Posts Tagged home rehabilitation

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

Abstract

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

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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[PhD Thesis] The Design Of Exergaming Systems For Autonomous Rehabilitation

A PhD thesis by Michele Pirovano (Politecnico di Milano, Italy), studying the feasibility of at-home rehabilitation using exergames for stroke patients. It includes the results of a 3-months pilot test using an original exergaming system developed by the author.

Download the thesis for free at http://www.michelepirovano.com/pdf/MichelePirovano_Thesis_Final_2015_01_09.pdf

via PhD Thesis: The Design Of Exergaming Systems For Autonomous Rehabilitation – Gabriele Ferri’s research blog

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[THESIS] Multi-sensors for realization of home tele-rehabilitation

Abstract

Research in assistive healthcare, in particular home rehabilitation, has spawn huge potential owing to the recent advancement of internet-of-things technology and the wearable hardware, Inertial Measurement Unit (IMU) in wearable sensors and smartphones become a affordable for community usage. However, using low cost IMU sensors or smartphones face certain challenges, such as accurate orientation estimation for lower-limb motion tracking, which is usually less of a problem in specialized motion tracking sensor devices. To address these issues, the candidate has made three main contributions: a new and better orientation estimation algorithm which combines quaternion-based Kalman filter with corrector estimates using gradient descent (KFGD), an auto-detector of post-filtered lower-limb orientation signal oscillation and the machine-learning based state identification of rehabilitation exercise. Firstly, obtaining accurate orientation readings with noise-prone IMU and post-processing drift is a key challenge in motion tracking research. It is the result of accumulated errors over the integration of the gyroscope signal to calculate the angular displacement, in other words, the orientation of the limb, in the motion tracking application. Thus, the candidate proposes two sensor fusion algorithms: the complementary filter feedback (CFF) and the quaternion-based Kalman filter with corrector estimates using gradient descent (KFGD). The complementary filter feedback (CFF) focuses on the components’ performance of high-pass filter (from angular velocity) and low-pass filter (from fusion of gravity and earth magnetic field). These components contribute to the estimated orientation while the proposed feedback loop can correct the drift. KFGD is later introduced to further improve the limitation of the low-pass filter and the fixed fusion threshold of the CFF. Gradient descent method and quaternion-based Kalman Filter are chosen for their progressive features. The performance was evaluated on the case study of early stage rehabilitation exercises, namely, leg extension and sit-to-stand. The result shows that CFF is capable of fast motion tracking and confirms that the feedback loop is capable of correcting errors caused by integration of gyroscope data. KFGD outperforms the state-of-the-art Madgwick algorithm and is recommended for obtaining accurate orientation readings using motion sensors. Secondly, upon observing the characteristics of the post-filtered orientation signals of the lower-limb, a noticeable artifact in the output signal that it would oscillate from positive to negative and vice versa. To address the oscillations in the signals of both motion capture and inertial measurement sensors, the candidate applied machine learning algorithms and compared them with the rule-based approach. Machine learning methods, such as Logistic Regression, Support Vector Machine and Multilayer perceptron, were adopted in order to automatically detect the oscillation. The results showed that machine learning methods are able to learn the oscillation patterns in wearable sensor data and identify the tendency of fluctuation thereby allowing the errors to be filtered out more efficiently than rule-based method. Lastly, in order to realize meaningful home rehabilitation, there is a need for informative feedback or intervention in parallel with the exercise monitoring. The study aims to use the collected data and the understanding of wearable signal to simulate the high-level observations by the physiotherapist towards the patients and provide informative feedback during exercising at home. Therefore, the candidate proposes the study on machine-learning based state identification of rehabilitation exercise by using wearable sensors on the lower limbs. The informative feedback and quality assessment could be obtained by selectively segmenting the exercise into four states: rest, raise, hold and drop. The segmentation potentially increases the frequency of detection resulting in almost real-time feedback. In addition, identifying the abnormal sequences against the correct pattern in the respective state results in more specific and informative feedback. In this work, the candidate analyses the impact and derives valuable insights of the extracted sensor signals in relation to the predicted. As a result, the predictive model yields up to 95.89% (SVM) and 94.04% (SVM) accuracy for binary and multi-label pattern recognition respectively. The experiment and recommended framework show the efficiency and potential of using signal data as features in motion-based exercise pattern recognition. The work presented in this thesis demonstrates the realization of home rehabilitation from the hardware-level to the simulation of user intervention. The methodologies exploit the a ordable hardware to correctly track the limb motion while the motion signal prediction model and analysis boost the potential of intervention strategy for the user’s home exercise feedback.

