Abstract
Telerehabilitation is a growing alternative to traditional face-to-face therapy, which uses technological solutions to cover rehabilitation care in both clinical centers and in-home programs. However, the current telerehabilitation systems are limited to deliver a set of exercise programs for some specific locomotor disability, without including tools that allow a quantitative analysis of the rehabilitation progress, in real-time, as well as the medical condition of patients. This paper presents the design and development of a novel web-based platform, named “Kushkalla”, that allows to perform movement assessment for creating personalized home-based therapy routines, integrating hardware and software tools for a quantitative analysis of locomotor movements based on motion capture, preprocessing, monitoring, visualization, storage and analysis, in real-time. The platform combines two motion capture strategies, the Kinect-based and IMU-based motion capture. In addition, a set of 2D and 3D graphical models, virtual environments, based on WebGL technology, and videoconference module are included to allow the interaction between user and clinician for enhancing the capability of the clinician to direct rehabilitation therapies.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Atrsaei, A., Salarieh, H., Alasty, A.: Human arm motion tracking by orientation-based fusion of inertial sensors and kinect using unscented kalman filter. J. Biomech. Eng. 138(9), 091 (2016)
Barriga, A., Conejero, J.M., Hernández, J., Jurado, E., Moguel, E., Sánchez-Figueroa, F.: A vision-based approach for building telecare and telerehabilitation services. Sensors 16(10), 1724 (2016)
Bernard, M.M., Janson, F., Flora, P.K., Faulkner, G.E., Meunier-Norman, L., Fruhwirth, M.: Videoconference-based physiotherapy and tele-assessment for homebound older adults: a pilot study. Activities, Adaptat. Aging 33(1), 39–48 (2009)
Callejas-Cuervo, M., Díaz, G.M., Ruíz-Olaya, A.F.: Integration of emerging motion capture technologies and videogames for human upper-limb telerehabilitation: a systematic review. Dyna 82(189), 68–75 (2015)
Clark, P.G., Dawson, S.J., Scheideman-Miller, C., Post, M.L.: Telerehab: stroke teletherapy and management using two-way interactive video. J. Neurol. Phys. Ther. 26(2), 87–93 (2002)
Da Gama, A., Fallavollita, P., Teichrieb, V., Navab, N.: Motor rehabilitation using kinect: a systematic review. Games Health J. 4(2), 123–135 (2015)
Díaz, I., Gil, J.J., Sánchez, E.: Lower-limb robotic rehabilitation: literature review and challenges. J. Robot. 2011(i), 1–11 (2011). doi:10.1155/2011/759764
DuBois, P.: MySQL Cookbook: Solutions for Database Developers and Administrators. O’Reilly Media Inc., Sebastopol (2014)
Edward, S.G., Sabharwal, N.: MongoDB architecture. Practical MongoDB, pp. 95–157. Apress, Berkeley, CA (2015). doi:10.1007/978-1-4842-0647-8_7
Helten, T., Muller, M., Seidel, H.P., Theobalt, C.: Real-time body tracking with one depth camera and inertial sensors. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1105–1112 (2013). doi:10.1109/ICCV.2013.141
Joukov, V., Karg, M., Kulic, D.: Online tracking of the lower body joint angles using IMUs for gait rehabilitation. In: Conference Proceedings of Annual International Conference on the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference 2014, pp. 2310–2313 (2014). doi:10.1109/EMBC.2014.6944082
Kurillo, G., Koritnik, T., Bajd, T., Bajcsy, R.: Real-time 3D avatars for tele-rehabilitation in virtual reality. Stud. Health Technol. Inf. 163, 290–296 (2011). doi:10.3233/978-1-60750-706-2-290
Laudanski, A., Brouwer, B., Li, Q.: Measurement of lower limb joint kinematics using inertial sensors during stair ascent and descent in healthy older adults and stroke survivors. J. Healthc. Eng. 4(4), 555–576 (2013). doi:10.1260/2040-2295.4.4.555
