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
› Institutsangaben
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.

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.

Supplementary Material



Publikationsverlauf

Eingereicht: 30. Oktober 2019

Angenommen: 02. März 2020

Artikel online veröffentlicht:
14. Juni 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
  • 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
  • 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
  • 4 Koldenhoven RM, Hertel J. Validation of a wearable sensor for measuring running biomechanics. Digit Biomark 2018; 2 (02) 74-78
  • 5 Bandura A. Self-efficacy: toward a unifying theory of behavioral change. Psychol Rev 1977; 84 (02) 191-215
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 21 Bergmann JHM, McGregor AH. Body-worn sensor design: what do patients and clinicians want?. Ann Biomed Eng 2011; 39 (09) 2299-2312
  • 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
  • 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
  • 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
  • 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
  • 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
  • 27 Meet VERA. Our FDA-Cleared, Clinically Proven Virtual Physical Therapy Assistant. Reflexion Health. https://reflexionhealth.com/vera/ . Accessed February 2, 2020
  • 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
  • 29 Jones SS. The development of imitation in infancy. Philos Trans R Soc Lond B Biol Sci 2009; 364 (1528): 2325-2335
  • 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
  • 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
  • 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
  • 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
  • 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