Posts Tagged Sensors

[Abstract] A Music-Based Digital Therapeutic: Proof-of-Concept Automation of a Progressive and Individualized Rhythm-Based Walking Training Program After Stroke



The rhythm of music can entrain neurons in motor cortex by way of direct connections between auditory and motor brain regions.


We sought to automate an individualized and progressive music-based, walking rehabilitation program using real-time sensor data in combination with decision algorithms.


A music-based digital therapeutic was developed to maintain high sound quality while modulating, in real-time, the tempo (ie, beats per minute, or bpm) of music based on a user’s ability to entrain to the tempo and progress to faster walking cadences in-sync with the progression of the tempo. Eleven individuals with chronic hemiparesis completed one automated 30-minute training visit. Seven returned for 2 additional visits. Safety, feasibility, and rehabilitative potential (ie, changes in walking speed relative to clinically meaningful change scores) were evaluated.


A single, fully automated training visit resulted in increased usual (∆ 0.085 ± 0.027 m/s, P = .011) and fast (∆ 0.093 ± 0.032 m/s, P = .016) walking speeds. The 7 participants who completed additional training visits increased their usual walking speed by 0.12 ± 0.03 m/s after only 3 days of training. Changes in walking speed were highly related to changes in walking cadence (R2 > 0.70). No trips or falls were noted during training, all users reported that the device helped them walk faster, and 70% indicated that they would use it most or all of the time at home.


In this proof-of-concept study, we show that a sensor-automated, progressive, and individualized rhythmic locomotor training program can be implemented safely and effectively to train walking speed after stroke. Music-based digital therapeutics have the potential to facilitate salient, community-based rehabilitation.


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


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.

1. A. Schwarz, C. M. Kanzler, O. Lambercy, A. R. Luft and J. M. Veerbeek, “Systematic review on kinematic assessments of upper limb movements after stroke”, Stroke, vol. 50, no. 3, pp. 718-727, Mar. 2019.

2. S. C. Cramer, “The EXCITE trial: A major step forward for restorative therapies in stroke”, Stroke, vol. 38, no. 7, pp. 2204-2205, Jul. 2007.

3. L. Sawaki, “Use-dependent plasticity of the human motor cortex in health and disease”, IEEE Eng. Med. Biol. Mag., vol. 24, no. 1, pp. 36-39, Jan. 2005.

4. L. Ada, S. Dorsch and C. G. Canning, “Strengthening interventions increase strength and improve activity after stroke: A systematic review”, Austral. J. Physiotherapy, vol. 52, no. 4, pp. 241-248, 2006.

5. J. H. van der Lee, I. A. Snels, H. Beckerman, G. J. Lankhorst, R. C. Wagenaar and L. M. Bouter, “Exercise therapy for arm function in stroke patients: A systematic review of randomized controlled trials”, Clin. Rehabil., vol. 15, no. 1, pp. 20-31, Feb. 2001.

6. M. G. M. Kloosterman, G. J. Snoek and M. J. A. Jannink, “Systematic review of the effects of exercise therapy on the upper extremity of patients with spinal-cord injury”, Spinal Cord, vol. 47, no. 3, pp. 196-203, Mar. 2009.

7. R. J. Nudo, “Postinfarct cortical plasticity and behavioral recovery”, Stroke, vol. 38, no. 2, pp. 840-845, Feb. 2007.

8. C. E. Lang et al., “Observation of amounts of movement practice provided during stroke rehabilitation”, Arch. Phys. Med. Rehabil., vol. 90, no. 10, pp. 1692-1698, Oct. 2009.

9. N. N. Byl, E. A. Pitsch and G. M. Abrams, “Functional outcomes can vary by dose: Learning-based sensorimotor training for patients stable poststroke”, Neurorehabilitation Neural Repair, vol. 22, no. 5, pp. 494-504, Sep. 2008.

10. M. S. Jeffers et al., “Does stroke rehabilitation really matter? Part B: An algorithm for prescribing an effective intensity of rehabilitation”, Neurorehabilitation Neural Repair, vol. 32, no. 1, pp. 73-83, Jan. 2018.

