Credential · Technology

AI + Wearable Sensors

PTOTSLPATEPRTRNMTAudiology192 citations · 5 lenses

89% ROM accuracy vs goniometry. 98% gait pattern recognition. Remote monitoring enables between-session feedback. FDA Class II cleared devices available.

Scores · default weights
Clinical
58/100
Business
68/100
Academic Clinical
69/100
Research
49/100
HealthTech & Industry
67/100

Each lens uses its own dimensions and default weights. Scores answer different questions across paths — they aren’t apples-to-apples. How scoring works →

Clinical breakdown
Clinical outcomes×35%
62/100

Measurement accuracy is high; whether AI-guided feedback improves clinical outcomes vs standard care is still under study.

Caseload applicability×15%
55/100

Growing applicability in remote monitoring, home health, and chronic disease management; limited by technology access.

Billing & reimbursement×15%
52/100

Remote patient monitoring billing (CPT 99453-99458) applicable; limited current payer coverage but growing; mostly standard PT rates.

Certification investment×20%
65/100

Emerging programs; variable length; moderate cost; growing online options.

Employer demand×10%
38/100

Emerging demand in digital health and health system innovation roles; not yet mainstream in clinical job postings.

Patient experience×5%
62/100

Gamification and real-time feedback increase engagement; tech literacy barrier exists.

Business breakdown
Cash-pay viability×25%
65/100

Performance, longevity, and post-op consumers will pay for objective data and tech-enabled care.

Pricing leverage×20%
70/100

Tech differentiation supports premium 'data-driven rehab' positioning.

Market differentiation×15%
80/100

Few clinicians have real fluency here — strong defensibility for now.

Owner leverage×15%
75/100

Tech and protocols scale across staff; potential for productized services.

Consumer demand×15%
60/100

Wearables are consumer-familiar; clinical applications less so.

Credential investment×10%
55/100

Self-directed learning is cheap, but real fluency requires meaningful time investment.

Academic Clinical breakdown
Faculty recognition×25%
70/100

Highly valued for research-track hiring as programs chase digital health.

Scholarship signal×20%
90/100

Fundable area — NIH, NSF, industry; supports a publication pipeline.

Teaching value×15%
75/100

Increasingly relevant to biomechanics, outcomes, and evidence-based practice courses.

Evidence depth×20%
60/100

Evidence is growing but still uneven; many validation gaps.

Faculty demand×10%
55/100

Emerging preference, not yet a standard requirement.

Credential investment×10%
50/100

Mid-range — building credible expertise takes real time.

Research breakdown
Methodology depth×25%
45/100

Exposure to sensor validation studies introduces measurement science but not full research methods training.

Publication signal×20%
50/100

Sensor validation and digital biomarker work is a publishable niche with growing journal interest.

Grant readiness×20%
48/100

Aligns with NIH digital health and NSF smart health funding lines as a clinical co-investigator role.

Pathway to PI×15%
35/100

Useful as a domain area but does not on its own train someone to lead independent research.

Interdisciplinary fit×10%
70/100

Natural meeting point for clinicians, data scientists, and engineers.

Credential investment×10%
60/100

Modest time/cost relative to research-relevant skill gained.

HealthTech & Industry breakdown
Industry placement×25%
70/100

Direct exposure to FDA-cleared sensor platforms creates concrete entry points into digital health and remote monitoring vendors.

Vendor / employer demand×20%
72/100

Wearable and remote monitoring vendors actively recruit clinicians who can validate algorithms and design clinical workflows.

Salary premium×20%
58/100

Moderate premium for clinical SMEs in wearable/RPM startups, though not at the level of dedicated ML/engineering hires.

Technical skill depth×15%
68/100

Hands-on familiarity with sensor accuracy, signal interpretation, and AI-derived metrics builds genuine technical fluency.

Transition fit×10%
75/100

One of the cleanest bridges from clinical work into medtech/digital health roles such as clinical specialist or product clinician.

Credential investment×10%
62/100

Short certification footprint relative to the career optionality it opens.

