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Wearable Sensors for Stroke Rehabilitation

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Neurorehabilitation Technology

Abstract

In this chapter, we provide a review of the current applications of wearable sensors in the field of stroke rehabilitation. Four key points are discussed in this review. First, wearable sensors are a viable solution for monitoring movement during rehabilitation exercises and clinical assessments, but more work needs to be done to derive clinically relevant information from sensor data collected during unstructured activities. Second, wearable technologies provide critical information related to the performance of activities in daily life, information that is not necessarily captured during in-clinic assessments. Third, wearable technologies can provide feedback and motivation to increase movement in the home and community settings. Finally, technologies are rapidly emerging that can complement “traditional” wearable sensors and sometimes replace them as they provide less obtrusive means of monitoring motor function in stroke survivors. These developing technologies, as well as readily available wearable sensors, are transforming stroke rehabilitation, their development is progressing at a fast pace, and their use so far has allowed us to gather important information, that we would have not been able to collect otherwise, which has tremendous potential to further advance stroke rehabilitation.

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Abbreviations

10 MWT:

10-M walk test

ADL:

Activity of Daily Living

ARAT:

Action Research Arm Test

BBT:

Box and Block Test

Cis:

Confidence intervals

EMG:

Electromyography

FAS:

Functional Ability Scale (subscale of the Wolf Motor Function Test)

FMA-UE:

Fugl-Meyer Assessment, Upper Extremity subsection

ICF:

International Classification of Functioning, Disability and Health

IMU:

Inertial Measurement Unit

IoT:

Internet of Things

LL:

Lower Limb

MAL:

Motor Activity Log

MLA:

Machine Learning Algorithms

MMG:

Mechanomyography

RFID:

Radio Frequency IDentification

RMSE:

Root Mean Square Error

SARAH:

Semi-Automated Rehabilitation at the Home

TIS:

Trunk Impairment Scale

TUG:

Timed Up-and-Go

UL:

Upper Limb

UWB:

Ultra-WideBand

WMFT:

Wolf Motor Function Test

References

  1. Zimbelman JL, Juraschek SP, Zhang X, Lin VW-H. Physical therapy workforce in the United States: Forecasting Nationwide Shortages. PM&R [Internet]. 2010 Nov;2(11):1021–9. http://doi.wiley.com, https://doi.org/10.1016/j.pmrj.2010.06.015.

  2. Langhorne P, Coupar F, Pollock A. Motor recovery after stroke: a systematic review. Lancet Neurol [Internet]. (2009);8(8):741–54. http://www.ncbi.nlm.nih.gov/pubmed/19608100.

  3. Teasell RW, Murie Fernandez M, McIntyre A, Mehta S. Rethinking the continuum of stroke rehabilitation. Arch Phys Med Rehabil [Internet]. 2014;95(4):595–6. http://www.ncbi.nlm.nih.gov/pubmed/24529594.

  4. Winstein C, Varghese R. Been there, done that, so what’s next for arm and hand rehabilitation in stroke? NeuroRehabilitation [Internet]. 2018;43(1):3–18. http://www.ncbi.nlm.nih.gov/pubmed/29991146.

  5. Lang CE, Strube MJ, Bland MD, Waddell KJ, Cherry-Allen KM, Nudo RJ, et al. Dose response of task-specific upper limb training in people at least 6 months poststroke: a phase II, single-blind, randomized, controlled trial. Ann Neurol [Internet]. 2016;80(3):342–54. http://www.ncbi.nlm.nih.gov/pubmed/27447365.

  6. Ward NS, Brander F, Kelly K. Intensive upper limb neurorehabilitation in chronic stroke: outcomes from the Queen Square programme. J Neurol Neurosurg Psychiatry [Internet]. 2019;90(5):498–506. http://www.ncbi.nlm.nih.gov/pubmed/30770457.

  7. Collins FS, Varmus H. A new initiative on precision medicine. N Engl J Med [Internet]. 2015 Feb 26;372(9):793–5. http://www.ncbi.nlm.nih.gov/pubmed/25635347.

  8. Hulsen T, Jamuar SS, Moody AR, Karnes JH, Varga O, Hedensted S, et al. From big data to precision medicine. Front Med [Internet]. 2019;6:34. http://www.ncbi.nlm.nih.gov/pubmed/30881956.

  9. Niederberger E, Parnham MJ, Maas J, Geisslinger G. 4 Ds in health research-working together toward rapid precision medicine. EMBO Mol Med [Internet]. 2019;11(11):e10917. http://www.ncbi.nlm.nih.gov/pubmed/31531943.

  10. Klein TA, Neumann J, Reuter M, Hennig J, von Cramon DY, Ullsperger M. Genetically determined differences in learning from errors. Science [Internet]. 2007 Dec 7;318(5856):1642–5. http://www.ncbi.nlm.nih.gov/pubmed/18063800.

  11. Tran DA, Pajaro-Blazquez M, Daneault J-F, Gallegos JG, Pons J, Fregni F, et al. Combining dopaminergic facilitation with robot-assisted upper limb therapy in stroke survivors: a focused review. Am J Phys Med Rehabil [Internet]. 2016;95(6):459–74. http://www.ncbi.nlm.nih.gov/pubmed/26829074.

  12. Pearson-Fuhrhop KM, Minton B, Acevedo D, Shahbaba B, Cramer SC. Genetic variation in the human brain dopamine system influences motor learning and its modulation by L-Dopa. PLoS One [Internet]. 2013;8(4):e61197. http://www.ncbi.nlm.nih.gov/pubmed/23613810.

  13. Cheung VCK, Turolla A, Agostini M, Silvoni S, Bennis C, Kasi P, et al. Muscle synergy patterns as physiological markers of motor cortical damage. Proc Natl Acad Sci USA [Internet]. 2012 Sep 4;109(36):14652–6. http://www.ncbi.nlm.nih.gov/pubmed/22908288.

  14. Miranda JGV, Daneault J-F, Vergara-Diaz G, Torres ÂFS de OE, Quixadá AP, Fonseca M de L, et al. Complex upper-limb movements are generated by combining motor primitives that scale with the movement size. Sci Rep [Internet]. 2018;8(1):12918. http://www.ncbi.nlm.nih.gov/pubmed/30150687.

  15. Rosenthal O, Wing AM, Wyatt JL, Punt D, Brownless B, Ko-Ko C, et al. Boosting robot-assisted rehabilitation of stroke hemiparesis by individualized selection of upper limb movements—a pilot study. J Neuroeng Rehabil [Internet]. 2019;16(1):42. http://www.ncbi.nlm.nih.gov/pubmed/30894192.

  16. World Health Organization. International classification of functioning, disability and health (ICF). Geneva, Switzerland; 2001

    Google Scholar 

  17. Patel S, Park H, Bonato P, Chan L, Rodgers M. A review of wearable sensors and systems with application in rehabilitation. J Neuroeng Rehabil [Internet]. 2012 Apr 20;9:21. http://www.ncbi.nlm.nih.gov/pubmed/22520559.

  18. Lee SI, Adans-Dester CP, Grimaldi M, Dowling A V., Horak PC, Black-Schaffer RM, et al. Enabling Stroke Rehabilitation in home and community settings: a wearable sensor-based approach for upper-limb motor training. IEEE J Transl Eng Heal Med [Internet]. 2018;6:1–11. https://ieeexplore.ieee.org/document/8353413/.