11. K. L. Cox, V. Burke, T. J. Gorely, L. J. Beilin and I. B. Puddey, “Controlled comparison of retention and adherence in home-vs center-initiated exercise interventions in women ages 40–65 years: The S.W.E.A.T. Study (sedentary women exercise adherence trial)”, Preventive Med., vol. 36, no. 1, pp. 17-29, Jan. 2003.

12. W. J. Rejeski, L. R. Brawley, W. Ettinger, T. Morgan and C. Thompson, “Compliance to exercise therapy in older participants with knee osteoarthritis: Implications for treating disability”, Med. Sci. Sports Exerc., vol. 29, no. 8, pp. 977-985, Aug. 1997.

13. E. M. Sluijs, G. J. Kok and J. van der Zee, “Correlates of exercise compliance in physical therapy”, Phys. Therapy, vol. 73, no. 11, pp. 771-782, Nov. 1993.

14. A. Turton and C. Fraser, “The use of home therapy programmes for improving recovery of the upper limb following stroke”, Brit. J. Occupational Therapy, vol. 53, no. 11, pp. 457-462, Nov. 1990.

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.

32. C. Meinert and S. Tonascia, Clinical Trials: Design Conduct and Analysis, New York, NY, USA:Oxford Univ.Press, 1986.

33. V. Mathiowetz, G. Volland, N. Kashman and K. Weber, “Adult norms for the box and block test of manual dexterity”, Amer. J. Occupational Therapy, vol. 39, no. 6, pp. 386-391, Jun. 1985.

34. M. L. Delignette-Muller and C. Dutang, “fitdistrplus: An R package for fitting distributions”, J. Stat. Softw., vol. 64, no. 4, pp. 1-23, 2015.

35. R. Proffitt and B. Lange, “Innovative technologies special series”, Phys. Therapy, vol. 95, no. 3, pp. 441-448, 2015.

36. S. Nijenhuis, G. Prange, F. Amirabdollahian, F. Infarinato, J. Buurke and J. Reitman, “Feasibility of a second iteration wrist and hand supported training system for self-administered training at home in chronic stroke”, Proc. 8th Int. Conf. eHealth Telemed. Soc. Med., pp. 51-56, Apr. 2016.

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.

39. C. M. McCrimmon, C. E. King, P. T. Wang, S. C. Cramer, Z. Nenadic and A. H. Do, “Brain-controlled functional electrical stimulation therapy for gait rehabilitation after stroke: A safety study”, J. NeuroEng. Rehabil., vol. 12, no. 1, pp. 57, Dec. 2015.

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.

41. R. A. Bos et al., “A structured overview of trends and technologies used in dynamic hand orthoses”, J. NeuroEngineering Rehabil., vol. 13, no. 1, Dec. 2016.

42. S. C. Cramer et al., “Stroke recovery and rehabilitation research: Issues opportunities and the National Institutes of Health StrokeNet”, Stroke, vol. 48, no. 3, pp. 813-819, Feb. 2017.

43. W. Nelson, “Statistical methods for reliability data”, Technometrics, vol. 40, no. 3, pp. 254-256, Aug. 1998.

44. E. Castillo, Extreme Value Theory in Engineering, New York, NY, USA:Academic, 1988.

45. M. A. Guadagnoli and T. D. Lee, “Challenge point: A framework for conceptualizing the effects of various practice conditions in motor learning”, J. Motor Behav., vol. 36, no. 2, pp. 212-224, Jul. 2004.

46. D. A. Brown, T. D. Lee, D. J. Reinkensmeyer and J. E. Duarte, “Designing robots that challenge to optimize motor learning” in Neurorehabilitation Technology, Cham, Switzerland:Springer, 2016.

47. L. Marchal-Crespo and D. J. Reinkensmeyer, “Review of control strategies for robotic movement training after neurologic injury”, J. NeuroEng. Rehabil., vol. 6, no. 1, Dec. 2009.

48. J. B. Rowe, V. Chan, M. L. Ingemanson, S. C. Cramer, E. T. Wolbrecht and D. J. Reinkensmeyer, “Robotic assistance for training finger movement using a hebbian model: A randomized controlled trial”, Neurorehabilitation Neural Repair, vol. 31, no. 8, pp. 769-780, Aug. 2017.

