Biofeedback from physical rehabilitation exercises has proved to lead to faster recovery, better outcomes, and increased patient motivation. In addition, it allows the physical rehabilitation processes carried out at the clinic to be complemented with exercises performed at home. However, currently existing approaches rely mostly on audio and visual reinforcement cues, usually presented to the user on a computer screen or a mobile phone interface. Some users, such as elderly people, can experience difficulties to use and understand these interfaces, leading to non-compliance with the rehabilitation exercises. To overcome this barrier, latest biosignal technologies can be used to enhance the efficacy of the biofeedback, decreasing the complexity of the user interface. In this paper we propose and validate a context-aware framework for the use of animatronic biofeedback, as a way of potentially increasing the compliance of elderly users with physical rehabilitation exercises performed at home. In the scope of our work, animatronic biofeedback entails the use of pre-programmed actions on a robot that are triggered in response to certain changes detected in the users biomechanical or electrophysiological signals. We use electromyographic and accelerometer signals, collected in real time, to monitor the performance of the user while executing the exercises, and a mobile robot to provide animatronic reinforcement cues associated with their correct or incorrect execution. A context-aware application running on a smartphone aggregates the sensor data and controls the animatronic feedback. The acceptability of the animatronic biofeedback has been tested on a set of volunteer elderly users, and results suggest that the participants found the animatronic feedback engaging and of added value.
This is a preview of subscription content, access via your institution.
Buy single article
Instant access to the full article PDF.
Tax calculation will be finalised during checkout.
Subscribe to journal
Immediate online access to all issues from 2019. Subscription will auto renew annually.
Tax calculation will be finalised during checkout.
European Agency for Safety and Health at Work, Musculoskeletal disorders. https://osha.europa.eu/en/themes/musculoskeletal-disorders Accessed 16 August 2015.
World Health Organization (2011). World Report on Disability 2011.
Giggins, O. M., Persson, U. M., and Caulfield, B., Biofeedback in rehabilitation. J. Neuroeng. Rehabil. 10(60), 2013.
Schwartz, M. S., and Andrasik, F., Biofeedback: a practitioner’s guide. The Guilford Press, 2005.
Koh, C. E., Young, C. J., Young, J. M., and Solomon, M. J., Systematic review of randomized controlled trials of the effectiveness of biofeedback for pelvic floor dysfunction. Br. J. Surg. 95(9):1079–1087, 2008.
Bo, K., Berghmans, B., Morkved, S., and Kampen, M., Evidence-Based physical therapy for the pelvic floor: bridging science and clinical practice. Elsevier, 2007.
Coulter, C. L., Scarvell, J. M., Neeman, T. M., and Smith, P. N., Physiotherapist-directed rehabilitation exercises in the outpatient or home setting improve strength, gait speed and cadence after elective total hip replacement: a systematic review. J. Physiother. 59(4):219–226, 2013.
Rao, S. S. C., Biofeedback therapy for constipation in adults. Best Pract. Res. Clin. Gastroenterol. 25(1):159–166, 2011.
Petrofsky, J. S., The use of electromyogram biofeedback to reduce Trendelenburg gait. Eur. J. Appl. Physiol. 85(5):491–495, 2001.
Chandra, H., Oakley, I., and Silva, H., User needs in the performance of prescribed home exercise therapy. In: Proceedings of CHI ’12, pp. 2369–2374, 2012.
Liu, Y., and Quian, G., Projector-Camera guided fast environment restoration of a biofeedback system for rehabilitation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–2, 2007.
Lange, B., Chang, C. Y., Suma, E., Newman, B., Rizzo, A. S., and Bolas, M., Development and evaluation of low cost game-based balance rehabilitation tool using the Microsoft Kinect sensor. In: Proceedings of the IEEE Eng Med Biol Soc Conf, pp. 1831–1834, 2007.
Daponte, P., de Vito, L., and Sementa, C., Validation of a home rehabilitation system for Range of Motion measurements of limb functions. In: Proceedings of IEEE Int. Symp. on Medical Measurement and Applications (MeMeA), pp. 288–293, 2013.
Aung, Y. M., and Al-Jumaily, A., Shoulder rehabilitation with biofeedback simulation. In: Proceedings of the International Conference on Mechatronics and Automation, pp. 974–979, 2012.
Farjadian, A. B., Sivak, M. L., and Mavroidis, C., SQUID: sensorized shirt with smartphone interface for exercise monitoring and home rehabilitation. In: Proceedings of the IEEE Int’l Conf. on Rehabilitation Robotics (ICORR), pp. 1–6, 2013.