  19. Maceira-Elvira P, Popa T, Schmid A-C, Hummel FC. Wearable technology in stroke rehabilitation: towards improved diagnosis and treatment of upper-limb motor impairment. J Neuroeng Rehabil [Internet]. 2019;16(1):142. http://www.ncbi.nlm.nih.gov/pubmed/31744553.

  20. Dobkin BH, Dorsch A. The promise of mHealth: daily activity monitoring and outcome assessments by wearable sensors. Neurorehabil Neural Repair [Internet]. 25(9):788–98. http://www.ncbi.nlm.nih.gov/pubmed/21989632.

  21. Bonato P. Advances in wearable technology and applications in physical medicine and rehabilitation. J Neuroeng Rehabil [Internet]. 2005;2(1):2. http://www.ncbi.nlm.nih.gov/pubmed/15733322.

  22. Kwakkel G, Lannin NA, Borschmann K, English C, Ali M, Churilov L, et al. Standardized measurement of sensorimotor recovery in stroke trials: consensus-based core recommendations from the stroke recovery and rehabilitation roundtable. Int J Stroke [Internet]. 2017;12(5):451–61. http://journals.sagepub.com/doi, https://doi.org/10.1177/1747493017711813.

  23. Schwarz A, Bhagubai MMC, Wolterink G, Held JPO, Luft AR, Veltink PH. Assessment of upper limb movement impairments after stroke using wearable inertial sensing. sensors [Internet]. 2020;20(17):4770. https://www.mdpi.com/1424-8220/20/17/4770.

  24. Kortier HG, Sluiter VI, Roetenberg D, Veltink PH. Assessment of hand kinematics using inertial and magnetic sensors. J Neuroeng Rehabil [Internet]. 2014;11(1):70. http://jneuroengrehab.biomedcentral.com/articles, https://doi.org/10.1186/1743-0003-11-70.

  25. Yao S, Vargas L, Hu X, Zhu Y. A Novel Finger Kinematic Tracking Method Based on Skin-Like Wearable Strain Sensors. IEEE Sens J [Internet]. 2018 Apr 1;18(7):3010–5. http://ieeexplore.ieee.org/document/8281089/.

  26. Nie JZ, Nie JW, Hung N-T, Cotton RJ, Slutzky MW. Portable, open-source solutions for estimating wrist position during reaching in people with stroke. Sci Rep [Internet]. 2021 Dec 18;11(1):22491. https://www.nature.com/articles/s41598-021-01805-2.

  27. Kim GJ, Parnandi A, Eva S, Schambra H. The use of wearable sensors to assess and treat the upper extremity after stroke: a scoping review. Disabil Rehabil [Internet]. 2021;1–20. http://www.ncbi.nlm.nih.gov/pubmed/34328803.

  28. Fugl-Meyer AR, Jääskö L, Leyman I, Olsson S, Steglind S. The post-stroke hemiplegic patient. 1. a method for evaluation of physical performance. Scand J Rehabil Med [Internet]. 1975;7(1):13–31. http://www.ncbi.nlm.nih.gov/pubmed/1135616.

  29. Yu L, Xiong D, Guo L, Wang J. A remote quantitative Fugl-Meyer assessment framework for stroke patients based on wearable sensor networks. Comput Methods Programs Biomed [Internet]. 2016;128:100–10. https://linkinghub.elsevier.com/retrieve/pii/S0169260715301905.

  30. Del Din S, Patel S, Cobelli C, Bonato P. Estimating Fugl-Meyer clinical scores in stroke survivors using wearable sensors. In: 2011 Annual international conference of the IEEE engineering in medicine and biology society [Internet]. IEEE; 2011. p. 5839–42. http://ieeexplore.ieee.org/document/ss6091444/.

  31. Formstone L, Huo W, Wilson S, McGregor A, Bentley P, Vaidyanathan R. Quantification of motor function post-stroke using novel combination of wearable inertial and mechanomyographic sensors. IEEE Trans Neural Syst Rehabil Eng [Internet]. 2021;29:1158–67. https://ieeexplore.ieee.org/document/9455409/.

  32. Wolf SL, Catlin PA, Ellis M, Archer AL, Morgan B, Piacentino A. Assessing Wolf motor function test as outcome measure for research in patients after stroke. Stroke. 2001;32(7):1635–9.

    Article  CAS  PubMed  Google Scholar 

  33. Patel S, Hughes R, Hester T, Stein J, Akay M, Dy JG, et al. A novel approach to monitor rehabilitation outcomes in stroke survivors using wearable technology. Proc IEEE [Internet]. 2010;98(3):450–61. http://ieeexplore.ieee.org/document/5420034/.

  34. Chen H-M, Chen CC, Hsueh I-P, Huang S-L, Hsieh C-L. Test-retest reproducibility and smallest real difference of 5 hand function tests in patients with stroke. Neurorehabil Neural Repair [Internet]. 2009;23(5):435–40. http://www.ncbi.nlm.nih.gov/pubmed/19261767.

  35. Friedman N, Chan V, Zondervan D, Bachman M, Reinkensmeyer DJ. MusicGlove: motivating and quantifying hand movement rehabilitation by using functional grips to play music. In: 2011 annual international conference of the IEEE engineering in medicine and biology society [Internet]. IEEE; 2011. p. 2359–63. http://ieeexplore.ieee.org/document/6090659/.

  36. Repnik E, Puh U, Goljar N, Munih M, Mihelj M. Using inertial measurement units and electromyography to quantify movement during action research arm test execution. Sensors [Internet]. 2018;18(9):2767. http://www.mdpi.com/1424-8220/18/9/2767.

  37. Bochniewicz EM, Emmer G, McLeod A, Barth J, Dromerick AW, Lum P. Measuring functional arm movement after stroke using a single wrist-worn sensor and machine learning. J Stroke Cerebrovasc Dis [Internet]. 2017;26(12):2880–7. http://www.ncbi.nlm.nih.gov/pubmed/28781056.

  38. Adans-Dester C, Hankov N, O’Brien A, Vergara-Diaz G, Black-Schaffer R, Zafonte R, et al. Enabling precision rehabilitation interventions using wearable sensors and machine learning to track motor recovery. NPJ Digit Med [Internet]. 2020;3(1):121. https://www.nature.com/articles/s41746-020-00328-w.

  39. Hocoma. ArmeoSenso [Internet]. https://www.hocoma.com/solutions/armeo-senso/.

  40. Wittmann F, Held JP, Lambercy O, Starkey ML, Curt A, Höver R, et al. Self-directed arm therapy at home after stroke with a sensor-based virtual reality training system. J Neuroeng Rehabil [Internet]. 2016;13(1):75. http://jneuroengrehab.biomedcentral.com/articles, https://doi.org/10.1186/s12984-016-0182-1.

  41. Widmer M, Held JPO, Wittmann F, Valladares B, Lambercy O, Sturzenegger C, et al. Reward during arm training improves impairment and activity after stroke: a randomized controlled trial. Neurorehabil Neural Repair [Internet]. 2022;36(2):140–50. http://journals.sagepub.com/doi, https://doi.org/10.1177/15459683211062898.

