day session of intervention using synergy-based FES was delivered to another three patients, in which patients performed task-oriented training in the same reaching movements in one-hour-per-day dose. The outcome of the short-term intervention was measured by changes in Fugl–Meyer scores and movement kinematics. Results on instantaneous effects showed that FES assistance was effective to increase the peak hand velocity in both or one of the tasks. In short-term intervention, evaluations prior to and post intervention showed improvements in both Fugl–Meyer scores and movement kinematics. The muscle synergy of patients also tended to evolve towards that of the normal subject. These results provide promising evidence of benefits using synergy-based FES for upper-limb rehabilitation following stroke. This is the first step towards a clinical protocol of applying FES as therapeutic intervention in stroke rehabilitation.

I. Introduction

Muscle activation during movement is commonly disrupted due to neural injuries from stroke. A major challenge for stroke rehabilitation is to re-establish the normal ways of muscle activation through a general restoration of motor control, otherwise impairments may be compensated by the motor system through a substitution strategy of task control [1]. In post-stroke intervention, new technologies such as neuromuscular electrical stimulation (NMES) or functional electrical stimulation (FES) offer advantages for non-invasively targeting specific groups of muscles [2]–[4] to restore the pattern of muscle activation. Nevertheless, their effectiveness is limited by lack of a systematic methodology to optimize the stimulation pattern, to implement the optimal strategy in clinical settings, and to design a protocol of training towards the goal of restoring motor functions. This pioneer study addresses these issues in clinical application with a non-invasive FES technology.

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1. M. F. Levin, J. A. Kleim, and S. L. Wolf, “What do motor ‘recovery’ and ‘compensation’ mean in patients following stroke?” Neurorehabilitation Neural Repair, vol. 23, no. 4, pp. 313–319, 2008.

2. G. Alon, A. F. Levitt, and P. A. McCarthy, “Functional electrical stimulation (FES) may modify the poor prognosis of stroke survivors with severe motor loss of the upper extremity: A preliminary study,” Amer. J. Phys. Med. Rehabil., vol. 87, no. 8, pp. 627–636, 2008.

3. W. Rong, “A neuromuscular electrical stimulation (NMES) and robot hybrid system for multi-joint coordinated upper limb rehabilitation after stroke,” J. Neuroeng. Rehabil., vol. 14, no. 1, p. 34, Dec. 2017.

4. J. J. Daly, “Recovery of coordinated gait: Randomized controlled stroke trial of functional electrical stimulation (FES) versus no FES, with weight-supported treadmill and over-ground training,” Neurorehabilitation Neural Repair, vol. 25, no. 7, pp. 588–596, Sep. 2011.

5. R. Nataraj, M. L. Audu, R. F. Kirsch, and R. J. Triolo, “Comprehensive joint feedback control for standing by functional neuromuscular stimulation—A simulation study,” IEEE Trans. Neural Syst. Rehabil. Eng., vol. 18, no. 6, pp. 646–657, Dec. 2010.

6. R. Nataraj, M. L. Audu, and R. J. Triolo, “Restoring standing capabilities with feedback control of functional neuromuscular stimulation following spinal cord injury,” Med. Eng. Phys., vol. 42, pp. 13–25, Apr. 2017.

7. H. Rouhani, M. Same, K. Masani, Y. Q. Li, and M. R. Popovic, “PID controller design for FES applied to ankle muscles in neuroprosthesis for standing balance,” Frontiers Neurosci., vol. 11, p. 347, Jun. 2017.

8. V. K. Mushahwar, P. L. Jacobs, R. A. Normann, R. J. Triolo, and N. Kleitman, “New functional electrical stimulation approaches to standing and walking,” J. Neural Eng., vol. 4, no. 3, pp. S181–S197, Sep. 2007.

9. B. J. Holinski, “Intraspinal microstimulation produces over-ground walking in anesthetized cats,” J. Neural Eng., vol. 13, no. 5, p. 056016, Oct. 2016.

10. M. B. Popovic, D. B. Popovic, T. Sinkjær, A. Stefanovic, and L. Schwirtlich, “Restitution of reaching and grasping promoted by functional electrical therapy,” Artif. Organs, vol. 26, no. 3, pp. 271–275, Mar. 2002.

11. A. B. Ajiboye, “Restoration of reaching and grasping movements through brain-controlled muscle stimulation in a person with tetraplegia: A proof-of-concept demonstration,” Lancet Lond. Engl., vol. 389, no. 10081, pp. 1821–1830, May 2017.