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

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[Abstract] When eHealth Meets IoT: A Smart Wireless System for Post-Stroke Home Rehabilitation


In recent years, the emerging framework of the Internet of Things has been leading the technological landscape in a number of different fields and applications, from autonomous and connected vehicles to wearable devices. The healthcare system is benefiting from this continuously evolving environment since it leverages the opportunities offered by the ubiquitous and pervasive presence of connected objects and smart services. This attitude has given rise to the concept of eHealth, thus enabling new approaches and solutions for healthcare. In this framework we propose SmartPants, an IoT-based wireless system specifically designed for the remote rehabilitation of lower limbs in poststroke patients. The platform consists of multiple nodes used to monitor physical therapy and a software platform that provides real-time feedback on the execution by recognizing the type of exercise currently being performed by the patient. Our experimental results, evaluated through appropriate metrics, show that the proposed movement recognition algorithm provides very good results in terms of classification performance, independent of the considered classifier, with an average true positive rate of about 91 percent and an overall accuracy of around 96.5 percent.

via When eHealth Meets IoT: A Smart Wireless System for Post-Stroke Home Rehabilitation – IEEE Journals & Magazine

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[Abstract] Design of Powered Wearable Elbow Brace for Rehabilitation Applications at Clinic and Home – IEEE Conference Publication


Spasticity and contractures are secondary to most neurological and orthopaedic pathologies. The most conservative method of management of spasticity and contractures is passive stretching exercises. Robotic rehabilitation aims to provide a solution to this problem. We describe in details the design of a powered wearable orthosis especially designed for managing spasticity and contractures. The device is fully portable, allowing the patient to undergo repeated-passive-dynamic exercises outside the hospital environment. The design of the device is modular to make it adaptable to different anatomies and pathologies. The device is also fitted with electrogoniometers and torque sensors to record kinematics and dynamics of the patient, improving the insight of the clinicians to the rehabilitation of the patient, as well as providing data for further clinical and scientific investigations. The mechanical integrity of the device elements is simulated and verified.

via Design of Powered Wearable Elbow Brace for Rehabilitation Applications at Clinic and Home – IEEE Conference Publication

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[NEWS] MbientLab Launches its MIOTherapy Physical Therapy Wearable Technology

Unique technology platform uses smart sensors, therapeutic exercises and games to improve rehabilitation and recovery for patients undergoing physical therapy

MIO is a complete, wearable sensor solution that automatically measures, analyzes, and stores a patient's physical therapy data. (Graphic: Business Wire)

MIO is a complete, wearable sensor solution that automatically measures, analyzes, and stores a patient’s physical therapy data. (Graphic: Business Wire)

January 28, 2019 09:00 AM Eastern Standard Time


SAN FRANCISCO–(BUSINESS WIRE)–MbientLab, a company building the next generation of sensors and tools for the healthcare industry, has announced the availability of its MIOTherapy (MIO) wearable technology for physical and occupational therapists. MIO is the first wearable technology platform that integrates the effectiveness of traditional physical therapy with smart sensors, therapeutic exercises, games, and 3D visualization technology to personalize and improve outpatient rehabilitation and accelerate recovery.

.@mbientLab announces the launch of its @MioTherapy wearable technology for physical and occupational therapists to improve rehabilitation and recovery for patients undergoing #physicaltherapy.

Research shows that most physical therapy patients do not fully adhere to their plans for care because of factors that include lack of social support, self-doubt and perceived barriers to exercise.1 This results in millions of Americans living with preventable mobility issues and pain that reduce their quality of life. This lack of compliance also increases the cost of healthcare for these patients due to a higher number of urgent care and emergency room visits related to their injuries, and in some cases, inpatient post-acute care stays.

Using a unique combination of technology software and sensors, MIO helps physical and occupational therapists improve the experience and outcomes of therapy for their patients. MIO provides consistently accurate measurements that can be used to monitor and personalize treatment, increase patient compliance, reduce recovery time, and reduce healthcare costs.