  42. Friedman N, Chan V, Reinkensmeyer AN, Beroukhim A, Zambrano GJ, Bachman M, et al. Retraining and assessing hand movement after stroke using the MusicGlove: comparison with conventional hand therapy and isometric grip training. J Neuroeng Rehabil [Internet]. 2014;11(1):76. http://jneuroengrehab.biomedcentral.com/articles, https://doi.org/10.1186/1743-0003-11-76.

  43. Zondervan DK, Friedman N, Chang E, Zhao X, Augsburger R, Reinkensmeyer DJ, 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 Dev [Internet]. 2016;53(4):457–72. http://www.ncbi.nlm.nih.gov/pubmed/27532880.

  44. Arteaga S, Chevalier J, Coile A, Hill AW, Sali S, Sudhakhrisnan S, et al. Low-cost accelerometry-based posture monitoring system for stroke survivors. In: Proceedings of the 10th international ACM SIGACCESS conference on computers and accessibility. 2008. p. 243–4.

    Google Scholar 

  45. Minet LR, Peterson E, von Koch L, Ytterberg C. Occurrence and predictors of falls in people with stroke: six-year prospective study. Stroke [Internet]. 2015;46(9):2688–90. http://www.ncbi.nlm.nih.gov/pubmed/26243230.

  46. Bergamini E, Iosa M, Belluscio V, Morone G, Tramontano M, Vannozzi G. Multi-sensor assessment of dynamic balance during gait in patients with subacute stroke. J Biomech [Internet]. 2017;61:208–15. http://www.ncbi.nlm.nih.gov/pubmed/28823468.

  47. Garcia F do V, da Cunha MJ, Schuch CP, Schifino GP, Balbinot G, Pagnussat AS. Movement smoothness in chronic post-stroke individuals walking in an outdoor environment—a cross-sectional study using IMU sensors. Najafi B, editor. PLoS One [Internet]. 2021;16(4):e0250100. https://dx.plos.org, https://doi.org/10.1371/journal.pone.0250100.

  48. Salarian A, Horak FB, Zampieri C, Carlson-Kuhta P, Nutt JG, Aminian K. iTUG, a sensitive and reliable measure of mobility. IEEE Trans Neural Syst Rehabil Eng [Internet]. 2010;18(3):303–10. http://www.ncbi.nlm.nih.gov/pubmed/20388604.

  49. Wüest S, Massé F, Aminian K, Gonzenbach R, de Bruin ED. Reliability and validity of the inertial sensor-based Timed “Up and Go” test in individuals affected by stroke. J Rehabil Res Dev [Internet]. 2016;53(5):599–610. http://www.ncbi.nlm.nih.gov/pubmed/27898161.

  50. Verheyden G, Nieuwboer A, Mertin J, Preger R, Kiekens C, De Weerdt W. The trunk impairment Scale: a new tool to measure motor impairment of the trunk after stroke. Clin Rehabil [Internet]. 2004;18(3):326–34. http://journals.sagepub.com/doi, https://doi.org/10.1191/0269215504cr733oa.

  51. Alhwoaimel N, Warner M, Hughes A-M, Ferrari F, Burridge J, Wee SK, et al. Concurrent validity of a novel wireless inertial measurement system for assessing trunk impairment in people with stroke. Sensors (Basel) [Internet]. 2020;20(6). Available from: http://www.ncbi.nlm.nih.gov/pubmed/32197493.

  52. Hocoma. Valedo [Internet]. http://www.valedotherapy.com/.

  53. Bauer CM, Rast FM, Ernst MJ, Kool J, Oetiker S, Rissanen SM, et al. Concurrent validity and reliability of a novel wireless inertial measurement system to assess trunk movement. J Electromyogr Kinesiol [Internet]. 2015;25(5):782–90. https://linkinghub.elsevier.com/retrieve/pii/S1050641115001157.

  54. Dorsch AK, Thomas S, Xu X, Kaiser W, Dobkin BH, Emara T, et al. SIRRACT. Neurorehabil neural repair [Internet]. 2015;29(5):407–15. http://journals.sagepub.com/doi, https://doi.org/10.1177/1545968314550369.

  55. Byl N, Zhang W, Coo S, Tomizuka M. Clinical impact of gait training enhanced with visual kinematic biofeedback: patients with Parkinson’s disease and patients stable post stroke. Neuropsychologia [Internet]. 2015;79(Pt B):332–43. http://www.ncbi.nlm.nih.gov/pubmed/25912760.

  56. Waddell KJ, Birkenmeier RL, Bland MD, Lang CE. An exploratory analysis of the self-reported goals of individuals with chronic upper-extremity paresis following stroke. Disabil Rehabil [Internet]. 2016;38(9):853–7. http://www.tandfonline.com/doi/full, https://doi.org/10.3109/09638288.2015.1062926.

  57. Rand D, Eng JJ. Disparity between functional recovery and daily use of the upper and lower extremities during subacute stroke rehabilitation. Neurorehabil Neural Repair [Internet]. 2012;26(1):76–84. http://journals.sagepub.com/doi, https://doi.org/10.1177/1545968311408918.

  58. Doman CA, Waddell KJ, Bailey RR, Moore JL, Lang CE. Changes in upper-extremity functional capacity and daily performance during outpatient occupational therapy for people with stroke. Am J Occup Ther [Internet]. 2016;70(3):7003290040p1–11. https://research.aota.org/ajot/article/70/3/7003290040p1/6159/Changes-in-Upper-Extremity-Functional-Capacity-and.

  59. Ardestani MM, Henderson CE, Hornby TG. Improved walking function in laboratory does not guarantee increased community walking in stroke survivors: Potential role of gait biomechanics. J Biomech [Internet]. 2019;91:151–9. https://linkinghub.elsevier.com/retrieve/pii/S0021929019303434.

  60. Waddell KJ, Strube MJ, Bailey RR, Klaesner JW, Birkenmeier RL, Dromerick AW, et al. Does task-specific training improve upper limb performance in daily life poststroke? Neurorehabil Neural Repair [Internet]. 2017;31(3):290–300. http://journals.sagepub.com/doi, https://doi.org/10.1177/1545968316680493.

  61. Taub E. The behavior-analytic origins of constraint-induced movement therapy: an example of behavioral neurorehabilitation. Behav Anal [Internet]. 2012;35(2):155–78. http://www.ncbi.nlm.nih.gov/pubmed/23449867.

  62. Hirsch T, Barthel M, Aarts P, Chen Y-A, Freivogel S, Johnson MJ, et al. A first step toward the operationalization of the learned non-use phenomenon: a Delphi study. neurorehabil neural repair [Internet]. 2021;35(5):383–92. http://journals.sagepub.com/doi, https://doi.org/10.1177/1545968321999064.

  63. Bailey RR, Lang CE. Upper-limb activity in adults: Referent values using accelerometry. J Rehabil Res Dev [Internet]. 2013;50(9):1213–22. http://www.rehab.research.va.gov/jour/2013/509/pdf/JRRD-2012-12-0222.pdf.