12. J. H. Grill and P. H. Peckham, “Functional neuromuscular stimulation for combined control of elbow extension and hand grasp in C5 and C6 quadriplegics,” IEEE Trans. Rehabil. Eng., vol. 6, no. 2, pp. 190–199, Jun. 1998.

13. M. R. Popovic, T. A. Thrasher, M. E. Adams, V. Takes, V. Zivanovic, and M. I. Tonack, “Functional electrical therapy: Retraining grasping in spinal cord injury,” Spinal Cord, vol. 44, no. 3, pp. 143–151, Mar. 2006.

14. C. Ethier, E. R. Oby, M. J. Bauman, and L. E. Miller, “Restoration of grasp following paralysis through brain-controlled stimulation of muscles,” Nature, vol. 485, no. 7398, pp. 368–371, May 2012.

15. G. Alon, “Use of neuromuscular electrical stimulation in neureorehabilitation: A challenge to all,” J. Rehabil. Res. Develop., vol. 40, no. 6, pp. 9–12, Dec. 2003.

16. G. Alon, A. F. Levitt, and P. A. McCarthy, “Functional electrical stimulation enhancement of upper extremity functional recovery during stroke rehabilitation: A pilot study,” Neurorehabilitation Neural Repair, vol. 21, no. 3, pp. 207–215, Jun. 2007.

17. C. Church, C. Price, A. D. Pandyan, S. Huntley, R. Curless, and H. Rodgers, “Randomized controlled trial to evaluate the effect of surface neuromuscular electrical stimulation to the shoulder after acute stroke,” Stroke, vol. 37, no. 12, pp. 2995–3001, Dec. 2006.

18. J. H. Cauraugh and S. B. Kim, “Chronic stroke motor recovery: Duration of active neuromuscular stimulation,” J. Neurolog. Sci., vol. 215, nos. 1–2, pp. 13–19, Nov. 2003.

19. S. Ferrante, T. Schauer, G. Ferrigno, J. Raisch, and F. Molteni, “The effect of using variable frequency trains during functional electrical stimulation cycling,” Neuromodulation, Technol. Neural Interface, vol. 11, no. 3, pp. 216–226, Jul. 2008.

20. R. W. Fields, “Electromyographically triggered electric muscle stimulation for chronic hemiplegia,” Arch. Phys. Med. Rehabil., vol. 68, no. 7, pp. 407–414, Jul. 1987.

21. G. H. Kraft, S. S. Fitts, and M. C. Hammond, “Techniques to improve function of the arm and hand in chronic hemiplegia,” Arch. Phys. Med. Rehabil., vol. 73, no. 3, pp. 220–227, Mar. 1992.

22. G. van Overeem Hansen, “EMG-controlled functional electrical stimulation of the paretic hand,” Scand. J. Rehabil. Med., vol. 11, no. 4, pp. 189–193, 1979.

23. J. H. Cauraugh, S. B. Kim, and A. Duley, “Coupled bilateral movements and active neuromuscular stimulation: Intralimb transfer evidence during bimanual aiming,” Neurosci. Lett., vol. 382, nos. 1–2, pp. 39–44, Jul. 2005.

24. J. S. Knutson, D. D. Gunzler, R. D. Wilson, and J. Chae, “Contralaterally controlled functional electrical stimulation improves hand dexterity in chronic hemiparesis: A randomized trial,” Stroke, vol. 47, no. 10, pp. 2596–2602, Oct. 2016.

25. D. A. E. Bolton, J. H. Cauraugh, and H. A. Hausenblas, “Electromyogram-triggered neuromuscular stimulation and stroke motor recovery of arm/hand functions: A meta-analysis,” J. Neurol. Sci., vol. 223, no. 2, pp. 121–127, Aug. 2004.

26. M. K.-L. Chan, R. K.-Y. Tong, and K. Y.-W. Chung, “Bilateral upper limb training with functional electric stimulation in patients with chronic stroke,” Neurorehabilitation Neural Repair, vol. 23, no. 4, pp. 357–365, May 2009.

27. J. B. Manigandan, G. S. Ganesh, M. Pattnaik, and P. Mohanty, “Effect of electrical stimulation to long head of biceps in reducing gleno humeral subluxation after stroke,” Neuro Rehabil., vol. 34, no. 2, pp. 245–252, 2014.

28. S. Li, C. Zhuang, C. M. Niu, Y. Bao, Q. Xie, and N. Lan, “Evaluation of functional correlation of task-specific muscle synergies with motor performance in patients poststroke,” Frontiers Neurol., vol. 8, p. 337, Jul. 2017.