“I’ve found the MIO based technology to be an invaluable tool in improving post-operative care for my patients where position is critical. It’s clear to me that MIO will be a great platform for doctors and physical therapists to analyze, adjust and customize patient treatment plans using precise measurements captured in real time,” said Frank Brodie, M.D., clinical faculty, University of California San Francisco. “This technology provides data that enables me to have an accurate understanding of my patients’ ongoing progress and adjust accordingly. I look forward to integrating MIO even more into my practice.”

Patients using MIO attach its sensors to any body part using stickers or flexible straps, so that physical therapists can measure, collect, and record all motion from a specific body area, delivering key insights about a patient’s range of motion and measurable progress through their exercise program. The extremely accurate sensors measure, analyze, and store a patient’s physical therapy data in the cloud for easy access and analysis via the MIO App. MIO also offers real-time 3D visualization, providing an exact picture of what the patient is doing at any moment, and can be used in-office or via a telehealth platform with clinical oversight.

“We are excited to offer physical and occupational therapists a wearable technology platform that improves patient and provider engagement, and ultimately supports better results and a quicker recovery time for patients,” said Laura Kassovic, co-founder and CEO of MbientLab. “Serving as their virtual assistant, MIO will help physical therapists rethink how they provide physical therapy and work to heal their patients so they can get back to doing the things they enjoy.”

MIO has undergone extensive sensor testing with more than a dozen third-party users, including physical therapists, researchers, clinics, and university labs. Since 2013, there have been more than 250 papers published on the use of the MbientLab sensors used in MIO. Physicians at the University of California, San Francisco have demonstrated that the MIO sensors can increase patient compliance by 20 percent to 80 percent in post-operative retinal surgery patients.2 Researchers at Duke University also found an average cost-savings of $2,745 per patient undergoing virtual physical therapy with MIO compared to traditional physical therapy.3

MIO is now commercially available in the United States and internationally and can be purchased by physical and occupational therapists, caregivers and researchers at MIO is available through monthly subscription plans that include the app, sensors, and access to the cloud, as well as unlimited and free customer support via email, and on-site services.

About MIOTherapy

MIOTherapy is the first wearable technology that integrates the effectiveness of traditional physical therapy with therapeutic exercises, games, and smart sensors to improve outpatient rehabilitation and speed up recovery. Visit or follow @miotherapy on Twitter, @miotherapy on Facebook and @miotherapy on Instagram for more information.

About MbientLab

MbientLab is building the next generation of sensors and tools for the healthcare industry including motion capture and analytics, biometrics, kinematics, industrial control, research and product development. Visit for more information.

Picha KJ, Howell DM. A model to increase rehabilitation adherence to home exercise programmes in patients with varying levels of self-efficacy. Musculoskeletal Care, 2018; 16:233-237.

Brodie et al., Novel positioning with real-time feedback for improved postoperative positioning: pilot study in control subjects; May 2017

Duke Clinical Research Institute, VERITAS research study, 2016


for MbientLab
Hannah Boxerman


via MbientLab Launches its MIOTherapy Physical Therapy Wearable Technology | Business Wire

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[Abstract + References] Novel Assessment Measures of Upper-Limb Function in Pre and Poststroke Rehabilitation: A Pilot Study – IEEE Conference Publication


Hand function assessment is essential for upper limb rehabilitation of stroke survivors. Conventional acquisition devices have inherent and restrictive difficulties for their clinical usage. Data gloves are limited for applications outside the medical environment, and motion tracking systems setup are time and personnel demanding. We propose a novel instrument designed as a replica of a glass, equipped with an omnidirectional vision system to capture hand images and an inertial measurement unit for movements kinematic data acquisition. Four stroke survivors were invited as volunteers in pre and post-treatment experiments for its evaluating. The exercise of drinking water from a glass was elected for the trails. Before treatment, subjects used their contralesional and ipsilateral hands to perform them. Two main functional features were found in the data analysis. There were differences between limbs in the grasping hand postures, mainly in the index and thumb abduction angle, and in the task timing. After treatment, two volunteers repeated the protocol with their contralesional hands. Changes in the features were observed, index and thumb abduction angles were greater in both cases, and tasks timing were altered in distinct ways. These preliminary results suggest the instrument can be used both in evaluation of hand functional deficit and rehabilitation progress. Improvements and future work are also presented.
1. R. L. Sacco, S. E. Kasner, J. P. Broderick, L. R. Caplan, A. Culebras, M. S. Elkind, M. G. George, A. D. Hamdan, R. T. Higashida, B. L. Hoh et al., “An updated definition of stroke for the 21st century: a statement for healthcare professionals from the american heart association/american stroke association”, Stroke, vol. 44, no. 7, pp. 2064-2089, 2013.