  64. Lemmens RJ, Timmermans AA, Janssen-Potten YJ, Smeets RJ, Seelen HA. Valid and reliable instruments for arm-hand assessment at ICF activity level in persons with hemiplegia: a systematic review. BMC Neurol [Internet]. 2012;12(1):21. http://bmcneurol.biomedcentral.com/articles, https://doi.org/10.1186/1471-2377-12-21.

  65. Uswatte G, Miltner WH, Foo B, Varma M, Moran S, Taub E. Objective measurement of functional upper-extremity movement using accelerometer recordings transformed with a threshold filter. Stroke [Internet]. 2000;31(3):662–7. http://www.ncbi.nlm.nih.gov/pubmed/10700501.

  66. Urbin MA, Waddell KJ, Lang CE. Acceleration metrics are responsive to change in upper extremity function of stroke survivors. Arch Phys Med Rehabil [Internet]. 2015;96(5):854–61. https://linkinghub.elsevier.com/retrieve/pii/S0003999314012829.

  67. Urbin MA, Bailey RR, Lang CE. Validity of Body-Worn Sensor Acceleration Metrics to Index Upper Extremity Function in Hemiparetic Stroke. J Neurol Phys Ther [Internet]. 2015;39(2):111–8. https://journals.lww.com/01253086-201504000-00006.

  68. Lang CE, Barth J, Holleran CL, Konrad JD, Bland MD. Implementation of wearable sensing technology for movement: pushing forward into the routine physical rehabilitation care field. Sensors (Basel) [Internet]. 2020;20(20). http://www.ncbi.nlm.nih.gov/pubmed/33050368.

  69. Lang CE, Waddell KJ, Klaesner JW, Bland MD. A method for quantifying upper limb performance in daily life using accelerometers. J Vis Exp [Internet]. 2017;(122). https://www.jove.com/video/55673/a-method-for-quantifying-upper-limb-performance-daily-life-using.

  70. Miller LC, Dewald JPA. Involuntary paretic wrist/finger flexion forces and EMG increase with shoulder abduction load in individuals with chronic stroke. Clin Neurophysiol [Internet]. 2012;123(6):1216–25. https://linkinghub.elsevier.com/retrieve/pii/S1388245712000466.

  71. Lee SI, Ozsecen MY, Della Toffola L, Daneault J-F, Puiatti A, Patel S, et al. Activity detection in uncontrolled free-living conditions using a single accelerometer. In: 2015 IEEE 12th international conference on wearable and implantable body sensor networks (BSN) [Internet]. IEEE; 2015. p. 1–6. http://ieeexplore.ieee.org/document/7299372/.

  72. Hoyt CR, Van AN, Ortega M, Koller JM, Everett EA, Nguyen AL, et al. Detection of pediatric upper extremity motor activity and deficits with accelerometry. JAMA Netw Open [Internet]. 2019;2(4):e192970. http://jamanetworkopen.jamanetwork.com/article.aspx?doi, https://doi.org/10.1001/jamanetworkopen.2019.2970.

  73. Smith BA, Lang CE. Sensor measures of symmetry quantify upper limb movement in the natural environment across the lifespan. Arch Phys Med Rehabil [Internet]. 2019;100(6):1176–83. https://linkinghub.elsevier.com/retrieve/pii/S0003999319300760.

  74. Parnandi A, Uddin J, Nilsen DM, Schambra HM. The pragmatic classification of upper extremity motion in neurological patients: a primer. Front Neurol [Internet]. 2019; 18:10. https://www.frontiersin.org/article, https://doi.org/10.3389/fneur.2019.00996/full.

  75. Goldsack JC, Coravos A, Bakker JP, Bent B, Dowling AV., Fitzer-Attas C, et al. Verification, analytical validation, and clinical validation (V3): the foundation of determining fit-for-purpose for biometric monitoring technologies (BioMeTs). NPJ Digit Med [Internet]. 2020;3(1):55. http://www.nature.com/articles/s41746-020-0260-4.

  76. Balasubramanian S, Melendez-Calderon A, Roby-Brami A, Burdet E. On the analysis of movement smoothness. J Neuroeng Rehabil [Internet]. 2015;12(1):112. http://www.jneuroengrehab.com/content/12/1/112.

  77. Balasubramanian S, Melendez-Calderon A, Burdet E. A robust and sensitive metric for quantifying movement smoothness. IEEE Trans Biomed Eng [Internet]. 2012;59(8):2126–36. http://ieeexplore.ieee.org/document/6104119/.

  78. David A, Subash T, Varadhan SKM, Melendez-Calderon A, Balasubramanian S. A framework for sensor-based assessment of upper-limb functioning in hemiparesis. Front Hum Neurosci [Internet]. 2021;15. https://www.frontiersin.org/articles, https://doi.org/10.3389/fnhum.2021.667509/full.

  79. Lang CE, Waddell KJ, Barth J, Holleran CL, Strube MJ, Bland MD. Upper limb performance in daily life approaches plateau around three to six weeks post-stroke. Neurorehabil Neural Repair [Internet]. 2021;35(10):903–14. http://journals.sagepub.com/doi, https://doi.org/10.1177/15459683211041302.

  80. Barth J, Lohse KR, Konrad JD, Bland MD, Lang CE. Sensor-based categorization of upper limb performance in daily life of persons with and without neurological upper limb deficits. Front Rehabil Sci [Internet]. 2021;2. https://www.frontiersin.org/articles, https://doi.org/10.3389/fresc.2021.741393/full.

  81. Harris JE, Eng JJ. Goal priorities identified through client-centred measurement in individuals with chronic stroke. Physiother Canada [Internet]. 2004;56(03):171. https://journals.bcdecker.com/CrossRef/showText.aspx?path=PTC/volume56%2C 2004/issue 03%2CJune/ptc_2004_00017/ptc_2004_00017.xml.

  82. Bohannon RW, Andrews AW, Smith MB. Rehabilitation goals of patients with hemiplegia. Int J Rehabil Res. 1988;11(2):181–4.

    Article  Google Scholar 

  83. Macko RF, Haeuber E, Shaughnessy M, Coleman KL, Boone DA, Smith GV, et al. Microprocessor-based ambulatory activity monitoring in stroke patients. Med Sci Sport Exerc [Internet]. 2002;34(3):394–9. http://journals.lww.com/00005768-200203000-00002.

  84. Fulk GD, Combs SA, Danks KA, Nirider CD, Raja B, Reisman DS. Accuracy of 2 activity monitors in detecting steps in people with stroke and traumatic brain injury. Phys Ther [Internet]. 2014;94(2):222–9. https://academic.oup.com/ptj/article/94/2/222/2735422.

  85. Evenson KR, Goto MM, Furberg RD. Systematic review of the validity and reliability of consumer-wearable activity trackers. Int J Behav Nutr Phys Act [Internet]. 2015;12(1):159. http://www.ijbnpa.org/content/12/1/159

  86. Larsen RT, Korfitsen CB, Juhl CB, Andersen HB, Langberg H, Christensen J. Criterion validity for step counting in four consumer-grade physical activity monitors among older adults with and without rollators. Eur Rev Aging Phys Act [Internet]. 2020;17(1):1. https://eurapa.biomedcentral.com/articles/https://doi.org/10.1186/s11556-019-0235-0.

