Methods Inf Med 2020; 59(01): 041-047
DOI: 10.1055/s-0040-1710380
Patient Rehabilitation Techniques

Design of a Low-Cost, Wearable Device for Kinematic Analysis in Physical Therapy Settings

Andrew Hua
1   Department of Kinesiology and Community Health, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States
,
Nicole Johnson
2   Department of Bioengineering, University of Illinois at Urbana-Champaign, Champaign, Illinois, United States
,
Joshua Quinton
3   Department of Physics, University of Illinois at Urbana-Champaign, Champaign, Illinois, United States
,
Pratik Chaudhary
4   Department of Computer Science, University of Illinois at Urbana-Champaign, Champaign, Illinois, United States
,
David Buchner
1   Department of Kinesiology and Community Health, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States
,
Manuel E. Hernandez
1   Department of Kinesiology and Community Health, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States
› Author Affiliations Funding Andrew Hua was supported in part by the Medical Scholars Program at the University of Illinois at Urbana-Champaign by a Patricia J. and Charles C.C. O'Morchoe Fellowship in Leadership Skills Awards and a Graduate Fellowship.
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Abstract

Background Unsupervised home exercise is a major component of physical therapy (PT). This study proposes an inexpensive, inertial measurement unit-based wearable device to capture kinematic data to facilitate exercise. However, conveying and interpreting kinematic data to non-experts poses a challenge due to the complexity and background knowledge required that most patients lack.

Objectives The objectives of this study were to identify key user interface and user experience features that would likely improve device adoption and assess participant receptiveness toward the device.

Methods Fifty participants were recruited to perform nine upper extremity exercises while wearing the device. Prior to exercise, participants completed an orientation of the device, which included examples of software graphics with exercise data. Surveys that measured receptiveness toward the device, software graphics, and ergonomics were given before and after exercise.

Results Participants were highly receptive to the device with 90% of the participants likely to use the device during PT. Participants understood how the simple kinematic data could be used to aid exercise, but the data could be difficult to comprehend with more complex movements. Devices should incorporate wireless sensors and emphasize ease of wear.

Conclusion Device-guided home physical rehabilitation can allow for individualized treatment protocols and improve exercise self-efficacy through kinematic analysis. Future studies should implement clinical testing to evaluate the impact a wearable device can have on rehabilitation outcomes.

Keywords

inertial measurement units - biomechanics - exercise - rehabilitation - wearable device

Supplementary Material



Publication History

Received: 30 October 2019

Accepted: 02 March 2020

Article published online:
14 June 2020

© 2020. Thieme. All rights reserved.

Georg Thieme Verlag KG
Stuttgart · New York

 