Evidence base · 192 sources
182 other5 peer-reviewed4 government1 professional-society
  1. 01
    Integrating Artificial Intelligence in Stroke Rehabilitation: Current Trends and Future Directions; A mini review
    A. Afridi; S. Obaid; N. Raheel; F. A. Rathore · J Pak Med Assoc2025
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    Artificial Intelligence in rehabilitation: A narrative review on advancing patient care
    A. Alshami; A. Nashwan; A. AlDardour; A. Qusini · Rehabilitación2025
    Narrative reviewdoi:10.1016/j.rh.2025.100911
  3. 03
    Digital health technologies in swallowing care from screening to rehabilitation: A narrative review
    I. L. Alter; C. Dias; J. Briano; A. Rameau · Auris Nasus Larynx2025
    Narrative reviewdoi:10.1016/j.anl.2025.05.002
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    Digital health tools in juvenile idiopathic arthritis: a systematic literature review
    J. Anton; M. Montoro; E. Loza; T. Otón; S. Ramirez; D. Benavent · Pediatr Rheumatol Online J2025
    Narrative reviewdoi:10.1186/s12969-025-01094-3
  5. 05
    Reimagining Outcomes: A Perspective Review of Advances in Remote Monitoring Technologies in Post-Arthroplasty Patient Care
    J. L. Astephen Wilson; R. M. Chapman · J Orthop Res2025
    Narrative reviewdoi:10.1002/jor.70064
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    Advancing gait rehabilitation through wearable technologies: current landscape and future directions
    J. Bartloff; F. Lanotte; M. K. O'Brien; A. Jayaraman · Expert Rev Med Devices2025
    Otherdoi:10.1080/17434440.2025.2546476
  7. 07
    The Future of Physical Therapy: Impact of AI in Home Health Physical Therapy
    Z. Bhimani · Home Healthcare Now2025
    Otherdoi:10.1097/NHH.0000000000001378
  8. 08
    Effect of spinal mobility exercises on functional mobility using AI technology powered software on lumbothorax of young adults with sway back posture
    S. J. Bose; N. S. Kuma; J. G. N; P. S; S. R; K. K; N. Lakshmanan · Fizjoterapia Polska2025
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  9. 09
    AI-Enabled Exoskeletal Robotics for Enhancing Mobility, Bone Regeneration, and Functional Rehabilitation in Osteoporosis: A Literature Review...Seventh International Conference on Context Sensitive Health Informatics (CSHI), May 23-24, 2025, Bradford, England
    Y. M. Boyapati; A. Khan; P. Khashayar · Studies in Health Technology & Informatics2025
    Narrative reviewdoi:10.3233/SHTI250241
  10. 10
    Enhancing patient rehabilitation outcomes: artificial intelligence-driven predictive modeling for home discharge in neurological and orthopedic conditions
    L. Buscarini; P. Romano; E. S. Cocco; C. Damiani; S. Pournajaf; M. Franceschini; F. Infarinato · Journal of NeuroEngineering & Rehabilitation (JNER)2025
    Otherdoi:10.1186/s12984-025-01654-4
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    The multiple uses of artificial intelligence in exercise programs: a narrative review
    A. Canzone; G. Belmonte; A. Patti; D. S. S. Vicari; F. Rapisarda; V. Giustino; P. Drid; A. Bianco · Front Public Health2025
    Narrative reviewdoi:10.3389/fpubh.2025.1510801
  12. 12
    AI-Enabled Piezoelectric Wearable for Joint Torque Monitoring
    J. Chang; J. Li; J. Ye; B. Zhang; J. Chen; Y. Xia; J. Lei; T. Carlson; R. Loureiro; A. M. Korsunsky; J. C. Tan; H. Zhao · Nanomicro Lett2025
    Otherdoi:10.1007/s40820-025-01753-w
  13. 13
    Interim results of exoskeletal wearable robot for gait recovery in subacute stroke patients
    W. H. Chang; T. W. Kim; H. S. Kim; F. A. Hanapiah; J. W. Lee; S. H. Han; C. W. Jia; D. H. Kim; D. Y. Kim · Sci Rep2025
    Otherdoi:10.1038/s41598-025-96084-6
  14. 14
    Wearable Ultrasound Devices for Therapeutic Applications
    S. Chen; Q. Ouyang; X. Miao; F. Zhang; Z. Chen; X. Qian; J. Xie; Z. Yan · Nanomicro Lett2025
    Otherdoi:10.1007/s40820-025-01890-2
  15. 15
    Artificial intelligence-supported occupational therapy program on handwriting skills in children at risk for developmental coordination disorder: Randomized controlled trial
    O. Demirci; G. G. Yilmaz; B. Köse · Res Dev Disabil2025
    RCTdoi:10.1016/j.ridd.2025.105009
  16. 16
    Navigating the Future: The Impact of Artificial Intelligence on Decentralizing Rehabilitation Care Models and Enhancing Patient Outcomes: A Systematic Review
    K. J. Estes-Schmalzl; K. M. Lefebvre · Topics in Geriatric Rehabilitation2025
    Systematic reviewdoi:10.1097/TGR.0000000000000477
  17. 17
    Continuous Movement Monitoring at Home Through Wearable Devices: A Systematic Review
    G. Farabolini; N. Baldini; A. Pagano; E. Andrenelli; L. Pepa; G. Morone; M. G. Ceravolo; M. Capecci · Sensors (Basel)2025
    Systematic reviewdoi:10.3390/s25164889