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via Multi-sensors for realization of home tele-rehabilitation | DR-NTU

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[ARTICLE] Feedback Design in Targeted Exercise Digital Biofeedback Systems for Home Rehabilitation: A Scoping Review – Full Text PDF

Digital biofeedback systems (DBSs) are used in physical rehabilitation to improve outcomes by engaging and educating patients and have the potential to support patients while doing targeted exercises during home rehabilitation. The components of feedback (mode, content, frequency and timing) can influence motor learning and engagement in various ways. The feedback design used in DBSs for targeted exercise home rehabilitation, as well as the evidence underpinning the feedback and how it is evaluated, is not clearly known. To explore these concepts, we conducted a scoping review where an electronic search of PUBMED, PEDro and ACM digital libraries was conducted from January 2000 to July 2019. The main inclusion criteria included DBSs for targeted exercises, in a home rehabilitation setting, which have been tested on a clinical population. Nineteen papers were reviewed, detailing thirteen different DBSs. Feedback was mainly visual, concurrent and descriptive, frequently providing knowledge of results. Three systems provided clear rationale for the use of feedback. Four studies conducted specific evaluations of the feedback, and seven studies evaluated feedback in a less detailed or indirect manner. Future studies should describe in detail the feedback design in DBSs and consider a robust evaluation of the feedback element of the intervention to determine its efficacy.

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via Feedback Design in Targeted Exercise Digital Biofeedback Systems for Home Rehabilitation: A Scoping Review – Sensors – X-MOL

 

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[Project] Functional Electrical Stimulation for at Home Rehabilitation | WalkHome Project | H2020 | CORDIS | European Commission

Objective

The Context
Stroke has huge human and economic cost. 1 million people suffer strokes in Europe every year, with an average life expectancy after stroke of 8 years. Roughly 20% of stroke survivors suffer from drop foot, with 45 billion euros spent on rehabilitating stroke patients in Europe every year.
The opportunity:
FES offers the tantalising prospect of retraining voluntary motor functions such as walking. However:
– FES rehabilitation must be carried out in a hospital with the support of trained healthcare professionals;
– Transporting patients and supervising treatment is expensive;
– Patient’s treatment plan is sub-optimal;
– Per patient rehabilitation costs reach 32,000 euros
Our solution:
Fesia WalkHome is a FES rehabilitation device for drop foot patients which can be administered by the patient in their own home. This not only reduces costs by 43% but also means patients can have an optimal treatment plan, improving their speed of recovery.
The use of Fesia Walk at home will give autonomy, independence and improve the quality of life for chronic patients. It will also mean a substantial reduction of waiting lists, health costs, number of physician office visits, and carer support.
The Project:
WalkHome represents a disruptive change of paradigm for the FES rehabilitation standard of care. The aim of the phase 1 project is to improve our understanding of the EU market for FES rehabilitation, identifying regional market variations in terms of key decision makers, appropriate business models, pricing structure and identifying which are the most attractive markets for us to use as a beachhead. We will also analyse what key improvements need to be made to the existing technology to create the new FES home care rehabilitation market.
The Market:
Currently, there is no FES rehabilitation technology that is offered outside of a clinical setting. We estimate that this new home FES rehabilitation market could be worth up to 40 billion euros in Europe alone.

via Functional Electrical Stimulation for at Home Rehabilitation | WalkHome Project | H2020 | CORDIS | European Commission

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[Abstract] BIGHand – A bilateral, integrated, and gamified handgrip stroke rehabilitation system for independent at-home exercise – Demo Video

Effective home rehabilitation is important for recovery of hand grip ability in post-stroke individuals. This paper presents BIGHand, a bilateral, integrated, and gamified handgrip stroke rehabilitation system for independent at-home exercise. BIGHand consists of affordable sensor-integrated hardware (Vernier hand dynamometers, Arduino Uno, interface shield) used to obtain real-time grip force data, and a set of exergames designed as parts of an interactive structural rehabilitation program. This program pairs targeted difficulty progression with user-ability scaled controls to create an adaptive, challenging, and enticing rehabilitation environment. This training prepares users for the many activities of daily living (ADLs) by targeting strength, bilateral coordination, hand-eye coordination, speed, endurance, precision, and dynamic grip force adjustment. Multiple measures are taken to engage, motivate, and guide users through the at-home rehabilitation process, including “smart” post-game feedback and in-game goals.