  87. Rozanski GM, Aqui A, Sivakumaran S, Mansfield A. Consumer wearable devices for activity monitoring among individuals after a stroke: a prospective comparison. JMIR Cardio [Internet]. 2018;2(1):e1. http://cardio.jmir.org/2018/1/e1/.

  88. Danks KA, Pohlig RT, Roos M, Wright TR, Reisman DS. Relationship between walking capacity, biopsychosocial factors, self-efficacy, and walking activity in persons poststroke. J Neurol Phys Ther [Internet]. 2016;40(4):232–8. https://journals.lww.com/01253086-201610000-00004.

  89. Holleran CL, Bland MD, Reisman DS, Ellis TD, Earhart GM, Lang CE. Day-to-day variability of walking performance measures in individuals poststroke and individuals with parkinson disease. J Neurol Phys Ther [Internet]. 2020;44(4):241–7. https://journals.lww.com, https://doi.org/10.1097/NPT.0000000000000327.

  90. Barak S, Wu SS, Dai Y, Duncan PW, Behrman AL. Adherence to accelerometry measurement of community ambulation poststroke. Phys Ther [Internet]. 2014;94(1):101–10. https://academic.oup.com/ptj/article/94/1/101/2735448.

  91. Lang CE, Wagner JM, Edwards DF, Dromerick AW. Upper extremity use in people with hemiparesis in the first few weeks after stroke. J Neurol Phys Ther [Internet]. 2007;31(2):56–63. http://www.ncbi.nlm.nih.gov/pubmed/17558358.

  92. Bailey RR, Birkenmeier RL, Lang CE. Real-world affected upper limb activity in chronic stroke: an examination of potential modifying factors. Top Stroke Rehabil [Internet]. 2015;22(1):26–33. http://www.tandfonline.com/doi/full, https://doi.org/10.1179/1074935714Z.0000000040.

  93. Duncan PW, Lai SM, Keighley J. Defining post-stroke recovery: implications for design and interpretation of drug trials. Neuropharmacology [Internet]. 2000;39(5):835–41. http://www.ncbi.nlm.nih.gov/pubmed/10699448.

  94. Kwakkel G, Kollen B, Lindeman E. Understanding the pattern of functional recovery after stroke: facts and theories. Restor Neurol Neurosci [Internet]. 2004;22(3–5):281–99. http://www.ncbi.nlm.nih.gov/pubmed/15502272.

  95. Ramsey LE, Siegel JS, Lang CE, Strube M, Shulman GL, Corbetta M. Behavioural clusters and predictors of performance during recovery from stroke. Nat Hum Behav [Internet]. 2017;1(3):0038. http://www.nature.com/articles/s41562-016-0038.

  96. Cortes JC, Goldsmith J, Harran MD, Xu J, Kim N, Schambra HM, et al. A short and distinct time window for recovery of arm motor control early after stroke revealed with a global measure of trajectory kinematics. Neurorehabil Neural Repair [Internet]. 2017;31(6):552–60. http://journals.sagepub.com/doi, https://doi.org/10.1177/1545968317697034.

  97. Jørgensen HS, Nakayama H, Raaschou HO, Vive-Larsen J, Støier M, Olsen TS. Outcome and time course of recovery in stroke. Part II: time course of recovery. The Copenhagen stroke study. Arch Phys Med Rehabil [Internet]. 1995;76(5):406–12. http://www.ncbi.nlm.nih.gov/pubmed/7741609.

  98. Vliet R, Selles RW, Andrinopoulou E, Nijland R, Ribbers GM, Frens MA, et al. Predicting upper limb motor impairment recovery after stroke: a mixture model. Ann Neurol [Internet]. 2020;87(3):383–93. https://onlinelibrary.wiley.com/doi, https://doi.org/10.1002/ana.25679.

  99. Yozbatiran N, Der-Yeghiaian L, Cramer SC. A Standardized approach to performing the action research arm test. Neurorehabil Neural Repair [Internet]. 2008;22(1):78–90. http://journals.sagepub.com/doi, https://doi.org/10.1177/1545968307305353.

  100. Dobkin BH. Behavioral self-management strategies for practice and exercise should be included in neurologic rehabilitation trials and care. Curr Opin Neurol [Internet]. 2016;29(6):693–9. https://journals.lww.com/00019052-201612000-00005.

  101. Glasgow RE, Goldstein MG, Ockene JK, Pronk NP. Translating what we have learned into practice. Am J Prev Med [Internet]. 2004;27(2):88–101. https://linkinghub.elsevier.com/retrieve/pii/S0749379704000996.

  102. Schweighofer N, Han CE, Wolf SL, Arbib MA, Winstein CJ. A functional threshold for long-term use of hand and arm function can be determined: predictions from a computational model and supporting data from the extremity constraint-induced therapy evaluation (excite) trial. Phys Ther. 2009;89(12):1327–36.

    Article  PubMed  PubMed Central  Google Scholar 

  103. Schwerz de Lucena D, Rowe J, Chan V, Reinkensmeyer D. Magnetically counting hand movements: validation of a calibration-free algorithm and application to testing the threshold hypothesis of real-world hand use after stroke. Sensors [Internet]. 2021;21(4):1502. https://www.mdpi.com/1424-8220/21/4/1502.

  104. Bravata DM, Smith-Spangler C, Sundaram V, Gienger AL, Lin N, Lewis R, et al. Using pedometers to increase physical activity and improve health: a systematic review. JAMA. 2007;298(19):2296–304.

    Article  CAS  PubMed  Google Scholar 

  105. Forbes. Fitness Tracker Market Size, Share & COVID-19 Impact Analysis [Internet]. 2021. https://www.fortunebusinessinsights.com/fitness-tracker-market-103358.

  106. Locke EA, Latham GP. Building a practically useful theory of goal setting and task motivation: a 35-year odyssey. Am Psychol. 2002;57(9):705.

    Article  PubMed  Google Scholar 

  107. Dobkin BH, Plummer-D’Amato P, Elashoff R, Lee J. International randomized clinical trial, stroke inpatient rehabilitation with reinforcement of walking speed (SIRROWS), improves outcomes. Neurorehabil Neural Repair [Internet]. 2010;24(3):235–42. http://journals.sagepub.com/doi, https://doi.org/10.1177/1545968309357558.

  108. Lang CE, MacDonald JR, Reisman DS, Boyd L, Kimberley TJ, Schindler-Ivens SM, et al. Observation of amounts of movement practice provided during stroke rehabilitation. Arch Phys Med Rehabil. 2009;90(10):1692–8.

    Article  PubMed  PubMed Central  Google Scholar 

  109. Jack K, McLean SM, Moffett JK, Gardiner E. Barriers to treatment adherence in physiotherapy outpatient clinics: a systematic review. Man Ther [Internet]. 201015(3):220–8. https://linkinghub.elsevier.com/retrieve/pii/S1356689X09002094.

  110. Bassett SF. The assessment of patient adherence to physiotherapy rehabilitation. New Zeal J Physiother. 2003;31(2):60–6.

    Google Scholar 

  111. McLean SM, Burton M, Bradley L, Littlewood C. Interventions for enhancing adherence with physiotherapy: A systematic review. Man Ther [Internet]. 2010;15(6):514–21. https://linkinghub.elsevier.com/retrieve/pii/S1356689X10000871.