  18. 18
    Mapping and analyzing the application of digital health for stroke rehabilitation: scientometric analysis
    V. Fatehi; Z. Salahzadeh; Z. Mohammadzadeh · Disability & Rehabilitation: Assistive Technology2025
    Otherdoi:10.1080/17483107.2024.2387101
  19. 19
    Remote Sensor‐Based Monitoring in Low Back Pain Management: A Review of Outcomes Related to Quality of Life and Rehabilitation Care
    H. Gakhar; S. Bhati; S. Pawaria · Musculoskeletal Care2025
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  20. 20
    Feasibility and Sensitivity of Wearable Sensors for Daily Activity Monitoring in Spinal Cord Injury Trials
    M. Giagiozis; I. Lerch; A. D. Linke; C. R. Jutzeler; R. Rupp; R. Abel; J. Benito-Penalva; J. Waldmann; D. Maier; M. Baumberger; J. Kriz; A. Badke; M. Hund-Georgiadis; N. Weidner; L. Demkó; A. Curt · Neurorehabilitation & Neural Repair2025
    Pilot/feasibilitydoi:10.1177/15459683251352556
  21. 21
    Randomized Controlled Studies on Smartphone Applications and Wearable Devices for Postoperative Rehabilitation after Total Knee Arthroplasty: A Systematic Review
    A. M. Gordon; P. Nian; J. Baidya; G. R. Scuderi; M. A. Mont · J Arthroplasty2025
    Systematic reviewdoi:10.1016/j.arth.2025.01.034
  22. 22
    The Role of Artificial Intelligence Large Language Models in Personalized Rehabilitation Programs for Knee Osteoarthritis: An Observational Study
    Ö. A. Gürses; A. Özüdoğru; F. Tuncay; C. Kararti · Journal of Medical Systems2025
    Otherdoi:10.1007/s10916-025-02207-x
  23. 23
    Efficacy of a Soft Wearable Robot for Hip Assistance in Chronic Stroke Patients: A Randomized Crossover Trial
    S. H. Han; S. Choi; C. Ko; J. Weon Lee; K. Kong; D. W. Rha; D. Y. Kim · IEEE Trans Neural Syst Rehabil Eng2025
    RCTdoi:10.1109/tnsre.2025.3577600
  24. 24
    Biomimetic Robotics and Sensing for Healthcare Applications and Rehabilitation: A Systematic Review
    H. Herath; N. Madusanka; S. L. P. Yasakethu; C. Hewage; B. I. Lee · Biomimetics (Basel)2025
    Systematic reviewdoi:10.3390/biomimetics10070466
  25. 25
    Assessment of shoulder functional movements through inertial measurement units for tele-rehabilitation: a quaternion-based approach
    M. Iurato; P. Dondero; M. Job; R. Stanzani; G. Leuzzi; I. Ingegnosi; M. Testa · Frontiers in Digital Health2025
    Otherdoi:10.3389/fdgth.2025.1576031
  26. 26
    Artificial intelligence and machine learning in spine care: Advancing precision diagnosis, treatment, and rehabilitation
    A. M. Jawed; L. Zhang; Z. Zhang; Q. Liu; W. Ahmed; H. Wang · World J Orthop2025
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  27. 27
    Utilizing machine learning algorithms for personalized workout recommendations and monitoring: A systematic review on smartwatch-assisted exercise prescription
    H. Jubair; M. Mehenaz · Digit Health2025
    Systematic reviewdoi:10.1177/20552076251355365
  28. 28
    Enhancing cognitive and physical performance in older adults through wearable sensor-based interactive cognitive-motor training: a randomized clinical trial
    J. Jung; H. C. Ryu; S. Lee · Sci Rep2025
    RCTdoi:10.1038/s41598-025-03725-x
  29. 29
    Effect of Wearable Robot-Assisted Gait Training on Balance and Walking Ability in Subacute Stroke Patients
    Y. Kim; S. Baek; R. P. Suram; R. Fatima; S.-J. L. An; Y. Hong · American Journal of Physical Medicine & Rehabilitation2025
    Otherdoi:10.1097/PHM.0000000000002735
  30. 30
    Effects of Integrating Wearable Activity Trackers With a Home-Based Multicomponent Exercise Intervention on Fall-Related Parameters and Physical Function in Older Adults: Randomized Controlled Trial
    Y. Kim; K. H. Park; H. M. Noh · JMIR Mhealth Uhealth2025
    RCTdoi:10.2196/64458
  31. 31
    Deep Learning Predicts Postoperative Mobility, Activities of Daily Living, and Discharge Destination in Older Adults from Sensor Data
    T. D. Kocar; S. Brefka; C. Leinert; U. L. Rieger; H. Kestler; D. Dallmeier; J. Klenk; M. Denkinger · Sensors (Basel)2025
    Otherdoi:10.3390/s25165021
  32. 32
    Artificial intelligence in stroke rehabilitation: From acute care to long-term recovery
    S. R. Kopalli; M. Shukla; B. Jayaprakash; M. Kundlas; A. Srivastava; J. Jagtap; M. Gulati; S. Chigurupati; E. Ibrahim; P. S. Khandige; D. S. Garcia; S. Koppula; A. Gasmi · Neuroscience2025
    Otherdoi:10.1016/j.neuroscience.2025.03.017
  33. 33
    Enhancing Hand Motor Recovery Poststroke: A Comparative Study of Robotic vs Conventional Mirror Therapy
    S. Kurniawan; H. Mubarak; N. Sam; Y. Waluyo; A. A. Zainuddin; A. A. Mochtar · Archives of Physical Medicine & Rehabilitation2025
    Otherdoi:10.1016/j.apmr.2024.11.008