Demo video 

via BIGHand – A bilateral, integrated, and gamified handgrip stroke rehabilitation system for independent at-home exercise

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[ARTICLE] Preliminary Analysis of Perception, Knowledge and Attitude of Home Health Patients Using Tele Rehabilitation in Riyadh, Saudi Arabia – Full Text

ABSTRACT

Telerehabilitation is defined as delivery of rehabilitation services over telecommunication networks and the internet, which comprise of clinical assessment (the patient’s functional abilities in his or her environment) and clinical therapy.This new area  of medical advancement, using state of the art technology is developing at a great speed and is  definitely going to be the next milestone in health care revolution.The objective of this study was to explore the awareness, knowledge and perception of the patients for using telerehabilitation as a medium to provide physiotherapy services as a part of home healthcare services.  A pretest-post test design was used where the home healthcare patients (n = 90) aged between 50 -75 years were asked to express views by given a validated modified TUQ questionnaire followed by an indepth interviewing to develop a key understanding regarding the themes. Interviews were transcribed and a qualitative thematic analysis was conducted. The awareness level regarding the  telerehabilitation changed significantly from 57% to 96% post session(p<0.05). Similarly, the knowledge of the participants regarding  online consultation, followup and online therapy  changed significantly from 50%, 47% and 57% to 96%, 76% and 96% respectively post session of rehabilitation(p<0.05). The perception level regarding the key benefits including  its usage in emergency(83%), convenience of no travel(84%), ease of getting treated at home(97%) and  availability of specialist consultation (84%) were the prime ideas for excellent rating among 95% participants (p<0.05) post session. Findings are helpful to health practitioners in designing their intervention programs across the kingdom. However the actual impact could be only derived from future studies which has to conducted based on different clinical conditions.

Introduction

Telerehabilitation is defined as the provision and delivery of rehabilitation health services at a distance using information and communication technologies and tools (Tan 2005; Russell 2007). Throughout the world, the health care practices is going through major transformation as it is driven through sea change because of the increased use of technology. The kingdom of Saudi Arabia too is witnessing a massive change with significant restructuring of healthcare systems with some major high-end technology driven development solutions. The increased demand is created on account of rapidly increasing saudi population including the growing elderly community, changing disease patterns, global climatic changes and financial inequity (Mahmood 2018).  According to a United nations report the elderly population of Saudi Arabia  those aged 60 and above is projected to increase from 3% in 2010 to 9.5% and 18.4% in 2035 and 2050, respectively (UN Report, 2018).

Similarly, comparing this phenomenon to an average life expectancy of the population in Saudi Arabia, the latest WHO data published in 2018, suggests that Saudi male and female have an average of 73.5 and female 76.5 life years with an average life expectancy of 74.8 years as against an average world life expectancy of 84 years.The increased demand in kingdom also raised because of immense economic pressure with steep fall in global oil prices in 2015-16 affecting the GDP significantly thereby been one of the key stimulus for the government to take timely corrective actions and diversify the economy from heavily oil dependent to develop other verticals for revenue generation (MoH Report, 2018).

Brian child of Crown Prince HH Mohammad Bin Salman, Vision 2030 was adopted in April 2016 and has identified its priorities across all economic sectors and serves as a roadmap for the economic development of the KSA with development of health services been one of the most important key themes. Therefore, as a part of realization of this vision the government strongly supports the partnership of private and public sectors and been seen as a strong indication of the Government’s commitment for making healthcare accessible to its citizens irrespective of the disparities available in the Saudi society (Vision 2030 Report, 2016). Access to healthcare generally relates to people’s ability to use health services when and where they are needed. Determinants of healthcare access are the types and quality of services, including the costs, time, distance (ease of travel) as well as regular interface between service users and healthcare providers. Saudi Arabia is the largest and fastest growing health care market in the region and is estimated to reach $40 billion by 2020 (NTP 2020 Report, 2016).

Moreover, the steep increase in the number of hospitals across all major cities of KSA are run by both government and private organizations which use  corporate business strategies and technology driven specializations, which aim to create demand as well as attract high number patients as the facilities in majority of these hospitals are world class.Among the various strategies listed in the NTP Report 2020, one of the key components of making healthcare accessible across the kingdom is the enhanced use of telemedicine (NTP 2020 Report, 2016). In the last one decade the health services across the kingdom have taken gigantic leap jumps with private healthcare taking lead and using innovations in delivering healthcare. One of such innovations is using Home Healthcare for delivering physiotherapy and other rehabilitation based services for the patients at home (Pulse Report 2018).