  112. Whitford M, Schearer E, Rowlett M. Effects of in home high dose accelerometer-based feedback on perceived and actual use in participants chronic post-stroke. Physiother Theory Pract. 2018;00(00):1–11.

    Google Scholar 

  113. de Lucena DS. New technologies for on-demand hand rehabilitation in the living environment after neurologic injury. Irvine: University of California; 2019.

    Google Scholar 

  114. Signal NEJ, McLaren R, Rashid U, Vandal A, King M, Almesfer F, et al. Haptic nudges increase affected upper limb movement during inpatient stroke rehabilitation: multiple-period randomized crossover study. JMIR mHealth uHealth [Internet]. 2020;8(7):e17036. https://mhealth.jmir.org/2020/7/e17036.

  115. Da-Silva RH, van Wijck F, Shaw L, Rodgers H, Balaam M, Brkic L, et al. Prompting arm activity after stroke: A clinical proof of concept study of wrist-worn accelerometers with a vibrating alert function. J Rehabil Assist Technol Eng. 2018;5:2055668318761524.

    PubMed  PubMed Central  Google Scholar 

  116. Da-Silva RH, Moore SA, Rodgers H, Shaw L, Sutcliffe L, van Wijck F, et al. Wristband accelerometers to motiVate arm Exercises after Stroke (WAVES): a pilot randomized controlled trial. Clin Rehabil. 2019;33(8):1391–403.

    Article  PubMed  Google Scholar 

  117. Wei WXJ, Fong KNK, Chung RCK, Cheung HKY, Chow ESL. Remind-to-move for promoting upper extremity recovery using wearable devices in subacute stroke: a multi-center randomized controlled study. IEEE Trans Neural Syst Rehabil Eng [Internet]. 2019;27(1):51–9. https://ieeexplore.ieee.org/document/8540924/.

  118. Chae SH, Kim Y, Lee K-S, Park H-S. Development and Clinical evaluation of a web-based upper limb home rehabilitation system using a smartwatch and machine learning model for chronic stroke survivors: prospective comparative study. JMIR mHealth uHealth [Internet]. 2020;8(7):e17216. http://www.ncbi.nlm.nih.gov/pubmed/32480361.

  119. Lynch EA, Jones TM, Simpson DB, Fini NA, Kuys SS, Borschmann K, et al. Activity monitors for increasing physical activity in adult stroke survivors. Cochrane Database Syst Rev. 2018;(7).

    Google Scholar 

  120. Mandigout S, Chaparro D, Borel B, Kammoun B, Salle J-Y, Compagnat M, et al. Effect of individualized coaching at home on walking capacity in subacute stroke patients: a randomized controlled trial (Ticaa’dom). Ann Phys Rehabil Med. 2021;64(4): 101453.

    Article  PubMed  Google Scholar 

  121. Grau-Pellicer M, Lalanza J, Jovell-Fernández E, Capdevila L. Impact of mHealth technology on adherence to healthy PA after stroke: a randomized study. Top Stroke Rehabil [Internet]. 2020;27(5):354–68. https://www.tandfonline.com/doi/full, https://doi.org/10.1080/10749357.2019.1691816.

  122. Hassett L, van den Berg M, Lindley RI, Crotty M, McCluskey A, van der Ploeg HP, et al. Digitally enabled aged care and neurological rehabilitation to enhance outcomes with activity and MObility UsiNg Technology (AMOUNT) in Australia: a randomised controlled trial. Nguyen C, editor. PLOS Med [Internet]. 2020;17(2):e1003029. https://dx.plos.org, https://doi.org/10.1371/journal.pmed.1003029.

  123. Angelucci A, Cavicchioli M, Cintorrino IA, Lauricella G, Rossi C, Strati S, et al. Smart textiles and sensorized garments for physiological monitoring: a review of available solutions and techniques. Sensors [Internet]. 2021;21(3):814. https://www.mdpi.com/1424-8220/21/3/814.

  124. Gopalsamy C, Park S, Rajamanickam R, Jayaraman S. The wearable motherboardTM: the first generation of adaptive and responsive textile structures (ARTS) for medical applications. Virtual Real. 1999;4(3):152–68.

    Article  Google Scholar 

  125. Park S, Gopalsamy C, Rajamanickam R, Jayaraman S. The wearable motherboard©: a flexible information infrastructure or sensate liner for medical applications. Stud Health Technol Inform. 1999;62:252–8.

    CAS  PubMed  Google Scholar 

  126. Marculescu D, Marculescu R, Zamora NH, Stanley-Marbell P, Khosla PK, Park S, et al. Electronic textiles: a platform for pervasive computing. Proc IEEE [Internet]. 200391(12):1995–2018. http://ieeexplore.ieee.org/document/1246382/.

  127. Scilingo EP, Lorussi F, Mazzoldi A, De Rossi D. Strain-sensing fabrics for wearable kinaesthetic-like systems. IEEE Sens J. 2003;3(4):460–7.

    Article  Google Scholar 

  128. Lorussi F, Rocchia W, Scilingo EP, Tognetti A, De Rossi D. Wearable, redundant fabric-based sensor arrays for reconstruction of body segment posture. IEEE Sens J. 2004;4(6):807–18.

    Article  Google Scholar 

  129. Lorussi F, Scilingo EP, Tesconi M, Tognetti A, De Rossi D. Strain sensing fabric for hand posture and gesture monitoring. IEEE Trans Inf Technol Biomed. 2005;9(3):372–81.

    Article  PubMed  Google Scholar 

  130. Lorussi F, Carbonaro N, De Rossi D, Paradiso R, Veltink P, Tognetti A. Wearable textile platform for assessing stroke patient treatment in daily life conditions. Front Bioeng Biotechnol [Internet]. 2016;4:28. http://www.ncbi.nlm.nih.gov/pubmed/27047939.

  131. McLaren R, Joseph F, Baguley C, Taylor D. A review of e-textiles in neurological rehabilitation: How close are we? J Neuroeng Rehabil [Internet]. 2016;13(1):59. http://www.ncbi.nlm.nih.gov/pubmed/27329186.

  132. Preece SJ, Kenney LPJ, Major MJ, Dias T, Lay E, Fernandes BT. Automatic identification of gait events using an instrumented sock. J Neuroeng Rehabil. 2011;8:32.

    Article  PubMed  PubMed Central  Google Scholar 

  133. Rogers JA, Someya T, Huang Y. Materials and mechanics for stretchable electronics. Science (80-) [Internet]. 2010;327(5973):1603–7. https://www.science.org/doi, https://doi.org/10.1126/science.1182383.

  134. Kim D-H, Lu N, Ma R, Kim Y-S, Kim R-H, Wang S, et al. Epidermal electronics. Science (80- ) [Internet]. 2011;333(6044):838–43. https://www.science.org/doi/, https://doi.org/10.1126/science.1206157.

  135. Kim KK, Ha I, Kim M, Choi J, Won P, Jo S, et al. A deep-learned skin sensor decoding the epicentral human motions. Nat Commun [Internet]. 2020;11(1):2149. http://www.nature.com/articles/s41467-020-16040-y.

