Osteoarthritis is the most common type of degenerative joint disease and affects millions of people. In this paper, we propose a non-obtrusive and straightforward method to assess the progression of osteoarthritis. In standard medicine praxis, osteoarthritis is observed with X-rays. In this study, we use widely available wearable sensors with gyroscopes to make the observation. Two novel methods are proposed for gyroscope data processing. A small-scale study has shown that these methods can be used to monitor osteoarthritis's progression, and to differentiate between healthy subjects and subjects with femoroacetabular impingement syndrome.

Centre of Advanced Technologies Faculty of Science University of Hradec Králové Rokitanského 62 500 03 Hradec Králové Czech Republic

Department of Physics Faculty of Science University of Hradec Králové Rokitanského 62 500 03 Hradec Králové Czech Republic

Faculty of Medicine in Hradec Kralove Charles University Šimkova 870 500 03 Hradec Králové Czech Republic

See more in PubMed

Asimakopoulos S, Asimakopoulos G, Spillers F. Motivation and user engagement in fitness tracking: heuristics for mobile healthcare wearables. In: Informatics. Multidisciplinary Digital Publishing Institute, 2017. p. 5.

Byrd JW. Femoroacetabular impingement in athletes: current concepts. Am J Sports Med. 2014;42(3):737–751. doi: 10.1177/0363546513499136. PubMed DOI

Cimr D, et al. Application of mechanical trigger for unobtrusive detection of respiratory disorders from body recoil micro-movements. Comput Methods Programs Biomed. 2021;207:106149. doi: 10.1016/j.cmpb.2021.106149. PubMed DOI

Dooley PJ. Femoroacetabular impingement syndrome: nonarthritic hip pain in young adults. Can Fam Phys. 2008;54(1):42–47. PubMed PMC

Ermier C, Hatcher J, Lee M. Wearable device implications in the healthcare industry. J Med Eng Technol. 2016;40(4):141–148. doi: 10.3109/03091902.2016.1153738. PubMed DOI

Fioruzzi A, et al. Interobserver and intraobserver reliability of a new radiological classification for femoroacetabular impingement syndrome. Musculoskeletal Surg. 2020;104(3):279–284. PubMed

Glyn-Jones S, et al. Osteoarthritis. The Lancet. 2015;386(9991):376–387. doi: 10.1016/S0140-6736(14)60802-3. PubMed DOI

Griffin DR, Dickenson EJ, O'Donnel J, et al. The Warwick Agreement on femoroacetabular impingement syndrome (FAI syndrome): an international consensus statement. Br J Sports Med. 2016;50(19):1169–1176. doi: 10.1136/bjsports-2016-096743. PubMed DOI

Hlubik J, Stritecka H, Hlubik P. Bioelectrical impedance analysis or basic anthropometrical parameters for evaluating weight loss success? Open Med. 2013;8(5):565–570. doi: 10.2478/s11536-013-0206-1. DOI

Kaiser DW, Harrington RA, Turakhina MP. Wearable fitness trackers and heart disease. JAMA Cardiol. 2016;1(2):239–239. doi: 10.1001/jamacardio.2016.0354. PubMed DOI PMC

Kiebzak GM, Leamy LJ, Pierson LM, Nord RH, Zhang ZY. Measurement precision of body composition variables using the lunar DPX-L densitometer. J Clin Densitometry. 2000;3(1):35–41. doi: 10.1385/JCD:3:1:035. PubMed DOI

King MG, et al. Lower limb biomechanics in femoroacetabular impingement syndrome: a systematic review and meta-analysis. Br J Sports Med. 2018;52(9):566–580. doi: 10.1136/bjsports-2017-097839. PubMed DOI

Lee TF, et al. Analysis of vibroarthrographic signals for knee osteoarthritis diagnosis. In: 2012 Sixth International Conference on Genetic and Evolutionary Computing. IEEE, 2012. p. 223–228.

Lee WY, et al. Descriptive epidemiology of symptomatic femoroacetabular impingement in young athlete: single center study. Hip Pelvis. 2016;28(1):29–34. doi: 10.5371/hp.2016.28.1.29. PubMed DOI PMC

Medina-Inojosa J, et al. Reliability of a 3D body scanner for anthropometric measurements of central obesity. Obesity, open access, 2016, 2.3. PubMed PMC

Rasche P, et al. Activity tracker and elderly. In: 2015 IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing. IEEE, 2015. p. 1411–1416.

Rasche P, et al. Self monitoring-an age-related comparison. In: Proceedings of the Human Factors and Ergonomics Society Europe Chapter 2015 Annual Conference. 2015.

Sudnicka F. Analysis of biomedical signals using differential geometry invariants. Acta Physica Polonica A, 2011, 6.120: A-154–A-157.

Stukenberg E. A quantitative pilot study on the use of a fitness tracker in the preventative management of employees at risk of chronic disease in a health care facility. On-Line J Nurs Inf, 2015, 19.3.

Swagerty JR, Daniel L, Hellinger D. Radiographic assessment of osteoarthritis. Am Fam Phys. 2001;64(2):279. PubMed

Tedesco S, Barton J, O’Flynn B. A review of activity trackers for senior citizens: research perspectives, commercial landscape and the role of the insurance industry. Sensors. 2017;17(6):1277. doi: 10.3390/s17061277. PubMed DOI PMC

Wang X, et al. An empirical study of wearable technology acceptance in healthcare. Industrial Management & Data Systems, 2015.