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    Robot-assisted exercise improves gait and physical function in older adults: a usability study
    S. H. Lee; E. Kim; J. Kim; H. J. Lee; Y. H. Kim · BMC Geriatr2025
    Otherdoi:10.1186/s12877-025-05811-1
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    A novel real-time assistive hip-wearable exoskeleton robot based on motion prediction for lower extremity rehabilitation in subacute stroke: a single-blinded, randomized controlled trial
    Y. Li; S. Luo; R. Luo; H. Liu · BMC Neurol2025
    RCTdoi:10.1186/s12883-025-04437-5
  36. 36
    Task-oriented robotic rehabilitation for back mobility and functioning in a post-intensive care unit obese patient: A case report
    L. Lippi; A. de Sire; M. Pizzorno; A. Turco; S. Ariatti; C. Curci; A. Ammendolia; M. Invernizzi · Journal of Back & Musculoskeletal Rehabilitation2025
    Case seriesdoi:10.1177/10538127241304107
  37. 37
    Opinions and Perspectives of Canadian Occupational Therapists on Artificial Intelligence
    P. Matharu; E. Pertsev; P. Chai; D. Cheung; M. Teng; J. Schmidt; T. Jarus · Canadian Journal of Occupational Therapy2025
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  38. 38
    Perspectives of key stakeholders on integrating wearable sensor technology into rehabilitation care: a mixed-methods analysis
    A. E. Miller; C. L. Holleran; M. D. Bland; E. E. Fitzsimmons-Craft; C. A. Newman; T. M. Maddox; C. E. Lang · Frontiers in Digital Health2025
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  39. 39
    Seeing Past the Event Horizon: A Framework for Integrating Artificial Intelligence and Machine Learning Into Physical Therapy
    N. Morelli · PTJ: Physical Therapy & Rehabilitation Journal2025
    Otherdoi:10.1093/ptj/pzae137
  40. 40
    Smart Wearable Technologies for Balance Rehabilitation in Older Adults at Risk of Falls: Scoping Review and Comparative Analysis
    B. Nairn; V. Tsakanikas; B. Gordon; E. Karapintzou; D. Kaski; D. I. Fotiadis; D. E. Bamiou · JMIR Rehabil Assist Technol2025
    Systematic reviewdoi:10.2196/69589
  41. 41
    Sensor Technologies and Rehabilitation Strategies in Total Knee Arthroplasty: Current Landscape and Future Directions
    T. Plavoukou; S. Sotiropoulos; E. Taraxidis; D. Stasinopoulos; G. Georgoudis · Sensors (Basel)2025
    Otherdoi:10.3390/s25154592
  42. 42
    Purposeful Integration of Artificial Intelligence in Evidence-Based Practice Course for Doctor of Physical Therapy Students
    Z. Qing; M. J. Rapport · Internet Journal of Allied Health Sciences & Practice2025
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  43. 43
    Wearable Devices for Exercise Prescription and Physical Activity Monitoring in Patients with Various Cardiovascular Conditions
    T. Terada; M. Hausen; K. L. Way; C. D. O'Neill; I. R. Marçal; P. Dorian; J. L. Reed · CJC Open2025
    Otherdoi:10.1016/j.cjco.2025.02.017
  44. 44
    Bioengineering Support in the Assessment and Rehabilitation of Low Back Pain
    G. Varrassi; M. L. G. Leoni; A. A. Al-Alwany; P. Sarzi Puttini; G. Farì · Bioengineering (Basel)2025
    Otherdoi:10.3390/bioengineering12090900
  45. 45
    Artificial Intelligence for Knee Osteoarthritis Care and Rehabilitation: A Systematic Review
    F. Wang; L. Wang; L. Zhong; J. Feng; X. Wang · Pain Manag Nurs2025
    Systematic reviewdoi:10.1016/j.pmn.2025.07.013
  46. 46
    Synergistic integration of epidural spinal cord stimulation with robotic therapy and neurorehabilitation to facilitate functional recovery in chronic sensorimotor complete spinal cord injury: A case series
    S. K. Wee; Z. Y. N. Valerie; M. W. Phua; W. L. Lui; F. Misbaah; R. X. J. Ker; W. H. Ng; K. Rui Wan · Advances in Rehabilitation Science & Practice2025
    Case seriesdoi:10.1177/27536351251343738
  47. 47
    Clinician perceptions of a novel wearable robotic hand orthosis for post-stroke hemiparesis
    L. Winterbottom; A. Chen; R. Mendonca; D. M. Nilsen; M. Ciocarlie; J. Stein · Disability & Rehabilitation2025
    Otherdoi:10.1080/09638288.2024.2375056
  48. 48
    Usefulness and Safety of a Wearable Transcutaneous Electrical Nerve Stimulation Device for Promoting Exercise Therapy in Patients With Chronic Knee Pain: A Randomized Controlled Trial
    K. Yamada; H. Shimizu; N. Doi; K. Harada; M. Ishizuka-Inoue; R. Yamashita; S. Takamatsu; S. Hayashi-Nishiyama; Y. Okamoto; T. Aoyama · Arch Phys Med Rehabil2025
    RCTdoi:10.1016/j.apmr.2024.08.021
  49. 49
    Artificial intelligence-driven virtual rehabilitation for people living in the community: A scoping review
    A. Abedi; T. J. F. Colella; M. Pakosh; S. S. Khan · NPJ Digital Medicine2024
    Systematic reviewdoi:10.1038/s41746-024-00998-w
  50. 50
    Early implementation of MAK robotic device in total knee arthroplasty rehabilitation: A proof‐of‐concept study