Rehabilitation is a very important component in medical care and helps in propelling patient to preinjury level. It is a well known fact that in all long term cases which requires follow-ups such as in surgical cases and other debilitating disorders including Stroke, Cancer, Multiple Sclerosios, rehabilitation is time consuming and financially constraining. To add to this, patients travelling long distances for treatment, it is not only physically challenging but emotionally draining too and especially in case of geriatric patients.Therefore home tele rehabilitation programs, are winding up progressively as an elective method of service delivery. In the western countries, quite a number of research studies has been proved that the Telerehabilitation for the delivery of health services is quite effective, however the scope of using such services in the kingdom is still novice and requires a detailed study, (Hailey et al., 2010, Johansson and Wild 2011, Chang et al 2019     ).

There are scant studies to prove its efficacy in the developing countries as its successful will depends on a number of factors (Clemens et al 2018) . However, among all the variables, the two most important are the technological component and second been its implementation in real terms (Jackson and McClean 2012, Clemens et al 2018). Accordingly, these both are of extreme critical importance from the patient satisfaction point of view. The perceptions of the stakeholders, i.e. the patient and the members of the Rehabilitation team are of utmost importance for its use and wide spread application.The home healthcare services in Saudi Arabia is still in infancy stages with few delivery partners across the kingdom. The usage of telerehabilitation is even more nascent, as the perception of patients in using such a technology for delivering healthcare would be quite critical and important to understand the phenomenon which would be quite useful in framing the guidelines for its applications at a mass level, (Alaboudi et al 2016).

Therefore, this study is an attempt to study the awareness, knowledge and perceptions of  the home healthcare patients in using physiotherapy services delivered via cloud based telerehabilitation. This study, to our knowledge is the first of its kind in the kingdom especially from the perspective of home healthcare patients. It aims to explore the key ideas which might work in favour or against the successful implementation of telerehabilitation used for the home healthcare delivery.

Materials and Methods

The pretest-post test study design was conducted on home healthcare patients so as to obtain an in-depth understanding of the patients’ perception about telerehabilitation services which they will receive as a part of home health services. While a few studies  conducted earlier emphasized about telemedicine to be a key part in delivery of health services, however none of the studies emphasized on perception of patients to implement telerehabilitation as part of home healthcare (Clemens et al 2018, Khalil et al 2018).

Due necessary approval were taken from the ethical clearance committee of the respective organization, which is a reputed home healthcare organization based in Riyadh. In order to recruit participants for the study, sample population were selected from a pool of home healthcare patients who were undergoing treatment under one of the most prominent home healthcare organizations in the kingdom, which incidentally was the only first licensed stand-alone home healthcare services company in Riyadh province.

The study was conducted from Jan 15 to May 30, 2019. In this context, non-probability sampling method was used. Out of 113 home healthcare patients who underwent treatment for different ailments, 90 were randomly selected who also gave their consent to participate in the study out of which 57 were males and 33 were females. Those patients who suffered from orthopedic problems such as Knee pain, low back ache, disc prolapse etc. or underwent orthopedic surgeries such as knee replacement or meniscectomy etc. participated in the study. The study mainly included common geriatric patients for the study who were willing to participate but excluded the pediatric and the critical care, neurological and cardiac patients as they underwent major surgeries such as for stroke or CABG and also were unable to respond directly to answer the questions. The patients who were able respond in English or Arabic were recruited for the study.

Based on literature review and discussion with key stakeholders, a questionnaire and an the interview guide was prepared, modified from Telehealth Usability Questionnaire (TUQ) based on key themes of perceived usefulness, ease of use and learnability,  Interaction quality, Reliability and Satisfaction and future use (Langbecker et al 2017) . The questionnaire was converted to Arabic version adapted from the original English version and pilot tested for the home healthcare patients using both forward and backward translation methods and achieved very acceptable score of confirmatory factor analysis of 0.78 using SPSS. It was also pilot tested   for the members of the rehabilitation team. The questionnaires as given in Appendix 1 were responded by the patients and the members of the rehabilitation team followed by a semi structured individual interview from the patient as well as from the team members involved in providing home health services. The interviews were audio recorded and transcribed verbatim using Text Analysis Markup System (TAMS) Analyzer as suggested by Yin (Yin 2013).