2. C. A. Doman, K. J. Waddell, R. R. Bailey, J. L. Moore, C. E. Lang, “Changes in upper-extremity functional capacity and daily performance during outpatient occupational therapy for people with stroke”, American Journal of Occupational Therapy, vol. 70, no. 3, pp. 7003290040pl-7003290040p11, 2016.

3. B. Brouwer, M. V. Sale, M. A. Nordstrom, “Asymmetry of motor cortex excitability during a simple motor task: relationships with handedness and manual performance”, Experimental Brain Research, vol. 138, no. 4, pp. 467-476, 2001.

4. J. Langan, P. van Donkelaar, “The influence of hand dominance on the response to a constraint-induced therapy program following stroke”, Neurorehabilitation and neural repair, vol. 22, no. 3, pp. 298-304, 2008.

5. H. I. Krebs, M. L. Aisen, B. T. Volpe, N. Hogan, “Quantization of continuous arm movements in humans with brain injury”, Proceedings of the National Academy of Sciences, vol. 96, no. 8, pp. 4645-4649, 1999.

6. B. Fisher, C. Winstein, M. Velicki, “Deficits in compensatory trajectory adjustments after unilateral sensorimotor stroke”, Experimental brain research, vol. 132, no. 3, pp. 328-344, 2000.

7. H. Sugarman, A. Avni, R. Nathan, A. Weisel-Eichler, J. Tiran, “Movement in the ipsilesional hand is segmented following unilateral brain damage”, Brain and cognition, vol. 48, no. 2-3, pp. 579-587, 2002.

8. D. A. Nowak, “The impact of stroke on the performance of grasping: usefulness of kinetic and kinematic motion analysis”, Neuroscience & Biobehavioral Reviews, vol. 32, no. 8, pp. 1439-1450, 2008.

9. M. Coluccini, E. S. Maini, C. Martelloni, G. Sgandurra, G. Cioni, “Kinematic characterization of functional reach to grasp in normal and in motor disabled children”, Gait & posture, vol. 25, no. 4, pp. 493-501, 2007.

10. E. Jaspers, H. Feys, H. Bruyninckx, J. Harlaar, G. Molenaers, K. Desloovere, “Upper limb kinematics: development and reliability of a clinical protocol for children”, Gait & posture, vol. 33, no. 2, pp. 279-285, 2011.

11. D. A. Nowak, J. Hermsdörfer, H. Topka, “Deficits of predictive grip force control during object manipulation in acute stroke”, Journal of neurology, vol. 250, no. 7, pp. 850-860, 2003.

12. R. W. Bohannon, “Adequacy of hand-grip dynamometry for characterizing upper limb strength after stroke”, Isokinetics and exercise science, vol. 12, no. 4, pp. 263-265, 2004.

13. H. Zhou, H. Hu, “Human motion tracking for rehabilitationâĂŤa survey”, Biomedical Signal Processing and Control, vol. 3, no. 1, pp. 1-18, 2008.

14. A. C. P. Rocha, E. Tudella, L. M. Pedro, V. C. R. Appel, L. G. P. da Silva, G. A. d. P. Caurin, “A novel device for grasping assessment during functional tasks: preliminary results”, Frontiers in bioengineering and biotechnology, vol. 4, pp. 16, 2016.

15. E. Taub, G. Uswatte, “Constraint-induced movement therapy: bridging from the primate laboratory to the stroke rehabilitation laboratory”, Journal of Rehabilitation Medicine-Supplements, vol. 41, pp. 34-40, 2003.