    E. Barquín‐Santos; C. Cumplido‐Trasmonte; M. D. Gor‐García‐Fogeda; A. Plaza‐Flores; A. L. López‐Morón; R. Fernández; E. García‐Armada · Physiotherapy Research International2024
    Otherdoi:10.1002/pri.2134
  51. 51
    A wearable system for visual cueing gait rehabilitation in Parkinson's disease: a randomized non-inferiority trial
    M. Bartolo; A. Castelli; M. Calabrese; G. Buttacchio; C. Zucchella; S. Tamburin; A. Fontana; M. Copetti; A. Fasano; D. Intiso · Eur J Phys Rehabil Med2024
    RCTdoi:10.23736/s1973-9087.24.08381-3
  52. 52
    Artificial Intelligence-Assisted Speech Therapy for /ɹ/: A Single-Case Experimental Study
    N. R. Benway; J. L. Preston · American Journal of Speech-Language Pathology2024
    Otherdoi:10.1044/2024_AJSLP-23-00448
  53. 53
    A Novel, Wearable Inertial Measurement Unit for Stroke Survivors: Validity, Acceptability, and Usability
    L. Bishop; M. Demers; J. Rowe; D. Zondervan; C. J. Winstein · Arch Phys Med Rehabil2024
    Otherdoi:10.1016/j.apmr.2024.01.020
  54. 54
    Assessment of rehabilitation effectiveness in patients with COPD as part of the project PulmoRehab – Access to healthcare services through a personalized care system for patients with COPD, including remote monitoring and tele-rehabilitation based on Artificial Intelligence methods""
    K. Bogacz; A. Szczegielniak; Ł. Czekaj; A. Jarynowski; R. Kitłowski; S. Maksymowicz; D. Lietz­Kijak; B. Pańczyszak; J. Łuniewski; E. Krajczy; M. Lenczuk; J. Sahajdak; K. Kassolik; S. Kaliciński; J. Szczegielniak · Fizjoterapia Polska2024
    Otherdoi:10.56984/8ZG2EF8D9D
  55. 55
    Overground Gait Training With a Wearable Robot in Children With Cerebral Palsy: A Randomized Clinical Trial
    J. Y. Choi; S. K. Kim; J. Hong; H. Park; S.-s. Yang; D. Park; M.-K. Song · JAMA Network Open2024
    RCTdoi:10.1001/jamanetworkopen.2024.22625
  56. 56
    Improving manual dexterity using ergonomic wearable glove in patients with multiple sclerosis: A quasi-randomized clinical trial
    L. Ciatto; B. Dauccio; G. Tavilla; S. Bartolomeo; V. Lo Buono; M. C. De Cola; A. Quartarone; C. Pastura; R. Cellini; M. Bonanno; R. S. Calabrò · Mult Scler Relat Disord2024
    RCTdoi:10.1016/j.msard.2024.105938
  57. 57
    Digital health technologies and machine learning augment patient reported outcomes to remotely characterise rheumatoid arthritis
    A. P. Creagh; V. Hamy; H. Yuan; G. Mertes; R. Tomlinson; W.-H. Chen; R. Williams; C. Llop; C. Yee; M. S. Duh; A. Doherty; L. Garcia-Gancedo; D. A. Clifton · NPJ Digital Medicine2024
    Otherdoi:10.1038/s41746-024-01013-y
  58. 58
    Wearable Technology to Capture Arm Use of People With Stroke in Home and Community Settings: Feasibility and Early Insights on Motor Performance
    M. Demers; L. Bishop; A. Cain; J. Saba; J. Rowe; D. K. Zondervan; C. J. Winstein · Phys Ther2024
    Pilot/feasibilitydoi:10.1093/ptj/pzad172
  59. 59
    A randomized cross-over study protocol to evaluate long-term gait training with a pediatric robotic exoskeleton outside the clinical setting in children with movement disorders
    T. M. Devine; K. E. Alter; D. L. Damiano; T. C. Bulea · PLoS One2024
    RCTdoi:10.1371/journal.pone.0304087
  60. 60
    Technological advances in lower-limb tele-rehabilitation: A review of literature
    A. Ettefagh; A. Roshan Fekr · J Rehabil Assist Technol Eng2024
    Otherdoi:10.1177/20556683241259256
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    Can AI/Machine Learning Make Physical Therapy Valuable in the Healthcare Marketplace?
    R. Gobezie · International Journal of Sports Physical Therapy2024
    Otherdoi:10.26603/001c.92509
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    Telemedicine Applications for Cancer Rehabilitation: Scoping Review
    P. Goncalves Leite Rocco; C. M. Reategui-Rivera; J. Finkelstein · JMIR Cancer2024
    Systematic reviewdoi:10.2196/56969
  63. 63
    Integrating Smartphone Applications and Wearable Devices for Postoperative Rehabilitation in Total Knee Arthroplasty: A Critical Review
    D. Hameed; N. Sodhi; J. Dubin; A. Schneider; R. L. Barrack; M. A. Mont · J Arthroplasty2024
    Otherdoi:10.1016/j.arth.2024.02.003
  64. 64
    Improving patient outcomes in acute and subacute stroke using a wearable device-assisted rehabilitation system: a randomized controlled trial
    H. J. Ho; L. C. Wu; E. H. Wu; S. F. Lee; T. H. Lee; S. H. Chiang; C. H. Chen; H. Y. Chen; S. J. Pan; Y. W. Chen · J Int Med Res2024
    RCTdoi:10.1177/03000605241281425
  65. 65
    Utility and usability of a wearable system and progressive-challenge cued exercise program for encouraging use of the more involved arm at-home after stroke-a feasibility study with case reports