  136. Bonnassieux Y, Brabec CJ, Cao Y, Carmichael TB, Chabinyc ML, Cheng K-T, et al. The 2021 flexible and printed electronics roadmap. Flex Print Electron [Internet]. 2021;6(2):023001. https://iopscience.iop.org/article, https://doi.org/10.1088/2058-8585/abf986.

  137. Khan MA, Saibene M, Das R, Brunner I, Puthusserypady S. Emergence of flexible technology in developing advanced systems for post-stroke rehabilitation: a comprehensive review. J Neural Eng [Internet]. 2021;18(6):061003. https://iopscience.iop.org/article, https://doi.org/10.1088/1741-2552/ac36aa.

  138. Doherty AR, Kelly P, Kerr J, Marshall S, Oliver M, Badland H, et al. Using wearable cameras to categorise type and context of accelerometer-identified episodes of physical activity. Int J Behav Nutr Phys Act [Internet]. 2013;10(1):22. http://ijbnpa.biomedcentral.com/articles, https://doi.org/10.1186/1479-5868-10-22.

  139. Yan Yan, Ricci E, Gaowen Liu, Sebe N. Egocentric daily activity recognition via multitask clustering. IEEE Trans Image Process [Internet]. 2015;24(10):2984–95. http://ieeexplore.ieee.org/document/7113851/.

  140. Zariffa J, Popovic MR. Hand contour detection in wearable camera video using an adaptive histogram region of interest. J Neuroeng Rehabil [Internet]. 2013;10(1):114. http://jneuroengrehab.biomedcentral.com/articles, https://doi.org/10.1186/1743-0003-10-114.

  141. Likitlersuang J, Sumitro ER, Cao T, Visée RJ, Kalsi-Ryan S, Zariffa J. Egocentric video: a new tool for capturing hand use of individuals with spinal cord injury at home. J Neuroeng Rehabil [Internet]. 2019;16(1):83. https://jneuroengrehab.biomedcentral.com/articles, https://doi.org/10.1186/s12984-019-0557-1.

  142. Visee RJ, Likitlersuang J, Zariffa J. An effective and efficient method for detecting hands in egocentric videos for rehabilitation applications. IEEE Trans Neural Syst Rehabil Eng [Internet]. 2020;28(3):748–55. https://ieeexplore.ieee.org/document/8967132/.

  143. Dousty M, Zariffa J. Tenodesis grasp detection in egocentric video. IEEE J Biomed Heal Inform [Internet]. 2021;25(5):1463–70. https://ieeexplore.ieee.org/document/9121713/.

  144. Likitlersuang J, Visée RJ, Kalsi-Ryan S, Zariffa J. Capturing hand use of individuals with spinal cord injury at home using egocentric video: a feasibility study. Spinal Cord Ser Cases [Internet]. 2021;7(1):17. http://www.nature.com/articles/s41394-021-00382-w.

  145. Tsai M-F, Wang RH, Zariffa J. Identifying hand use and hand roles after stroke using egocentric video. IEEE J Transl Eng Heal Med [Internet]. 2021;1–1. https://ieeexplore.ieee.org/document/9399477/.

  146. Bandini A, Zariffa J. Analysis of the hands in egocentric vision: a survey. IEEE Trans Pattern Anal Mach Intell [Internet]. 2020;1–1. https://ieeexplore.ieee.org/document/9064606/.

  147. Zhang Y, Sun S, Lei L, Liu H, Xie H. STAC: spatial-temporal attention on compensation information for activity recognition in FPV. Sensors [Internet]. 2021;21(4):1106. https://www.mdpi.com/1424-8220/21/4/1106.

  148. Capra M, Sapienza S, Motto Ros P, Serrani A, Martina M, Puiatti A, et al. Assessing the feasibility of augmenting fall detection systems by relying on UWB-based position tracking and a home robot. Sensors (Basel) [Internet]. 2020;20(18). http://www.ncbi.nlm.nih.gov/pubmed/32962142.

  149. Vahia I V, Kabelac Z, Hsu C-Y, Forester BP, Monette P, May R, et al. Radio signal sensing and signal processing to monitor behavioral symptoms in dementia: a case study. Am J Geriatr Psychiatry [Internet]. 2020;28(8):820–5. http://www.ncbi.nlm.nih.gov/pubmed/32245677.

  150. Ridolfi M, Kaya A, Berkvens R, Weyn M, Joseph W, Poorter E De. Self-calibration and collaborative localization for UWB positioning systems. ACM Comput Surv [Internet]. 2022;54(4):1–27. https://dl.acm.org/doi, https://doi.org/10.1145/3448303.

  151. Landaluce H, Arjona L, Perallos A, Falcone F, Angulo I, Muralter F. A review of IoT sensing applications and challenges using RFID and wireless sensor networks. Sensors [Internet]. 2020;20(9):2495. https://www.mdpi.com/1424-8220/20/9/2495.

  152. Pascacio P, Casteleyn S, Torres-Sospedra J, Lohan ES, Nurmi J. Collaborative indoor positioning systems: a systematic review. Sensors [Internet]. 2021;21(3):1002. https://www.mdpi.com/1424-8220/21/3/1002

  153. Adib F, Kabelac Z, Katabi D, Miller RC. 3d tracking via body radio reflections. In: 11th $\{$USENIX$\}$ Symposium on networked systems design and implementation ($\{$NSDI$\}$ 14). 2014. p. 317–29.

    Google Scholar 

  154. Adib F, Katabi D. See through walls with WiFi! In: Proceedings of the ACM SIGCOMM 2013 conference on SIGCOMM [Internet]. New York, NY, USA: ACM; 2013. p. 75–86. https://dl.acm.org/doi, https://doi.org/10.1145/2486001.2486039.

  155. Fan L, Li T, Yuan Y, Katabi D. In-home daily-life captioning using radio signals. 2020. p. 105–23. https://link.springer.com, https://doi.org/10.1007/978-3-030-58536-5_7.

  156. Toshev A, Szegedy C. DeepPose: human pose estimation via deep neural networks. In: 2014 IEEE conference on computer vision and pattern recognition [Internet]. IEEE; 2014. p. 1653–60. https://ieeexplore.ieee.org/document/6909610.

  157. Insafutdinov E, Pishchulin L, Andres B, Andriluka M, Schiele B. DeeperCut: a deeper, stronger, and faster multi-person pose estimation model. 2016. p. 34–50. http://link.springer.com/https://doi.org/10.1007/978-3-319-46466-4_3.

  158. Cao Z, Hidalgo G, Simon T, Wei S-E, Sheikh Y. OpenPose: realtime multi-person 2D pose estimation using part affinity fields. IEEE Trans Pattern Anal Mach Intell [Internet]. 2021;43(1):172–86. https://ieeexplore.ieee.org/document/8765346/.

  159. Cao Z, Simon T, Wei S-E, Sheikh Y. Realtime multi-person 2D pose estimation using part affinity fields. In: 2017 IEEE conference on computer vision and pattern recognition (CVPR) [Internet]. IEEE; 2017. p. 1302–10. http://ieeexplore.ieee.org/document/8099626/.

