Abstract
In the following decades, the European population will be steadily growing older, which causes serious problems, especially with regard to the health sector. Problems are further aggravated by the lack of personnel resources. Even now, the number of therapists is not sufficient to supervise the increasing number of patients during their rehabilitation process. At this point, technical systems can support both patients and therapists in order to ensure the quality of rehabilitation. In this study, we review recent developments in the field of feedback-based therapy systems and identify needs that have not been satisfied thus far. On the basis of these findings, we introduce a technical system for assisted motion control in real therapy applications and discuss possible solutions in order to encounter current deficits. We thereby address aspects, such as sensor technologies, approaches for capturing and matching human motions, the grade of muscular stress, user-specific system parametrization, the way of delivering feedback and user-friendly interfaces for feedback and therapy evaluation. The presented system could contribute to a more efficient therapy, because it can supervise patients when the therapist is not present.
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Bartuzi, P., Roman-Liu, D., Wiśniewski, T.: The influence of fatigue on muscle temperature. Int. J. Occup. Saf. Ergon. 18(2), 233–243 (2012)
Bregler, C., Malik, J.: Tracking people with twists and exponential maps. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1998. Proceedings, pp. 8–15. IEEE (1998)
Brubaker, M.A., Fleet, D.J., Hertzmann, A.: Physics-based person tracking using the anthropomorphic walker. Int. J. Comput. Vis. 87(1–2), 140–155 (2010)
Brudny, J., Korein, J., Grynbaum, B.B., Friedmann, L.W., Weinstein, S., Sachs-Frankel, G., Belandres, P.V.: EMG feedback therapy: review of treatment of 114 patients. Arch. Phys. Med. Rehabil. 57(2), 55–61 (1976)
Caterisano, A., Moss, R.E., Pellinger, T.K., Woodruff, K., Lewis, V.C., Booth, W., Khadra, T.: The effect of back squat depth on the EMG activity of 4 superficial hip and thigh muscles. J. Strength Cond. Res. 16(3), 428–432 (2002)
Fröhlich, M., Ludwig, O., Zeller, P., Felder, H.: Changes in skin surface temperature after a 10-minute warm-up on a bike ergometer. Int. J. Kinesiol. Sports Sci. 3(3), 13–17 (2015)
Gal, N., Andrei, D., Nemeş, D., Nădăşan, E., Stoicu-Tivadar, V.: A kinect based intelligent e-rehabilitation system in physical therapy. In: Digital Healthcare Empowering Europeans, pp. 489–493 (2015)
Ganapathi, V., Plagemann, C., Koller, D., Thrun, S.: Real time motion capture using a single time-of-flight camera. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2010, pp. 755–762. IEEE (2010)
Gorsuch, J., Long, J., Miller, K., Primeau, K., Rutledge, S., Sossong, A., Durocher, J.J.: The effect of squat depth on multiarticular muscle activation in collegiate cross-country runners. J. Strength Cond. Res. 27(9):2619–2625 (2013)
Grest, D., Woetzel, J., Koch, R.: Nonlinear body pose estimation from depth images. In: Pattern Recognition, pp. 285–292. Springer (2005)
Hamman, R.G., Mekjavic, I., Mallinson, A.I., Longridge, N.S.: Training effects during repeated therapy sessions of balance training using visual feedback. Arch. Phys. Med. Rehabil. 72(8), 738–744 (1992)
Huang, H.Y., Chang, S.H.: A skeleton-occluded repair method from kinect. In: International Symposium on Computer, Consumer and Control (IS3C), 2014, pp. 264–267. IEEE (2014)
Huang, T.-C., Cheng, Y.-C., Chiang, C.-C.: Automatic dancing assessment using kinect. In: Advances in Intelligent Systems and Applications, Vol. 2, pp. 511–520. Springer (2013)
Kang, S.-Y., Choung, S.-D., Jeon, H.-S.: Modifying the hip abduction angle during bridging exercise can facilitate gluteus maximus activity. Man. Ther. (2016)
Khan, N.M., Lin, S., Guan, L., Guo, B.: A visual evaluation framework for in-home physical rehabilitation. In: 2014 IEEE International Symposium on Multimedia (ISM), pp. 237–240. IEEE (2014)
Krug, J., Herrmann, H., Naundorf, F., Panzer, S., Wagner, K.: Messplatztraining: Konzepte, Entwicklungsstand und Ausblick. Messplatztraining, pp. 13–27 (2004)
Kyan, M., Sun, G., Li, H., Zhong, L., Muneesawang, P., Dong, N., Elder, B., Guan, L.: An approach to ballet dance training through MS kinect and visualization in a CAVE virtual reality environment. ACM Trans. Intell. Syst. Technol. 6(2), 23:1–23:37 (2015)
Liu, Z., Zhu, J., Bu, J., Chen, C.: A survey of human pose estimation: the body parts parsing based methods. J. Vis. Commun. Image Rep. 32, 10–19 (2015)
Nakagawa, W., Matsumoto, K., de Sorbier, F., Sugimoto, M., Saito, H., Senda, S., Shibata, T., Iketani, A.: Visualization of temperature change using RGB-D camera and thermal camera. In: Computer Vision-ECCV 2014 Workshops, pp. 386–400. Springer (2014)
Sakamoto, A., Sinclair, P.J.: Muscle activations under varying lifting speeds and intensities during bench press. Eur. J. Appl. Physiol. 112(3), 1015–1025 (2012)
Shotton, J., Girshick, R., Fitzgibbon, A., Sharp, T., Cook, M., Finocchio, M., Moore, R., Kohli, P., Criminisi, A., Kipman, A., et al.: Efficient human pose estimation from single depth images. IEEE Trans. Pattern Anal. Mach. Intell. 35(12), 2821–2840 (2013)
Shotton, J., Sharp, T., Kipman, A., Fitzgibbon, A., Finocchio, M., Blake, A., Cook, M., Moore, R.: Real-time human pose recognition in parts from single depth images. Commun. ACM 56(1), 116–124 (2013)
Shum, H.P.H., Ho, E.S.L., Jiang, Y., Takagi, S.: Real-time posture reconstruction for microsoft kinect. IEEE Trans. Cybern. 43(5), 1357–1369 (2013)
Sigal, L.: Human pose estimation. In: Computer Vision, pp. 362–370. Springer (2014)
Sigal, L., Bhatia, S., Roth, S., Black, M.J., Isard, M.: Tracking loose-limbed people. In Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004, vol. 1, pp. I–421. IEEE (2004)
Su, C.-J., Chiang, C.-Y., Huang, J.-Y.: Kinect-enabled home-based rehabilitation system using dynamic time warping and fuzzy logic. Appl. Soft Comput. 22, 652–666 (2014)
Tak, Y.-S., Rho, S., Hwang, E.: Motion sequence-based human abnormality detection scheme for smart spaces. Wirel. Pers. Commun. 60(3), 507–519 (2011)
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This project is funded by the European Social Fund (ESF).
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Richter, J. et al. (2017). Assisted Motion Control in Therapy Environments Using Smart Sensor Technology: Challenges and Opportunities. In: Wichert, R., Mand, B. (eds) Ambient Assisted Living. Advanced Technologies and Societal Change. Springer, Cham. https://doi.org/10.1007/978-3-319-52322-4_8
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DOI: https://doi.org/10.1007/978-3-319-52322-4_8
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