The Tele-rehabilitation Technological solutions were a part of home health services which were delivered by the company. As a part of cloud based HIPAA compliant network, the telemedicine unit consists of a portal to track health metrics and rehabilitation treatment plan and progress by the PT specialists as well as the Case Managers. The system included case briefing, consultation by specialists as well as providing physiotherapy sessions both by Home health therapists or via health workers such as PTAs within the vicinity of home environment at patient’s ease as schematically represented in Fig. no.1.

Figure 1: Set-up for in-home telerehabilitation: (A) Framework system; (B) dashboard Screen (C) Integrated loop with benefits

The participants were given a pre and post session modified TUQ and asked to reflect on their entire rehabilitation experience using the Telerehabilitation platform so as to get relevant information about telemedicine services including key events such as finding out they would receive services at home by videoconference, having the internet and videoconferencing equipment installed at home and receiving services by videoconference including dealing with technical issues. Following the same detailed interview was taken using the TAMS so as to identify key ideas which can affect usage of telerehabilitation. . Statistical tests was conducted  using SPSS for Pre-post differences evaluation. using paired  t-tests to assess factors associated with awareness, knowledge and perception. Significance was set a priori at p < 0.05. […]

Continue —> Preliminary Analysis of Perception, Knowledge and Attitude of Home Health Patients Using Tele Rehabilitation in Riyadh, Saudi Arabia

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[ARTICLE] Stroke patients’ and non-professional coaches’ experiences with home-based constraint-induced movement therapy: a qualitative study – Full Text

To investigate the experiences of chronic stroke patients and non-professional coaches with home-based constraint-induced movement therapy (homeCIMT).

Qualitative study embedded within a cluster randomized controlled trial investigating the efficacy of homeCIMT to improve the use of the affected arm in daily activities.

Patients’ home environment.

13 stroke patients and 9 non-professional coaches’ alias family members who had completed the four-week homeCIMT programme in the context of the HOMECIMT trial.

Semi-structured interviews; qualitative data were analysed using the methodology of the hermeneutic phenomenological data analysis.

We identified six themes in the qualitative analysis describing the experiences of patients and non-professional coaches with homeCIMT: (1) homeCIMT can be integrated into everyday life with varying degrees of success; (2) training together may produce positive experiences as well as strain; (3) self-perceived improvements during and following homeCIMT; (4) using the affected arm in everyday life is challenging; (5) subjective evaluation of and experiences with homeCIMT-specific exercises; and (6) impact of professional therapists’ guidance and motivation during homeCIMT. Statements regarding theme five and six were only provided by patients, whereas the other themes contain both, the experiences of stroke patients and non-professional coaches.

Patients’ and non-professional coaches’ narratives offer a detailed insight into the manifold experiences with the practical implementation of homeCIMT that may help improve implementing the homeCIMT programme and similar approaches involving increased training duration and intensity and/or involvement of family members.

 

In stroke rehabilitation, repetitive, task-specific training is one of the key principles.1,2 For stroke patients with upper limb dysfunction, constraint-induced movement therapy and its modifications are one of the most promising techniques taking this principle into account.14 To induce the use of the affected arm in everyday life,5 constraint-induced movement therapy comprises an intensive motor training, the use of adherence-enhancing behavioural methods and the immobilization of the non-affected hand.5,6 A four-week home-based training in conjunction with the support of a non-professional coach (e.g. family member) and reduced professional assistance to meet ambulatory care conditions (home-based constraint-induced movement therapy (homeCIMT)) is one way to deliver constraint-induced movement therapy to patients in long-term care.7 The HOMECIMT trial showed homeCIMT to be superior to conventional therapies with regard to the self-perceived use of the stroke-affected arm in daily activities.8

HomeCIMT and other forms of constraint-induced movement therapy have been shown to be particularly effective in improving upper limb function post stroke.1,3 However, these interventions will only work if patients adhere to them. Constraint-induced movement therapy requires numerous hours of repetitive exercises, which are likely to present a challenge for patients.9,10 Regarding homeCIMT, the involvement of a non-professional coach might be an additional challenging aspect for both, patients and non-professional coaches. Thus, it is vital to better understand the users’ experiences with different forms of constraint-induced movement therapies in order to adapt the way how we deliver these interventions and maximize adherence to them. However, there are only few investigations with the users’ perspectives on constraint-induced movement therapies. We are only aware of three minor qualitative studies investigating the experiences of two or three patients with modified constraint-induced movement therapies.1113 A qualitative research approach, in particular, provides information about the users’ experiences with the practical application of a therapy.14,15

