Wearable sensor-based rehabilitation exercise assessment for knee osteoarthritis

Sensors (Switzerland)

Volumn 15, Issue 2, 2015, Pages 4193-4211

  Chen, Kun Hui ^a,b   Chen, Po Chao ^a   Liu, Kai Chun ^a   Chan, Chia Tai ^a  

^a NATIONAL YANG MING UNIVERSITY   (Taiwan)

^b TAICHUNG VETERANS GENERAL HOSPITAL   (Taiwan)

Author keywords

Knee joint rehabilitation exercise; Rehabilitation assessment system; Wearable sensor

Indexed keywords

CLASSIFICATION (OF INFORMATION); FREQUENCY DOMAIN ANALYSIS; JOINTS (ANATOMY); PATIENT TREATMENT; TIME DOMAIN ANALYSIS; WEARABLE SENSORS;

10-FOLD CROSS-VALIDATION; ASSESSMENT MECHANISM; ASSESSMENT SYSTEM; KNEE OSTEOARTHRITIS; RECOGNITION ACCURACY; REHABILITATION ACTIVITIES; REHABILITATION EXERCISE; TYPE CLASSIFICATIONS;

PATIENT REHABILITATION;

HUMAN; KINESIOTHERAPY; KNEE; OSTEOARTHRITIS, KNEE; PATHOPHYSIOLOGY; PHYSIOLOGY; QUADRICEPS FEMORIS MUSCLE; REMOTE SENSING; TELEMEDICINE;

EXERCISE THERAPY; HUMANS; KNEE JOINT; OSTEOARTHRITIS, KNEE; QUADRICEPS MUSCLE; REMOTE SENSING TECHNOLOGY; TELEMEDICINE;

EID: 84922893295     PISSN: 14248220     EISSN: None     Source Type: Journal    
DOI: 10.3390/s150204193     Document Type: Article

References (33)