  • References

  • 1 Ahamed NU, Kobsar D, Benson L. , et al. Using wearable sensors to classify subject-specific running biomechanical gait patterns based on changes in environmental weather conditions. PLoS One 2018; 13 (09) e0203839 . Doi: 10.1371/journal.pone.0203839
    CrossrefPubMedGoogle Scholar
  • 2 Alberto R, Draicchio F, Varrecchia T, Silvetti A, Iavicoli S. Wearable monitoring devices for biomechanical risk assessment at work: current status and future challenges-a systematic review. Int J Environ Res Public Health 2018; 15 (09) E2001 . Doi: 10.3390/ijerph15092001
    CrossrefPubMedGoogle Scholar
  • 3 Benson LC, Ahamed NU, Kobsar D, Ferber R. New considerations for collecting biomechanical data using wearable sensors: number of level runs to define a stable running pattern with a single IMU. J Biomech 2019; 85: 187-192
    CrossrefPubMedGoogle Scholar
  • 4 Koldenhoven RM, Hertel J. Validation of a wearable sensor for measuring running biomechanics. Digit Biomark 2018; 2 (02) 74-78
    CrossrefPubMedGoogle Scholar
  • 5 Bandura A. Self-efficacy: toward a unifying theory of behavioral change. Psychol Rev 1977; 84 (02) 191-215
    CrossrefPubMedGoogle Scholar
  • 6 Gualtieri L, Rosenbluth S, Phillips J. Can a free wearable activity tracker change behavior? The impact of trackers on adults in a physician-led wellness group. JMIR Res Protoc 2016; 5 (04) e237 . Doi: 10.2196/resprot.6534
    CrossrefPubMedGoogle Scholar
  • 7 King AC, Hekler EB, Grieco LA. , et al. Effects of three motivationally targeted mobile device applications on initial physical activity and sedentary behavior change in midlife and older adults: a randomized trial. PLoS One 2016; 11 (06) e0156370 . Doi: 10.1371/journal.pone.0156370
    CrossrefPubMedGoogle Scholar
  • 8 Sullivan AN, Lachman ME. Behavior change with fitness technology in sedentary adults: a review of the evidence for increasing physical activity. Front Public Health 2017; 4: 289 . Doi: 10.3389/fpubh.2016.00289
    CrossrefPubMedGoogle Scholar
  • 9 Lambert TE, Harvey LA, Avdalis C. , et al. An app with remote support achieves better adherence to home exercise programs than paper handouts in people with musculoskeletal conditions: a randomised trial. J Physiother 2017; 63 (03) 161-167
    CrossrefPubMedGoogle Scholar
  • 10 Tulipani L, Boocock MG, Lomond KV, El-Gohary M, Reid DA, Henry SM. Validation of an inertial sensor system for physical therapists to quantify movement coordination during functional tasks. J Appl Biomech 2018; 34 (01) 23-30
    CrossrefPubMedGoogle Scholar
  • 11 Argent R, Slevin P, Bevilacqua A, Neligan M, Daly A, Caulfield B. Wearable sensor-based exercise biofeedback for orthopaedic rehabilitation: a mixed methods user evaluation of a prototype system. Sensors (Basel) 2019; 19 (02) E432 . Doi: 10.3390/s19020432
    CrossrefPubMedGoogle Scholar
  • 12 Burns DM, Leung N, Hardisty M, Whyne CM, Henry P, McLachlin S. Shoulder physiotherapy exercise recognition: machine learning the inertial signals from a smartwatch. Physiol Meas 2018; 39 (07) 075007 . Doi: 10.1088/1361-6579/aacfd9
    CrossrefPubMedGoogle Scholar
  • 13 Giggins OM, Sweeney KT, Caulfield B. Rehabilitation exercise assessment using inertial sensors: a cross-sectional analytical study. J Neuroeng Rehabil 2014; 11: 158 . Doi: 10.1186/1743-0003-11-158
    CrossrefPubMedGoogle Scholar
  • 14 O'Reilly MA, Whelan DF, Ward TE, Delahunt E, Caulfield BM. Classification of deadlift biomechanics with wearable inertial measurement units. J Biomech 2017; 58: 155-161
    CrossrefPubMedGoogle Scholar
  • 15 O'Reilly MA, Whelan DF, Ward TE, Delahunt E, Caulfield BM. Technology in strength and conditioning: assessing bodyweight squat technique with wearable sensors. J Strength Cond Res 2017; 31 (08) 2303-2312
    CrossrefPubMedGoogle Scholar
  • 16 O'Reilly MA, Slevin P, Ward T, Caulfield B. A wearable sensor-based exercise biofeedback system: mixed methods evaluation of Formulift. JMIR Mhealth Uhealth 2018; 6 (01) e33 . Doi: 10.2196/mhealth.8115
    CrossrefPubMedGoogle Scholar