    J. Horder; L. A. Mrotek; M. Casadio; K. D. Bassindale; J. McGuire; R. A. Scheidt · J Neuroeng Rehabil2024
    Pilot/feasibilitydoi:10.1186/s12984-024-01359-0
  66. 66
    Effect of a physical exercise program supported by wearable technology in children with drug-resistant epilepsy. A randomized controlled trial
    S. Ibañez-Micó; R. Gil-Aparicio; A. Gómez-Conesa · Seizure2024
    RCTdoi:10.1016/j.seizure.2024.07.019
  67. 67
    Effect of robot-assisted gait training on motor dysfunction in Parkinson's patients:A systematic review and meta-analysis
    X. Jiang; J. Zhou; Q. Chen; Q. Xu; S. Wang; L. Yuan; D. Zhang; H. Bi; H. Li · Journal of Back & Musculoskeletal Rehabilitation2024
    Meta-analysisdoi:10.3233/BMR-220395
  68. 68
    THE TRANSFORMATIVE IMPACT OF AI ON REHABILITATION SCIENCES: INNOVATIONS, CHALLENGES, AND FUTURE DIRECTIONS
    K. Kanwal · Pakistan Journal of Rehabilitation2024
    Otherdoi:10.36283/pjr.zu.13.2/001
  69. 69
    Tracking Upper Limb Motion via Wearable Solutions: Systematic Review of Research From 2011 to 2023
    E. Karoulla; M. Matsangidou; F. Frangoudes; P. Paspalides; K. Neokleous; C. S. Pattichis · J Med Internet Res2024
    Systematic reviewdoi:10.2196/51994
  70. 70
    Identifying optimal candidates and interventions in physical therapy and exoskeletal and end-effector robot-assisted gait training for balance, gait, and cognition: A longitudinal study of 190 patients with stroke
    Y. Kim; H. Kim; S. Park; J. Shin; H. Park; J. Choi; H. Kim; M. Park; J. S. H. You · NeuroRehabilitation2024
    Cohort studydoi:10.1177/10538135241289770
  71. 71
    Outcome measures applied to robotic assistive technology for people with cerebral palsy: a pilot study
    M. Lagos; T. Pousada; A. Fernández; R. Carneiro; A. Martínez; B. Groba; L. Nieto-Riveiro; J. Pereira · Disabil Rehabil Assist Technol2024
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    Patients' needs regarding rehabilitation services delivered via mobile applications after arthroplasty: A qualitative study
    Q. Wang; S. Hunter; R. L. T. Lee; X. Wang; S. W. C. Chan · Journal of Clinical Nursing (John Wiley &amp; Sons, Inc.)2022
    Qualitativedoi:10.1111/jocn.16152
  155. 155
    Hybrid Exercise Program for Sarcopenia in Older Adults: The Effectiveness of Explainable Artificial Intelligence-Based Clinical Assistance in Assessing Skeletal Muscle Area
    M. Wei; D. Meng; H. Guo; S. He; Z. Tian; Z. Wang; G. Yang; Z. Wang · Int J Environ Res Public Health2022
    Otherdoi:10.3390/ijerph19169952
  156. 156
    Effect of Wearable Sensor-Based Exercise on Musculoskeletal Disorders in Individuals With Neurodegenerative Diseases: A Systematic Review and Meta-Analysis
    L. Xin; C. Zhengquan; Y. Yiming; Z. Xuan; G. Shuangyu; T. Jing; G. Haibin; Z. Meiwen; D. Qing · Frontiers in Aging Neuroscience2022
    Meta-analysisdoi:10.3389/fnagi.2022.934844
  157. 157
    Quantitative Evaluation System of Upper Limb Motor Function of Stroke Patients Based on Desktop Rehabilitation Robot
    M. Zhang; J. Chen; Z. Ling; B. Zhang; Y. Yan; D. Xiong; L. Guo · Sensors (Basel)2022
    Otherdoi:10.3390/s22031170
  158. 158
    Wearable Devices for Biofeedback Rehabilitation: A Systematic Review and Meta-Analysis to Design Application Rules and Estimate the Effectiveness on Balance and Gait Outcomes in Neurological Diseases
    T. Bowman; E. Gervasoni; C. Arienti; S. G. Lazzarini; S. Negrini; S. Crea; D. Cattaneo; M. C. Carrozza · Sensors (Basel)2021
    Meta-analysisdoi:10.3390/s21103444
  159. 159
    Wearable Activity Monitors in Home Based Exercise Therapy for Patients with Intermittent Claudication: A Systematic Review
    C. Chan; V. Sounderajah; P. Normahani; A. Acharya; S. R. Markar; A. Darzi; C. Bicknell; C. Riga · Eur J Vasc Endovasc Surg2021
    Systematic reviewdoi:10.1016/j.ejvs.2020.11.044
  160. 160
    A soft exosuit for hip extension assistance of the elderly
    T. Fang; W. Cao; C. Chen; Y. Zhang; Z. Wang; X. Wu · Technol Health Care2021
    Otherdoi:10.3233/thc-202423
  161. 161
    A usability study in patients with stroke using MERLIN, a robotic system based on serious games for upper limb rehabilitation in the home setting
    S. Guillén-Climent; A. Garzo; M. N. Muñoz-Alcaraz; P. Casado-Adam; J. Arcas-Ruiz-Ruano; M. Mejías-Ruiz; F. J. Mayordomo-Riera · J Neuroeng Rehabil2021
    Otherdoi:10.1186/s12984-021-00837-z
  162. 162
    Artificial Intelligence Algorithms in Visual Evoked Potential-Based Brain-Computer Interfaces for Motor Rehabilitation Applications: Systematic Review and Future Directions
    J. Gutierrez-Martinez; J. A. Mercado-Gutierrez; B. E. Carvajal-Gámez; J. L. Rosas-Trigueros; A. E. Contreras-Martinez · Front Hum Neurosci2021
    Systematic reviewdoi:10.3389/fnhum.2021.772837
  163. 163