Moffet, H., Tousignant, M., Nadeau, S., Merette, C., Boissy, P., Corriveau, H., Marquis, F., Cabana, F., Ranger, P., Belzile, E.L., Dimentberg, R.: In-home telerehabilitation compared with face-to-face rehabilitation after total knee arthroplasty: a noninferiority randomized controlled trial. J. Bone Joint Surg. 97(14), 1129–1141 (2015). doi:10.2106/JBJS.N.01066
Muñoz-Cardona, J.E., Henao-Gallo, O.A., López-Herrera, J.F.: Sistema de rehabilitación basado en el uso de análisis biomecánico y videojuegos mediante el sensor kinect. Tecno Lógicas (2013)
Natis, Y., Schulte, R.: Introduction to service-oriented architecture. Gartner Group, 14 April 2003
World Health Organization, et al.: World report on disability. World Health Organization (2011)
Roetenberg, D., Luinge, H., Slycke, P.: Xsens MVN: full 6DOF human motion tracking using miniature inertial sensors. Xsens Technologies White Paper, 1–7, January 2009. (2013)
Sosa, G.D., Sanchez, J., Francoy, H.: Improved front-view tracking of human skeleton from Kinect data for rehabilitation support in multiple sclerosis. In: Conference Proceedings of the 2015 20th Symposium on Signal Processing, Images and Computer Vision, STSIVA 2015 (2015). doi:10.1109/STSIVA.2015.7330422
Spasojević, S., Ilić, T., Milanović, S., Potkonjak, V., Rodić, A., Santos-Victor, J., et al.: Combined vision and wearable sensors-based system for movement analysis in rehabilitation. Methods Inf. Med. 56(2), 95–111 (2017)
Tanaka, K., Parker, J., Baradoy, G., Sheehan, D., Holash, J.R., Katz, L.: A comparison of exergaming interfaces for use in rehabilitation programs and research. Loading 6(9), 69–81 (2012)
Tian, Y., Meng, X., Tao, D., Liu, D., Feng, C.: Upper limb motion tracking with the integration of imu and kinect. Neurocomputing 159, 207–218 (2015)
Vargas-Valencia, L., Elias, A., Rocon, E., Bastos-Filho, T., Frizera, A.: An IMU-to-body alignment method applied to human gait analysis. Sensors 16(12), 2090 (2016). doi:10.3390/s16122090
Wang, L., Zhang, Z., Sun, P.: Quaternion-based Kalman Filter for AHRS using an adaptive-step gradient descent algorithm. Int. J. Adv. Rob. Syst. 12(9), 1–12 (2015). doi:10.5772/61313
Wu, G., Van Der Helm, F.C.T., Veeger, H.E.J., Makhsous, M., Van Roy, P., Anglin, C., Nagels, J., Karduna, A.R., McQuade, K., Wang, X., Werner, F.W., Buchholz, B.: ISB recommendation on definitions of joint coordinate systems of various joints for the reporting of human joint motion - Part II: shoulder, elbow, wrist and hand. J. Biomech. 38(5), 981–992 (2005). doi:10.1016/j.jbiomech.2004.05.042
Zhang, J., Novak, A.C., Brouwer, B., Li, Q.: Concurrent validation of Xsens MVN measurement of lower limb joint angular kinematics. Physiol. Meas. 34(8), N63–N69 (2013). doi:10.1088/0967-3334/34/8/N63
Acknowledgment
This work was partially funded by the Ecuadorian Consortium for Advanced Internet Development (CEDIA) through the CEPRA projects. Specifically, under grants CEPRA-X-2016 project; “Tele-rehabilitation platform for elderly with dementia disorders, based on emerging technologies”. [Grant number: X-2016-02].
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Narváez, F., Arbito, F., Luna, C., Merchán, C., Cuenca, M.C., Díaz, G.M. (2017). Kushkalla: A Web-Based Platform to Improve Functional Movement Rehabilitation. In: Valencia-García, R., Lagos-Ortiz, K., Alcaraz-Mármol, G., Del Cioppo, J., Vera-Lucio, N., Bucaram-Leverone, M. (eds) Technologies and Innovation. CITI 2017. Communications in Computer and Information Science, vol 749. Springer, Cham. https://doi.org/10.1007/978-3-319-67283-0_15
Download citation
DOI: https://doi.org/10.1007/978-3-319-67283-0_15
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-67282-3
Online ISBN: 978-3-319-67283-0
eBook Packages: Computer ScienceComputer Science (R0)