  • 17 Whelan D, O'Reilly M, Huang B, Giggins O, Kechadi T, Caulfield B. Leveraging IMU data for accurate exercise performance classification and musculoskeletal injury risk screening. In: 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Orlando, FL, USA: IEEE; 2016: 659-662 . Doi: 10.1109/EMBC.2016.7590788
    PubMedGoogle Scholar
  • 18 Anton D, Berges I, Bermúdez J, Goñi A, Illarramendi A. A telerehabilitation system for the selection, evaluation and remote management of therapies. Sensors (Basel) 2018; 18 (05) E1459 . Doi: 10.3390/s18051459
    CrossrefPubMedGoogle Scholar
  • 19 Chang Y-J, Chen S-F, Huang J-D. A Kinect-based system for physical rehabilitation: a pilot study for young adults with motor disabilities. Res Dev Disabil 2011; 32 (06) 2566-2570
    CrossrefPubMedGoogle Scholar
  • 20 Zapata BC, Fernández-Alemán JL, Idri A, Toval A. Empirical studies on usability of mHealth apps: a systematic literature review. J Med Syst 2015; 39 (02) 1 . Doi: 10.1007/s10916-014-0182-2
    CrossrefPubMedGoogle Scholar
  • 21 Bergmann JHM, McGregor AH. Body-worn sensor design: what do patients and clinicians want?. Ann Biomed Eng 2011; 39 (09) 2299-2312
    CrossrefPubMedGoogle Scholar
  • 22 Crenshaw SJ, Richards JG. A method for analyzing joint symmetry and normalcy, with an application to analyzing gait. Gait Posture 2006; 24 (04) 515-521
    CrossrefPubMedGoogle Scholar
  • 23 Argent R, Slevin P, Bevilacqua A, Neligan M, Daly A, Caulfield B. Clinician perceptions of a prototype wearable exercise biofeedback system for orthopaedic rehabilitation: a qualitative exploration. BMJ Open 2018; 8 (10) e026326 . Doi: 10.1136/bmjopen-2018-026326
    CrossrefPubMedGoogle Scholar
  • 24 Nicolson PJA, Hinman RS, French SD, Lonsdale C, Bennell KL. Improving adherence to exercise: do people with knee osteoarthritis and physical therapists agree on the behavioral approaches likely to succeed?. Arthritis Care Res (Hoboken) 2018; 70 (03) 388-397
    CrossrefPubMedGoogle Scholar
  • 25 Argent R, Daly A, Caulfield B. Patient involvement with home-based exercise programs: can connected health interventions influence adherence?. JMIR Mhealth Uhealth 2018; 6 (03) e47 . Doi: 10.2196/mhealth.8518
    CrossrefPubMedGoogle Scholar
  • 26 Bassett SF. Bridging the intention-behaviour gap with behaviour change strategies for physiotherapy rehabilitation non-adherence. New Zealand J Physiother 2015; 43 (03) 105-111
    CrossrefPubMedGoogle Scholar
  • 27 Meet VERA. Our FDA-Cleared, Clinically Proven Virtual Physical Therapy Assistant. Reflexion Health. https://reflexionhealth.com/vera/ . Accessed February 2, 2020
    PubMedGoogle Scholar
  • 28 Komatireddy R, Chokshi A, Basnett J, Casale M, Goble D, Shubert T. Quality and quantity of rehabilitation exercises delivered by a 3-D motion controlled camera: a pilot study. Int J Phys Med Rehabil 2014; 2 (04) 214 . Doi: 10.4172/2329-9096.1000214
    CrossrefPubMedGoogle Scholar
  • 29 Jones SS. The development of imitation in infancy. Philos Trans R Soc Lond B Biol Sci 2009; 364 (1528): 2325-2335
    CrossrefPubMedGoogle Scholar
  • 30 Tsekleves E, Paraskevopoulos IT, Warland A, Kilbride C. Development and preliminary evaluation of a novel low cost VR-based upper limb stroke rehabilitation platform using Wii technology. Disabil Rehabil Assist Technol 2016; 11 (05) 413-422
    CrossrefPubMedGoogle Scholar
  • 31 Olson KE, O'Brien MA, Rogers WA, Charness N. Diffusion of technology: frequency of use for younger and older adults. Ageing Int 2011; 36 (01) 123-145
    CrossrefPubMedGoogle Scholar
  • 32 Vaportzis E, Clausen MG, Gow AJ. Older adults perceptions of technology and barriers to interacting with tablet computers: a focus group study. Front Psychol 2017; 8: 1687 . Doi: 10.3389/fpsyg.2017.01687
    CrossrefPubMedGoogle Scholar
  • 33 Hayden-Wade HA, Coleman KJ, Sallis JF, Armstrong C. Validation of the telephone and in-person interview versions of the 7-day PAR. Med Sci Sports Exerc 2003; 35 (05) 801-809
    CrossrefPubMedGoogle Scholar
  • 34 Sallis JF, Haskell WL, Wood PD. , et al. Physical activity assessment methodology in the Five-City project. Am J Epidemiol 1985; 121 (01) 91-106
    CrossrefPubMedGoogle Scholar