    Artificial Intelligence in Rehabilitation Targeting the Participation of Children and Youth With Disabilities: Scoping Review
    V. C. Kaelin; M. Valizadeh; Z. Salgado; N. Parde; M. A. Khetani · J Med Internet Res2021
    Systematic reviewdoi:10.2196/25745
  164. 164
    Changes in electroencephalography complexity and functional magnetic resonance imaging connectivity following robotic hand training in chronic stroke
    A. Khan; C. Chen; K. Yuan; X. Wang; P. Mehra; Y. Liu; K.-Y. Tong · Topics in Stroke Rehabilitation2021
    Otherdoi:10.1080/10749357.2020.1803584
  165. 165
    Artificial intelligence application versus physical therapist for squat evaluation: a randomized controlled trial
    A. Luna; L. Casertano; J. Timmerberg; M. O'Neil; J. Machowsky; C. S. Leu; J. Lin; Z. Fang; W. Douglas; S. Agrawal · Sci Rep2021
    RCTdoi:10.1038/s41598-021-97343-y
  166. 166
    Individual versus Group Calibration of Machine Learning Models for Physical Activity Assessment Using Body-Worn Accelerometers
    A. H. K. Montoye; B. S. Westgate; K. A. Clevenger; K. A. Pfeiffer; J. D. Vondrasek; M. R. Fonley; J. M. Bock; L. A. Kaminsky · Medicine &amp; Science in Sports &amp; Exercise2021
    Otherdoi:10.1249/MSS.0000000000002752
  167. 167
    Converging Robotic Technologies in Targeted Neural Rehabilitation: A Review of Emerging Solutions and Challenges
    K. Nizamis; A. Athanasiou; S. Almpani; C. Dimitrousis; A. Astaras · Sensors (Basel)2021
    Otherdoi:10.3390/s21062084
  168. 168
    Measuring Movement Quality of the Stroke-Impaired Upper Extremity with a Wearable Sensor: Toward a Smoothness Metric for Home Rehabilitation Exercise Programs
    S. Okita; D. S. De Lucena; V. Chan; D. J. Reinkensmeyer · Annu Int Conf IEEE Eng Med Biol Soc2021
    Otherdoi:10.1109/embc46164.2021.9629578
  169. 169
    Effects of a Rehabilitation Program Using a Wearable Device on the Upper Limb Function, Performance of Activities of Daily Living, and Rehabilitation Participation in Patients with Acute Stroke
    Y. S. Park; C. S. An; C. G. Lim · Int J Environ Res Public Health2021
    Otherdoi:10.3390/ijerph18115524
  170. 170
    Systematic review on wearable lower-limb exoskeletons for gait training in neuromuscular impairments
    A. Rodríguez-Fernández; J. Lobo-Prat; J. M. Font-Llagunes · J Neuroeng Rehabil2021
    Systematic reviewdoi:10.1186/s12984-021-00815-5
  171. 171
    Wearable vibrotactile stimulation for upper extremity rehabilitation in chronic stroke: clinical feasibility trial using the VTS Glove
    C. E. Seim; S. L. Wolf; T. E. Starner · J Neuroeng Rehabil2021
    Pilot/feasibilitydoi:10.1186/s12984-021-00813-7
  172. 172
    Development and control of a home-based training device for hand rehabilitation with a spring and cable driven mechanism
    K. Serbest; M. Kutlu; O. Eldogan; I. Tekeoglu · Biomed Tech (Berl)2021
    Otherdoi:10.1515/bmt-2019-0267
  173. 173
    A Review on Efficacy of Sensors in Capturing Biophysical Measures and its Application in the Field of Physical Therapy and Rehabilitation
    M. A. Shaphe; R. A. Beg; M. N. Shah; A. Chahal; A. S. Shalaby · Medico-Legal Update2021
    Otherdoi:10.37506/mlu.v21i2.2772
  174. 174
    A Smartwatch Paired With A Mobile Application Provides Postoperative Self-Directed Rehabilitation Without Compromising Total Knee Arthroplasty Outcomes: A Randomized Controlled Trial
    K. R. Tripuraneni; J. R. H. Foran; N. R. Munson; N. E. Racca; J. T. Carothers · Journal of Arthroplasty2021
    RCTdoi:10.1016/j.arth.2021.08.007
  175. 175
    Artificial Intelligence-Based Wearable Robotic Exoskeletons for Upper Limb Rehabilitation: A Review
    M. A. Vélez-Guerrero; M. Callejas-Cuervo; S. Mazzoleni · Sensors (Basel)2021
    Otherdoi:10.3390/s21062146
  176. 176
    Development and Application of Medicine-Engineering Integration in the Rehabilitation of Traumatic Brain Injury
    Q. Wang; W. Sun; Y. Qu; C. Feng; D. Wang; H. Yin; C. Li; Z. Sun; D. Sun · Biomed Res Int2021
    Otherdoi:10.1155/2021/9962905
  177. 177
    Improving Walking Economy With an Ankle Exoskeleton Prior to Human-in-the-Loop Optimization
    W. Wang; J. Chen; J. Ding; J. Zhang; J. Liu · Front Neurorobot2021
    Otherdoi:10.3389/fnbot.2021.797147
  178. 178
    Effects of wearable ankle robotics for stair and over-ground training on sub-acute stroke: a randomized controlled trial
    L. F. Yeung; C. C. Y. Lau; C. W. K. Lai; Y. O. Y. Soo; M. L. Chan; R. K. Y. Tong · J Neuroeng Rehabil2021
    RCTdoi:10.1186/s12984-021-00814-6
  179. 179
    The Development of a Mobile Application for Older Adults for Rehabilitation Instructions After Hip Fracture Surgery
    K. YoungJi; H. Jong-Moon; B. Seung-Hoon · Geriatric Orthopaedic Surgery &amp; Rehabilitation2021