In addition to the cluster randomized controlled HOMECIMT trial, we conducted a comprehensive qualitative study to explore the users’ perspectives on homeCIMT following the driving question: What are the experiences of chronic stroke patients and non-professional coaches with homeCIMT?[…]

 

Continue —> Stroke patients’ and non-professional coaches’ experiences with home-based constraint-induced movement therapy: a qualitative study – Anne Stark, Christine Färber, Britta Tetzlaff, Martin Scherer, Anne Barzel, 2019

Figure 1. Themes regarding the experiences of patients and non-professional coaches with homeCIMT.

 

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[WEB SITE] HOMEREHAB – Development of Robotic Technology for Post-Stroke Home Tele-Rehabilitation – The European Coordination Hub for Open Robotics Development

homerehab1

Rehabilitation can help hemiparetic patients to learn new ways of using and moving their weak arms and legs. With immediate therapy it is also possible that people who suffer from hemiparesis may eventually regain movement. However, reductions in healthcare reimbursement place constant demands on rehabilitation specialists to reduce the cost of care and improve productivity. Service providers have responded by shortening the length of patient hospitalisation.

The HOMEREHAB project will develop a new tele-rehabilitation robotic system for delivering therapy to stroke patients at home. It will research on the complex trade-off between robotic design requirements for in home systems and the performance required for optimal rehabilitation therapies, which current commercial systems designed for laboratories and hospitals do not take into account. Additionally, the new home scenario also demands for the smart monitoring of the patient’s physiological state, and the adaptation of the rehabilitation therapy for an optimal service.

 

Contact:

Universidad Miguel Hernández de Elche (UMH)
Nicolas M. Garcia-Aracil
Email: Nicolas.garcia@umh.es
Internet: www.umh.es

 

CEIT – Centro de Estudios e Investigaciones Técnicas
Iñaki Díaz
Email: idiaz@ceit.es
Internet: www.ceit.es

 

Instead Technologies
Alejandro García Moll
Email: Alejandro.garciam@gouhm.umh.es
Internet: www.gouhm.uhm.es

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via HOMEREHAB – Development of Robotic Technology for Post-Stroke Home Tele-Rehabilitation – The European Coordination Hub for Open Robotics Development

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[ARTICLE] An Upper Extremity Rehabilitation System Using Efficient Vision-Based Action Identification Techniques – Full Text PDF

Abstract

This study proposes an action identification system for home upper extremity rehabilitation.
In the proposed system, we apply an RGB-depth (color-depth) sensor to capture the image sequences of the patient’s upper extremity actions to identify its movements. We apply a skin color detection technique to assist with extremity identification and to build up the upper extremity skeleton points.
We use the dynamic time warping algorithm to determine the rehabilitation actions. The system presented herein builds up upper extremity skeleton points rapidly. Through the upper extremity of the human skeleton and human skin color information, the upper extremity skeleton points are effectively established by the proposed system, and the rehabilitation actions of patients are identified by a dynamic time warping algorithm. Thus, the proposed system can achieve a high recognition rate of 98% for the defined rehabilitation actions for the various muscles.
Moreover, the computational speed of the proposed system can reach 125 frames per second—the processing time per frame is less than 8 ms on a personal computer platform. This computational efficiency allows efficient extensibility for future developments to deal with complex ambient environments and for implementation in embedded and pervasive systems.
The major contributions of the study are:
  1. The proposed system is not only a physical exercise game, but also a movement training program for specific muscle groups;
  2. The hardware of upper extremity rehabilitation system included a personal computer with personal computer and a depth camera. These are economic equipment, so that patients who need this system can set up one set at home;
  3. patients can perform rehabilitation actions in sitting position to prevent him/her from falling down during training;
  4. The accuracy rate of identifying rehabilitation action is as high as 98%, which is sufficient for distinguishing between correct and wrong action when performing specific action trainings;
  5. The proposed upper extremity rehabilitation system is real-time, efficient to vision-based action identification, and low-cost hardware and software, which is affordable for most families.

via “An Upper Extremity Rehabilitation System Using Efficient Vision-Based Action Identification Techniques” by Yen-Lin Chen

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