1. Mangine, R.E.; Price, S. Innovative Approaches to Surgery and Rehabilitation. In Physical Therapy of the Knee; Mangine, R.E., Ed.; Churchill Livingstone: New York, NY, USA, 1988; pp. 191–220.
2. United States Census, the 2012 Statistical Abstract, the National Data Book, Resident Population by Sex and Age. Available online: http://www.census.gov/compendia/statab/cats/population.html (accessed on 21 September 2014).
3. Haq, I.; Murphy, E.; Dacre, J. Osteoarthritis. Postgrad. Med. J. 2003, 79, 377–383.
4. Bennell, K.L.; Kyriakides, M.; Metcalf, B.; Egerton, T.; Wrigley, T.V.; Hodges, P.W.; Hunt, M.A.; Roos, E.M.; Forbes, A.; Ageberg, E.; et al. Neuromuscular Versus Quadriceps Strengthening Exercise in Patients With Medial Knee Osteoarthritis and Varus Malalignment. Arthritis Rheumatol. 2014, 66, 950–959.
5. Taylor, P.E.; Almeida, G.J.M.; Kanade, T.; Hodgins, J.K. Classifying Human Motion Quality for Knee Osteoarthritis Using Accelerometers. In Proceeding of the 32nd Annual International Conference of the IEEE EMBS, Buenos Aires, Argentina, 31 August 2010; pp. 339–343.
6. Mathie, M.J.; Coster, A.C.F.; Lovell, N.H.; Celler, B.G. Accelerometry: Providing an Integrated, Practical Method for Long-Term, Ambulatory Monitoring of Human Movement. Physiol. Meas. 2004, 25, R1–R20.
7. Preece, S.J.; Goulermas, J.Y.; Kenney, L.P.J.; Howard, D.; Meijer, K.; Crompton, R. Activity Identification Using Body-Mounted Sensors—A Review of Classification Techniques. Physiol. Meas. 2009, 30, R1–R33.
8. Karantonis, D.M.; Narayanan, M.R.; Lovell, N.H.; Celler, B.G. Implementation of a Real-Time Human Movement Classifier Using a Triaxial Accelerometer for Ambulatory Monitoring. IEEE Trans. Inf. Technol. Biomed. 2006, 1, 156–167.
9. Ermes, M.; Pärkka, J.; Mantyjarvi, J.; Korhonen, I. Detection of Daily Activities and Sports with Wearable Sensors in Controlled and Uncontrolled Conditions. IEEE Trans. Inf. Technol. Biomed. 2008, 1, 20–26.
10. Yang, M.; Zheng, H.; Wang, H.; McClean, S.; Hall, J.; Harris, N. A Machine Learning Approach to Assessing Gait Patterns for Complex Regional Pain Syndrome. Med. Eng. Phys. 2011, 6, 740–746.
11. Woodman, O.J. An Introduction to Inertial Navigation; Technical Report, Number 696, ISSN 1476-2986; University of Cambridge, Computer Laboratory: Cambridge, UK, 2007.
12. Luinge, H.J.; Veltink, P.H. Measuring orientation of human body segments using miniature gyroscopes and accelerometers. Med. Biol. Eng. Comput. 2005, 43, 273–282.
13. Fahrenberg, J.; Foerster, F.; Smeja, M.; Muller, W. Assessment of posture and motion by multichannel piezoresistive accelerometer recordings. Psychophysiology 1997, 5, 607–612.
14. Fahrenberg, J.; Muller, W.; Foerster, F.; Smeja, M. A multi-channel investigation of physical activity. Psychophysiology 1996, 3, 209–217.
15. Duda, R.O.; Hart, P.E.; Stork, D.G. Pattern Classification, 2nd ed.; Wiley: New York, NY, USA, 2001.
16. Theodoridis, S.; Koutroumbas, K. Pattern Recognition, 3rd ed.; Elsevier: San Diego, CA, USA, 2006.
17. Maurer, U.; Rowe, A.; Smailagic, A.; Siewiorek, D. Location and activity recognition using eWatch: A wearable sensor platform. In Ambient Intelligence in Everday Life; Lecture Notes in Computer Science; Cai, Y., Abascal, J., Eds.; Springer: Berlin, Germany, 2006; pp. 86–102.
18. Ravi, N.; Dikhil, N.; Mysore, P.; Littman, M.L. Activity recognition from accelerometer data. In Proceedings of the 7th Innovative Applications of Artificial Intelligence Conference, Pittsburgh, PA, USA, 9 July 2005; pp. 1541–1546.
19. Foerster, F.; Smeja, M.; Fahrenberg, J. Detection of posture and motion by accelerometry: A validation study inambulatory monitoring. Comp. Hum. Behav. 1999, 15, 571–583.
20. Foerster, F.; Fahrenberg, F. Motion pattern and posture: correctly assessed by calibrated accelerometers. Behav. Res. Methods Instrum. Comput. 2000, 3, 450–457.
21. Godfrey, A.; Conway, R.; Meagher, D.; Olaighin, G. Direct measurement of human movement by accelerometry. Med. Eng. Phys. 2008, 10, 1364–1386.
22. Quinlan, J.R. Improved use of continuous attributes in c4.5. J. Artif. Intell. Res. 1996, 1, 77–90.
23. Bao, L.; Intille, S.S. Activity recognition from user-annotated acceleration data. In Pervasive Computing; Lecture Notes in Computer Science; Ferscha, A., Mattern, F., Eds.; Srpinger: Linz/Vienna, Austria, 2004; Volume 3001, pp. 1–17.
24. Shull, P.B.; Jirattigalachote, W.; Hunt, M.A.; Cutkosky, M.R.; Scott, L. Delp. Quantified self and human movement: A review on the clinical impact of wearable sensing and feedback for gait analysis and intervention. Gait Posture 2014, 40, 11–19.
25. Hester, T.; Hughes, R.; Sherrill, D.M.; Knorr, B.; Akay, M.; Stein, J.; Bonato, P. Using wearable sensors to measure motor abilities following stroke. In Proceedings of the International Workshop on Wearable and Implantable Body Sensor Networks, Cambridge, MA, USA, 3–5 April 2006; pp. 4–8.
26. Brutovsky, J.; Novak, D. Low-cost motivated rehabilitation system for post-operation exercises. In Proceeding of the 28th Annual International Conference of the IEEE Engineering in Medicine and Biology, New York, NY, USA, 30 August–3 September 2006; pp. 6663–6666.
27. Tseng, Y.C.; Wu, C.H.; Wu, F.J.; Huang, C.F.; King, C.T.; Lin, C.Y.; Sheu, J.P.; Chen, C.Y.; Lo, C.Y.; Yang, C.W.; et al. A wireless human motion capturing system for home rehabilitation. In Proceeding of the International Conference of Mobile Data Management, Taipei, Taiwan, 18–20 May 2009; pp. 359–360.
28. Yeh, S.C.; Hwang, W.Y.; Huang, T.C.; Liu, W.K.; Chen, Y.T.; Hung, Y.P. A study for the application of body sensing in assisted rehabilitation training. In Proceeding of the IEEE International Symposium on Computer, Consumer and Contro, Taichung, Taiwan, 4–6 June 2012; pp. 922–925.
29. Cordella, F.; Corato, F.; Zollo, L.; Siciliano, B.; Smagt, P. Patient performance evaluation using Kinect and Monte Carlo-Based finger tracking. In Proceeding of the IEEE International Conference on Biomedical Robotics and Biomechatronics, Rome, Italy, 24–27 June 2012; pp.1967–1972.
30. Clark, R.A.; Pua, Y.H.; Fortin, K.; Ritchie, C.; Webster, K.E.; Denehy, L.; Bryant, A.L. Validity of the Microsoft Kinect for assessment of postural control. Gate Posture 2012, 3, 372–377.
31. Hingtgen, B.A.; McGuire, J.R.; Wang, M.; Harris, G.F. Design and validation of an upper extremity kinematic model for application in stroke rehabilitation. In Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Cancun, Mexico, 17–21 September 2003; pp. 1682–1685.
32. Zhou, H.; Hu, H. Human motion tracking for rehabilitation—A survey. Biomed. Signal Process. Control 2008, 1, 1–18.
33. Takeda, R.; Tadano, S.; Natorigawa, A.; Todoh, M.; Yoshinari, S. Gait posture estimation using wearable acceleration and gyro sensors. J. Biomech. 2009, 42, 2486–2494.