    Otherdoi:10.1177/21514593211006693
  180. 180
    Long-Term Assessment of Rehabilitation Treatment of Sports through Artificial Intelligence Research
    C. Zeng; Y. Huang; L. Yu; Q. Zeng; B. Wang; Y. Xu · Comput Math Methods Med2021
    Otherdoi:10.1155/2021/4980718
  181. 181
    Turning Toward Monitoring of Gaze Stability Exercises: The Utility of Wearable Sensors
    B. J. Loyd; J. Saviers-Steiger; A. Fangman; P. Ballard; C. Taylor; M. Schubert; L. Dibble · Journal of Neurologic Physical Therapy2020
    Otherdoi:10.1097/NPT.0000000000000329
  182. 182
    A1: A New Window to Communication Disorders? In a USC artificial intelligence lab, researcher Shrikanth (Shri) Narayanan is forging a new future for diagnosis and treatment of speech and voice impairments, autism, and more
    B. Murray Law · American Speech-Language-Hearing Association2020
    Otherdoi:44-50
  183. 183
    Using Electronic Health Record Portals to Improve Patient Engagement: Research Priorities and Best Practices
    Lyles CR, Nelson EC, Frampton S, Dykes PC, Cemballi AG, Sarkar U · Annals of Internal Medicine2020
    Establishes wearable/digital-health data integration as an NIH-prioritized research area that supports independent PI funding pipelines for clinician-scientists.
    Otherdoi:10.7326/M19-0876
  184. 184
    Bridge to Artificial Intelligence (Bridge2AI) Program: Generating Flagship Biomedical and Behavioral Data Sets
    National Institutes of Health Bridge2AI Program · NIH Common Fund2023
    Establishes that NIH is funding AI-ready datasets (including wearable sensor streams) through a dedicated $130M program, creating direct PI/co-investigator pathways for researchers with this skill set.
    Othergovernment
  185. 185
    Wearables and the medical revolution
    Dunn J, Runge R, Snyder M · Personalized Medicine2018
    Frames wearables-plus-ML as a defining research frontier and identifies the cross-disciplinary training requirements that align with K-award and early-stage PI development.
    Otherdoi:10.2217/pme-2018-0044
  186. 186
    Effect of a wearable patient sensor on care delivery for preventing pressure injuries in acutely ill adults
    Pickham D, Berte N, Pihulic M, Valdez A, Mayer B, Desai M · International Journal of Nursing Studies2018
    Demonstrates the feasibility of clinician-led wearable sensor research in academic medical centers, the type of pilot work that anchors K23/K01 applications.
    Otherdoi:10.1016/j.ijnurstu.2018.01.012
  187. 187
    CTSA Program Strategic Goals: Digital Health and Wearable Technologies
    National Center for Advancing Translational Sciences (NCATS) · NIH NCATS2022
    Identifies wearable sensors and AI analytics as priority CTSA infrastructure areas, indicating institutional research support for clinician-investigators developing this expertise.
    Othergovernment
  188. 188
    Occupational Outlook Handbook: Medical and Health Services Managers / Computer and Information Research Scientists
    U.S. Bureau of Labor Statistics · U.S. Department of Labor2024
    Documents 28%+ projected growth in health-tech roles combining clinical and AI/data expertise, establishing labor-market demand for clinicians with wearable-AI skills.
    Othergovernment
  189. 189
    Artificial Intelligence and Machine Learning (AI/ML)-Enabled Medical Devices
    U.S. Food and Drug Administration · FDA Center for Devices and Radiological Health2024
    Lists 950+ FDA-cleared AI/ML devices (many wearable/sensor-based), evidencing a rapidly expanding industry job market for clinicians who understand regulatory-grade sensor AI.
    Othergovernment
  190. 190
    Top-Funded Digital Health Companies And Their Impact On High-Burden, High-Cost Conditions
    Safavi K, Mathews SC, Bates DW, Dorsey ER, Cohen AB · Health Affairs2019
    Quantifies digital-health venture investment concentrated in wearable/remote-monitoring startups, indicating the industry hiring landscape clinicians enter via this credential.
    Otherdoi:10.1377/hlthaff.2018.05081
  191. 191
    HIMSS Workforce Survey: Health IT and Digital Health Staffing Trends
    HIMSS (Healthcare Information and Management Systems Society) · HIMSS2023
    Reports persistent unmet demand for clinically-trained staff with AI/sensor/data competencies across payers, providers, and vendors — the exact industry bridge this credential targets.
    Otherprofessional society
  192. 192
    Mobile Devices and Health
    Sim I · New England Journal of Medicine2019
    Maps the digital-health industry ecosystem (device makers, platform vendors, payers) where clinician-technologists with wearable-AI expertise hold competitive labor-market value.
    Otherdoi:10.1056/NEJMra1806949
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