29. A. d’Avella, P. Saltiel, and E. Bizzi, “Combinations of muscle synergies in the construction of a natural motor behavior,” Nature Neurosci., vol. 6, no. 3, pp. 300–308, Mar. 2003.

30. V. C. K. Cheung, “Muscle synergy patterns as physiological markers of motor cortical damage,” Proc. Nat. Acad. Sci. USA, vol. 109, no. 36, pp. 14652–14656, Sep. 2012.

31. D. J. Clark, L. H. Ting, F. E. Zajac, R. R. Neptune, and S. A. Kautz, “Merging of healthy motor modules predicts reduced locomotor performance and muscle coordination complexity post-stroke,” J. Neurophysiol., vol. 103, no. 2, pp. 844–857, Feb. 2010.

32. E. Ambrosini, “Neuro-mechanics of recumbent leg cycling in post-acute stroke patients,” Ann. Biomed. Eng., vol. 44, pp. 3238–3251, Jun. 2016.

33. C. Zhuang, J. C. Marquez, H. E. Qu, X. He, and N. Lan, “A neuromuscular electrical stimulation strategy based on muscle synergy for stroke rehabilitation,” in Proc. IEEE 7th Int./EMBS Conf. Neural Eng. (NER), vol. 15, Apr. 2015, pp. 816–819.

34. R. S. Razavian, B. Ghannadi, N. Mehrabi, M. Charlet, and J. McPhee, “Feedback control of functional electrical stimulation for 2-D arm reaching movements,” IEEE Trans. Neural Syst. Rehabil. Eng., vol. 26, no. 10, pp. 2033–2043, Oct. 2018.

35. C. M. Niu, C. Zhuang, Y. Bao, S. Li, N. Lan, and Q. Xie, “Synergy-based NMES intervention accelerated rehabilitation of post-stroke hemiparesis,” in Proc. Assoc. Acad. Physiatrists Annu. Conf., Las Vegas, NV, USA, 2017.

36. H. Qu, “Development of network-based multichannel neuromuscular electrical stimulation system for stroke rehabilitation,” J. Rehabil. Res. Develop., vol. 52, no. 3, pp. 263–278, 2016.

37. C. M. Niu, “Effectiveness of short-term training with a synergy-based FES paradigm on motor function recovery post stroke,” in Proc. 12th Int. Soc. Phys. Rehabil. Med. World Congr., Paris, France, 2018.

38. T. Wang, “Customization of synergy-based FES for post-stroke rehabilitation of upper-limb motor functions,” in Proc. IEEE 40th Annu. Int. Conf. Eng. Med. Biol. Soc. (EMBS), Jul. 2018, 3541–3544.

39. L. L. Baker, D. R. McNeal, L. A. Benton, B. R. Bowman, and R. L. Waters, Ed., Neuromuscular Electrical Stimulation a Practical Guide, 4th ed. Downey, CA, USA: Los Amigos Research & Education Institute, 2000.

40. A. d’Avella, A. Portone, L. Fernandez, and F. Lacquaniti, “Control of fast-reaching movements by muscle synergy combinations.,” J. Neurosci., vol. 26, no. 30, pp. 7791–7810, Jul. 2006.

41. R. D. Wilson, “Upper-limb recovery after stroke: A randomized controlled trial comparing EMG-triggered, cyclic, and sensory electrical stimulation,” Neurorehabilitation Neural Repair, vol. 30, no. 10, pp. 978–987, Nov. 2016.

42. A. J. Levine, “Identification of a cellular node for motor control pathways,” Nature Neurosci., vol. 17, no. 4, pp. 586–593, Apr. 2014.

43. S. B. Frost, S. Barbay, K. M. Friel, E. J. Plautz, and R. J. Nudo, “Reorganization of remote cortical regions after ischemic brain injury: A potential substrate for stroke recovery,” J. Neurophysiol., vol. 89, no. 6, pp. 3205–3214, Jun. 2003.

44. P. Langhorne, J. Bernhardt, and G. Kwakkel, “Stroke rehabilitation,” Lancet, vol. 377, no. 9778, pp. 1693–1702, May 2011.

45. M. D. Ellis, B. G. Holubar, A. M. Acosta, R. F. Beer, and J. P. A. Dewald, “Modifiability of abnormal isometric elbow and shoulder joint torque coupling after stroke,” Muscle Nerve, vol. 32, pp. 170–178, Aug. 2005.

46. J. P. A. Dewald, P. S. Pope, J. D. Given, T. S. Buchanan, and W. Z. Rymer, “Abnormal muscle coactivation patterns during isometric torque generation at the elbow and shoulder in hemiparetic subjects,” Brain, vol. 118, no. 2, pp. 495–510, 1995.