16. R. d. N. B. Marques, A. C. Magesto, R. E. Garcia, C. B. d. Oliveira, G. d. S. Matuti, “Efeitos da terapia por contensão induzida nas lesões encefálicas adquiridas”, Fisioterapia Brasil, vol. 17, no. 1, pp. f-30, 2016.

17. E. E. Butler, A. L. Ladd, L. E. LaMont, J. Rose, “Temporal-spatial parameters of the upper limb during a reach & grasp cycle for children”, Gait & posture, vol. 32, no. 3, pp. 301-306, 2010.

18. E. E. Butler, A. L. Ladd, S. A. Louie, L. E. LaMont, W. Wong, J. Rose, “Three-dimensional kinematics of the upper limb during a reach and grasp cycle for children”, Gait & posture, vol. 32, no. 1, pp. 72-77, 2010.

19. L. Gauthier, Structural brain changes produced by different motor therapies after stroke, 2011.

20. L. M. Pedro, G. A. de Paula Caurin, “Kinect evaluation for human body movement analysis”, Biomedical Robotics and Biomechatronics (BioRob) 2012 4th IEEE RAS & EMBS International Conference on, pp. 1856-1861, 2012.

21. A. Hussain, S. Balasubramanian, N. Roach, J. Klein, N. Jarrassé, M. Mace, A. David, S. Guy, E. Burdet, “Sitar: a system for independent task-oriented assessment and rehabilitation”, Journal of Rehabilitation and Assistive Technologies Engineering, vol. 4, pp. 2055668317729637, 2017.

22. L. R. L. Cardoso, M. N. Martelleto, P. M. Aguiar, E. Burdet, G. A. P. Caurin, L. M. Pedro, “Upper limb rehabilitation through bicycle controlling”, 24th International Congress of Mechanical Engineering, 2017.

23. M. N. Martelleto, P. M. Aguiar, E. Burdet, G. A. P. Caurin, R. V. Aroca, L. M. Pedro, “Instrumented module for investigation of contact forces for use in rehabilitation and assessment of bimanual functionalities”, 24th International Congress of Mechanical Engineering, 2017.


via Novel Assessment Measures of Upper-Limb Function in Pre and Poststroke Rehabilitation: A Pilot Study – IEEE Conference Publication

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[Abstract] sEMG Bias-driven Functional Electrical Stimulation System for Upper Limb Stroke Rehabilitation


It is evident that the dominant therapy of functional electrical stimulation (FES) for stroke rehabilitation suffers from heavy dependency on therapists experience and lack of feedback from patients status, which decrease the patients’ voluntary participation, reducing the rehabilitation efficacy. This paper proposes a closed loop FES system using surface electromyography (sEMG) bias feedback from bilateral arms for enhancing upper-limb stroke rehabilitation. This wireless portable system consists of sEMG data acquisition and FES modules, the former is used to measure and analyze the subject’s bilateral arm motion intention and neuromuscular states in terms of their sEMG, the latter of multi-channel FES output is controlled via the sEMG bias of the bilateral arms. The system has been evaluated with experiments proving that the system can achieve 39.9 dB signal-to-noise ratio (SNR) in the lab environment, outperforming existing similar systems. The results also show that voluntary and active participation can be effectively employed to achieve different FES intensity for FES-assisted hand motions, demonstrating the potential for active stroke rehabilitation.
Published in: IEEE Sensors Journal ( Early Access ) Date of Publication: 18 June 2018

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via sEMG Bias-driven Functional Electrical Stimulation System for Upper-Limb Stroke Rehabilitation – IEEE Journals & Magazine

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[Abstract] A wearable monitoring system for at-home stroke rehabilitation exercises: A preliminary study


When stroke survivors perform rehabilitation exercises in clinical settings, experienced therapists can evaluate the associated quality of movements by observing only the initial part of the movement execution so that they can discourage therapeutically undesirable movements effectively and reinforce desirable ones as much as possible in the limited therapy time. This paper introduces a novel monitoring platform based on wearable technologies that can replicate the capability of skilled therapists. Specifically, we propose to deploy five wearable sensors on the trunk, and upper and forearm of the two upper limbs, analyze partial to complete observation data of reaching exercise movements, and employ supervised machine learning to estimate therapists’ evaluation of movement quality. Estimation performance was evaluated using F-Measure, Receiver Operating Characteristic Area, and Root Mean Square Error, showing that the proposed system can be trained to evaluate the movement quality of the entire exercise movement using as little as the initial 5s of the exercise performance. The proposed platform may help ensure high quality exercise performance and provide virtual feedback of experienced therapists during at-home rehabilitation.