  160. Insafutdinov E, Andriluka M, Pishchulin L, Tang S, Levinkov E, Andres B, et al. ArtTrack: articulated multi-person tracking in the wild. In: IEEE conference on computer vision and pattern recognition (CVPR) [Internet]. IEEE; 2017. p. 1293–301. http://ieeexplore.ieee.org/document/8099625/.

  161. Mathis A, Mamidanna P, Cury KM, Abe T, Murthy VN, Mathis MW, et al. DeepLabCut: markerless pose estimation of user-defined body parts with deep learning. Nat Neurosci [Internet]. 2018;21(9):1281–9. http://www.nature.com/articles/s41593-018-0209-y.

  162. Fang H-S, Xie S, Tai Y-W, Lu C. RMPE: regional multi-person pose estimation. 2016; http://arxiv.org/abs/1612.00137.

  163. Lugaresi C, Tang J, Nash H, McClanahan C, Uboweja E, Hays M, et al. MediaPipe: a framework for building perception pipelines. 2019.

    Google Scholar 

  164. Lugaresi C, Tang J, Nash H, McClanahan C, Uboweja E, Hays M, et al. MediaPipe: a framework for perceiving and processing reality. 2019.

    Google Scholar 

  165. Zhang F, Bazarevsky V, Vakunov A, Tkachenka A, Sung G, Chang C-L, et al. MediaPipe hands: on-device real-time hand tracking. 2020. http://arxiv.org/abs/2006.10214.

  166. Seethapathi N, Wang S, Saluja R, Blohm G, Kording KP. Movement science needs different pose tracking algorithms. 2019. http://arxiv.org/abs/1907.10226.

  167. Arac A. Machine learning for 3D kinematic analysis of movements in neurorehabilitation. Curr Neurol Neurosci Rep [Internet]. 2020;20(8):29. http://www.ncbi.nlm.nih.gov/pubmed/32542455.

  168. Cronin NJ. Using deep neural networks for kinematic analysis: challenges and opportunities. J Biomech [Internet]. 2021;123:110460. http://www.ncbi.nlm.nih.gov/pubmed/34029787.

  169. Mehdizadeh S, Nabavi H, Sabo A, Arora T, Iaboni A, Taati B. Concurrent validity of human pose tracking in video for measuring gait parameters in older adults: a preliminary analysis with multiple trackers, viewing angles, and walking directions. J Neuroeng Rehabil [Internet]. 2021;18(1):139. http://www.ncbi.nlm.nih.gov/pubmed/34526074.

  170. Stenum J, Rossi C, Roemmich RT. Two-dimensional video-based analysis of human gait using pose estimation. PLoS Comput Biol [Internet]. 2021;17(4):e1008935. http://www.ncbi.nlm.nih.gov/pubmed/33891585.

  171. Takeda I, Yamada A, Onodera H. Artificial Intelligence-Assisted motion capture for medical applications: a comparative study between markerless and passive marker motion capture. Comput Methods Biomech Biomed Engin [Internet]. 2021;24(8):864–73. https://www.tandfonline.com/doi/full, https://doi.org/10.1080/10255842.2020.1856372.

  172. Viswakumar A, Rajagopalan V, Ray T, Gottipati P, Parimi C. Development of a robust, simple, and affordable human gait analysis system using bottom-up pose estimation with a smartphone camera. Front Physiol [Internet]. 2021;12:784865. http://www.ncbi.nlm.nih.gov/pubmed/35069246.

  173. Stenum J, Cherry-Allen KM, Pyles CO, Reetzke RD, Vignos MF, Roemmich RT. Applications of pose estimation in human health and performance across the lifespan. sensors (Basel) [Internet]. 2021;21(21). http://www.ncbi.nlm.nih.gov/pubmed/34770620.

  174. Cornman HL, Stenum J, Roemmich RT. Video-based quantification of human movement frequency using pose estimation: a pilot study. PLoS One [Internet]. 2021;16(12):e0261450. http://www.ncbi.nlm.nih.gov/pubmed/34929012.

  175. Ahmed T, Thopalli K, Rikakis T, Turaga P, Kelliher A, Huang J-B, et al. Automated movement assessment in stroke rehabilitation. Front Neurol [Internet]. 2021;12:720650. http://www.ncbi.nlm.nih.gov/pubmed/34489855.

  176. Zhu Y, Lu W, Gan W, Hou W. A contactless method to measure real-time finger motion using depth-based pose estimation. Comput Biol Med [Internet]. 2021;131:104282. https://linkinghub.elsevier.com/retrieve/pii/S0010482521000767.

  177. Ren S, He K, Girshick R, Sun J. Faster R-CNN: towards real-time object detection with region proposal networks. 2015. http://arxiv.org/abs/1506.01497.

  178. Regan EW, Handlery R, Stewart JC, Pearson JL, Wilcox S, Fritz S. Integrating survivors of stroke into exercise-based cardiac rehabilitation improves endurance and functional strength. J Am Heart Assoc [Internet]. 2021;10(3):e017907. http://www.ncbi.nlm.nih.gov/pubmed/33499647.

  179. Hutchinson K, Sloutsky R, Collimore A, Adams B, Harris B, Ellis TD, et al. A music-based digital therapeutic: proof-of-concept automation of a progressive and individualized rhythm-based walking training program after stroke. Neurorehabil Neural Repair [Internet]. 2020;34(11):986–96. http://www.ncbi.nlm.nih.gov/pubmed/33040685.

  180. Backhaus W, Kempe S, Hummel FC. The effect of sleep on motor learning in the aging and stroke population - a systematic review. Restor Neurol Neurosci [Internet]. 2016;34(1):153–64. http://www.ncbi.nlm.nih.gov/pubmed/26835597.

  181. Vogels EA. About one-in-five Americans use a smart watch or fitness tracker [Internet]. January 9. 2020 [cited 2021 Dec 11]. https://www.pewresearch.org/fact-tank/2020/01/09/about-one-in-five-americans-use-a-smart-watch-or-fitness-tracker/.

    Google Scholar 

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  1. Department of Physical Medicine and Rehabilitation, Harvard Medical School at Spaulding Rehabilitation Hospital, Boston, MA, USA

    Catherine P. Adans-Dester & Paolo Bonato

  2. Washington University School of Medicine, in St. Louis, St. Louis, MO, USA

    Catherine E. Lang

  3. Department of Mechanical and Aerospace Engineering, Department of Anatomy and Neurobiology, University of California Irvine, University of California Irvine, Irvine, CA, USA

    David J. Reinkensmeyer

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    David J. Reinkensmeyer

  2. Delft University of Technology, Delft, The Netherlands

    Laura Marchal-Crespo

  3. Spinal Cord Injury Center, University Hospital Balgrist, Zürich, Switzerland

    Volker Dietz

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Adans-Dester, C.P., Lang, C.E., Reinkensmeyer, D.J., Bonato, P. (2022). Wearable Sensors for Stroke Rehabilitation. In: Reinkensmeyer, D.J., Marchal-Crespo, L., Dietz, V. (eds) Neurorehabilitation Technology. Springer, Cham. https://doi.org/10.1007/978-3-031-08995-4_21

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  • DOI: https://doi.org/10.1007/978-3-031-08995-4_21

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-08994-7

  • Online ISBN: 978-3-031-08995-4

  • eBook Packages: MedicineMedicine (R0)

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