47. D. G. Kamper, A. N. McKenna-Cole, L. E. Kahn, and D. J. Reinkensmeyer, “Alterations in reaching after stroke and their relation to movement direction and impairment severity,” Arch. Phys. Med. Rehabil., vol. 83, no. 5, pp. 702–707, May 2002.

48. C. L. Massie, S. Fritz, and M. P. Malcolm, “Elbow extension predicts motor impairment and performance after stroke,” Rehabil. Res. Pract., vol. 2011, pp. 1–7, 2011.

49. V. C. K. Cheung, L. Piron, M. Agostini, S. Silvoni, A. Turolla, and E. Bizzi, “Stability of muscle synergies for voluntary actions after cortical stroke in humans,” Proc. Nat. Acad. Sci. USA, vol. 106, no. 46, pp. 19563–19568, Nov. 2009.

50. J. Roh, W. Z. Rymer, and R. F. Beer, “Robustness of muscle synergies underlying three-dimensional force generation at the hand in healthy humans,” J. Neurophysiol., vol. 107, no. 8, pp. 2123–2142, Apr. 2012.

51. J. Roh, W. Z. Rymer, and R. F. Beer, “Evidence for altered upper extremity muscle synergies in chronic stroke survivors with mild and moderate impairment,” Frontiers Hum. Neurosci., vol. 9, p. 6, Feb. 2015.

52. J. Roh, W. Z. Rymer, E. J. Perreault, S. B. Yoo, and R. F. Beer, “Alterations in upper limb muscle synergy structure in chronic stroke survivors,” J. Neurophysiol., vol. 109, no. 3, pp. 768–781, Feb. 2013.

53. W. H. Backes, W. H. Mess, V. van Kranen-Mastenbroek, and J. P. H. Reulen, “Somatosensory cortex responses to median nerve stimulation: fMRI effects of current amplitude and selective attention,” Clin. Neurophysiol., vol. 111, no. 10, pp. 1738–1744, Oct. 2000.

54. G. Francisco, “Electromyogram-triggered neuromuscular stimulation for improving the arm function of acute stroke survivors: A randomized pilot study,” Arch. Phys. Med. Rehabil., vol. 79, no. 5, pp. 570–575, May 1998.

55. S. K. Sabut, C. Sikdar, R. Kumar, and M. Mahadevappa, “Functional electrical stimulation of dorsiflexor muscle: Effects on dorsiflexor strength, plantarflexor spasticity, and motor recovery in stroke patients,” Neurorehabilitation, vol. 29, no. 4, pp. 393–400, 2011.

56. Y.-H. Wang, F. Meng, Y. Zhang, M.-Y. Xu, and S.-W. Yue, “Full-movement neuromuscular electrical stimulation improves plantar flexor spasticity and ankle active dorsiflexion in stroke patients: A randomized controlled study,” Clin. Rehabil., vol. 30, no. 6, pp. 577–586, Jun. 2016.

57. W. H. Chang and Y.-H. Kim, “Robot-assisted therapy in stroke rehabilitation,” J. Stroke, vol. 15, no. 3, p. 174, 2013.

58. H. G. Wu, Y. R. Miyamoto, L. N. G. Castro, B. P. Ölveczky, and M. A. Smith, “Temporal structure of motor variability is dynamically regulated and predicts motor learning ability,” Nature Neurosci., vol. 17, no. 2, pp. 312–321, Jan. 2014.

59. J. Frère and F. Hug, “Between-subject variability of muscle synergies during a complex motor skill,” Frontiers Comput. Neurosci., vol. 6, p. 99, Dec. 2012.

60. S. Muceli, A. T. Boye, A. d’Avella, and D. Farina, “Identifying representative synergy matrices for describing muscular activation patterns during multidirectional reaching in the horizontal plane,” J. Neurophysiol., vol. 103, no. 3, pp. 1532–1542, Mar. 2010.

61. J. F. Soechting and F. Lacquaniti, “Invariant characteristics of a pointing movement in man,” J. Neurosci., vol. 1, no. 7, pp. 710–720, Jul. 1981.

62. B. Cesqui, A. d’Avella, A. Portone, and F. Lacquaniti, “Catching a ball at the right time and place: Individual factors matter,” PLoS ONE, vol. 7, no. 2, p. e31770, Feb. 2012.

 

via Synergy-Based FES for Post-Stroke Rehabilitation of Upper-Limb Motor Functions – IEEE Journals & Magazine

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