I. Introduction

Stroke is a leading cause of death and disabilities in adults, and the majority of its survivors suffer from upper extremity paresis [1]. There is scientific evidence that repetitive rehabilitation exercises and training could improve motor abilities as a result of motor learning processes [2]. Among many, a reaching movement is a fundamental component of daily movement that requires the coordination of multiple upper extremity segments [3]. It is shown that repetitive reaching exercises improve the smoothness, precision, and speed of arm movements [4]. To continue to improve and to sustain motor function, it is clinically important that patients continue to engage in rehabilitation exercises even outside the clinical settings [5], which emphasizes the importance of the home-based therapy.


via A wearable monitoring system for at-home stroke rehabilitation exercises: A preliminary study – IEEE Conference Publication

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[WEB SITE] Wearable tech aids stroke patients – BBC News

Scientists in the US are developing wearable sensors to speed up the recovery of stroke patients.

The sensors are able to send information to doctors continuously.

The team developing the system says it could allow therapists to more closely monitor the effectiveness of their care.

Details of the study were released at the recent annual meeting of the American Association for the Advancement of Science in Texas.

Lizzy McAninch had a stroke two years ago. She could not move or speak or swallow for several weeks.

Lizzy is testing out wearable sensors that might speed her recovery.

They look like small white sticking plasters, but they send information wirelessly to her medical team.

She is a doctor herself and can see how they could help her.


SHIRLEY RYAN ABILITYLAB They look like small sticking plasters

“This technology to put sensors on the body to assess which muscle groups work or not can really pinpoint the areas affected by the stroke and can target therapies to specifically improve those issues,” she told BBC News.

The sensors continue to send back readings even after she has finished her exercises. This means that her therapist Kristen Hohl, from the Shirley Ryan AbilityLab in Chicago, can monitor her progress at home.

“As a therapist, I think about what my patients are doing at home. Are they able to carry through the recommendations I’m giving them as a therapist to do more? Do we see that they are walking more or do we see them engaging in conversations?

“Those are the types of things that I can get feedback from the sensors where currently I have to rely on what they tell me they have done.”


 SHIRLEY RYAN ABILITYLAB  The team is gathering large amounts of data

The challenge for the scientists was to pack a lot of electronics on to a small flexible material and still make it comfortable for the patient to wear for a long time.

“It is almost mechanically imperceptible to the patient who is wearing the device,” according to John Rogers, of Northwestern University in Chicago, who developed the sensors.

“And you can embed all sorts of advanced sensor functionality, microprocessor computing capability, power supplies and WiFi into this very unusual platform, and that is the uniqueness of what we do.”

By the end of this year, the research team will have more information than ever before on stroke recovery. The scientists believe that their study could transform the way patients are treated in the future.

Lizzy and scientist



via Wearable tech aids stroke patients – BBC News

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[Abstract+References] A Computer-Assisted System with Kinect Sensors and Wristband Heart Rate Monitors for Group Classes of Exercise-Based Rehabilitation


Exercise-based rehabilitation for chronic conditions such as cardiovascular disease, diabetes, and chronic obstructive pulmonary disease, constitutes a key element in reducing patient symptoms and improving health status and quality of life. However, group exercise in rehabilitation programmes faces several challenges imposed by the diversified needs of their participants. In this direction, we propose a novel computer-assisted system enhanced with sensors such as Kinect cameras and wristband heart rate monitors, aiming to support the trainer in adapting the exercise programme on-the-fly, according to identified requirements. The proposed system design facilitates maximal tailoring of the exercise programme towards the most beneficial and enjoyable execution of exercises for patient groups. This work contributes in the design of the next-generation of computerised systems in exercise-based rehabilitation.


  1. 1.
    Sagar VA, Davies EJ, Briscoe S et al (2015) Exercise-based rehabilitation for heart failure: systematic review and meta-analysis. Open Hear 2:e000163. Scholar
  2. 2.
    Kouidi E, Karagiannis V, Grekas D et al (2010) Depression, heart rate variability, and exercise training in dialysis patients. Eur J Cardiovasc Prev Rehabil 17:160–167. Scholar
  3. 3.
    Nocon M, Hiemann T, Müller-Riemenschneider F et al (2008) Association of physical activity with all-cause and cardiovascular mortality: a systematic review and meta-analysis. Eur J Cardiovasc Prev Rehabil 15:239–246. Scholar
  4. 4.
    Vanhees L, Geladas N, Hansen D et al (2012) Importance of characteristics and modalities of physical activity and exercise in the management of cardiovascular health in individuals with cardiovascular risk factors: recommendations from the EACPR. Part II. Eur J Prev Cardiol 19:1005–33.
  5. 5.
    Thompson PD, Arena R, Riebe D, Pescatello LS (2013) ACSM’s new preparticipation health screening recommendations from ACSM’s guidelines for exercise testing and prescription, 9th (edn). Curr Sports Med Rep 12:215–217.
  6. 6.
    Raedeke TD (2007) The relationship between enjoyment and affective responses to exercise. J Appl Sport Psychol 19:105–115. Scholar
  7. 7.
    Borg G, Hassmén P, Lagerström M (1987) Perceived exertion related to heart rate and blood lactate during arm and leg exercise. Eur J Appl Physiol Occup Physiol 56:679–685. Scholar
  8. 8.
    Claes J, Buys R, Woods C et al (2017) PATHway I: Design and rationale for the investigation of the feasibility, clinical effectiveness and cost-effectiveness of a technology-enabled cardiac rehabilitation platform. BMJ Open. Scholar
  9. 9.
    Filos D, Triantafyllidis A, Chouvarda I et al (2016) PATHway: decision support in exercise programmes for cardiac rehabilitation. Stud Health Technol Inform 224:40–45Google Scholar
  10. 10.
    Chang K-M, Liu S-H (2011) Wireless portable electrocardiogram and a tri-axis accelerometer implementation and application on sleep activity monitoring. Telemed J E Health 17:177–184. Scholar
  11. 11.
    Chatzitofis A, Zarpalas D, Filos D et al (2017) Technological module for unsupervised, personalized cardiac rehabilitation exercising. In: 2017 IEEE 41st annual computer software and applications conference (COMPSAC).
  12. 12.
    Claes J, Buys R, Avila A et al (2017) Validity of heart rate measurements by the Garmin Forerunner 225 at different walking intensities. J Med Eng Technol 41:480–485. Scholar
  13. 13.
    Kranz M, Möller A, Hammerla N et al (2013) The mobile fitness coach: towards individualized skill assessment using personalized mobile devices. Pervasive Mob Comput 9:203–215. Scholar
  14. 14.
    Compernolle S, Vandelanotte C, Cardon G et al (2015) Effectiveness of a web-based, computer-tailored, pedometer-based physical activity intervention for adults: a cluster randomized controlled trial. J Med Internet Res 17:e38. Scholar
  15. 15.
    Peng H-T, Song C-Y (2015) Predictors of treatment response to strengthening and stretching exercises for patellofemoral pain: An examination of patellar alignment. Knee 22:494–498. Scholar
  16. 16.
    Buttussi F, Chittaro L (2008) MOPET: A context-aware and user-adaptive wearable system for fitness training. Artif Intell Med 42:153–163. Scholar
  17. 17.
    Triantafyllidis AK, Koutkias VG, Chouvarda I, Maglaveras N (2014) Development and usability of a personalized sensor-based system for pervasive healthcare. In: 2014 36th Annual international conference of the IEEE engineering in medicine and biology society EMBC.

via A Computer-Assisted System with Kinect Sensors and Wristband Heart Rate Monitors for Group Classes of Exercise-Based Rehabilitation | SpringerLink

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