Daponte, P., de Vito, L., Pavic, B., and Silva, H., Case study on muscle activation analysis in post-stroke rehabilitation patients. In: Proceedings of IEEE Int. Symp. on Medical Measurement and Applications (MeMeA), pp. 360–365, 2011.
Thought Technology Ltd, Comercial device list. http://thoughttechnology.com/index.php/hardware.html Accessed 14 July 2015.
MindMedia Neuro and Biofeedback Systems. http://www.mindmedia.info/CMS2014/ Accessed 14 July 2015.
PLUX, Physioplux. http://www.physioplux.com/ Accessed 14 July 2015.
Sugar, T. G., He, J., Koeneman, E. J., Koeneman, J. B., Herman, R., Huang, H., Schultz, R. S., Herring, D. E., Wanberg, J., Balasubramanian, S., Swenson, P., and Ward, J. A., Design and control of RUPERT: a device for robotic upper extremity repetitive therapy. IEEE Trans. Neural Syst. Rehabil. Eng. 15(3): 336–46, 2007.
Lunenburger, L., Wellner, M., Banz, R., and Colombo, G., Combining immersive virtual environments with robot-aided gait training. In: Proceedings of the IEEE Conference on Rehabilitation Robotics, pp. 421–424, 2007.
Monaco, V., Galardi, G., Coscia, M., and Martelli, D., Design and evaluation of NEUROBike: a neurorehabilitative platform for bedridden post-stroke patients. IEEE Trans. Neural Syst. Rehabil. Eng. 20(6):845–852, 2012.
Shull, P. B., Silder, A., Shultz, R., Dragoo, J. L., Besier, T. F., Delp, S. L., and Cutkosky, M. R., Six-week gait retraining program reduces knee adduction moment, reduces pain, and improves function for individuals with medial compartment knee osteoarthritis. J. Orthop. Res. 31(7):1020–1025, 2013.
Casellato, C., Pedrocchi, A., Zorzi, G., Vernisse, L., Ferrigno, G., and Nardocci, N., EMG-based visual-haptic biofeedback: a tool to improve motor control in children with primary dystonia. IEEE Trans. Neural Syst. Rehabil. Eng. 21(3): 474–480, 2013.
Banerji, S., Heng J., Ponvignesh, P. S., and Menezes, D. D., Augmenting rehabilitation after stroke: a flexible platform for combining multi-channel biofeedback with FES. Converging Clinical and Engineering Research on Neurorehabilitation Biosystems and Biorobotics, pp. 259–263, 2013.
Ribeiro, A. F., Lopes, G., Pereira, N., Cruz, J., and Costa, M. F. M., Bot’n roll robotic kit as a learning tool for youngsters. In: Proceedings of the 9th International Conference on Hands on Science (HSCI’2012), p. 192, 2012.
Silva, H., Lourenço, A., Fred, A., and Martins, R., BIT: Biosignal igniter toolkit. Comput. Methods Prog. Biomed. 115(1):20–32, 2014.
Dey, A., Abowd, G. D., and Salber, D., A conceptual framework and a toolkit for supporting the rapid prototyping of context-aware applications. Hum. Comput. Interact. 16(2):97–166, 2001.
Cânovas, M., Silva, H., Lourenço, A., and Fred, A., MobileBIT: A Framework for mobile interaction recording and display. In: Proceedings of the 6th Conference on Health Informatics (HEALTHINF), pp. 366–369. SciTePress, 2013.
Gamecho, B., Guerreiro, J., Alves, A. P., Lourenço, A., Silva, H., Gardeazabal, L., Abascal, J., and Fred, A., Evaluation of a context-aware application for mobile robot control mediated by physiological data: the ToBITas case study. In: Ubiquitous Computing and Ambient Intelligence. Personalisation and User Adapted Services, LNCS 8867, pp. 147–154, 2014.
Brooke, J., SUS: a quick and dirty usability scale. Usability Evaluation in Industry. London: Taylor and Francis, 1996.
SENIAM Project, Surface ElectroMyoGraphy for the Non-Invasive Assessment of Muscles, available at: http://www.seniam.org/ Accessed 14 July 2015.
The authors gratefully acknowledge the support of the Spanish Ministry of Economy and Competitiveness through Project eGovernability (TIN2014-52665-C2-1-R). EGOKITUZ is funded by the Department of Education, Universities and Research of the Basque Government (grant IT395-10). The authors would also like to acknowledge the support of the Portuguese Fundação para a Ciência e Tecnologia (FCT) under the grant SFRH/BD/65248/2009, the company Bot’n Roll for supporting in use of their platform, as well as the Jatorra retired people association located in Donostia for giving us the opportunity to make the experiment in their facilities. We also want to thank Priscilla Alves, André Lourenço and Prof. Ana Fred for their collaboration in the foundational work for this research .
This article is part of the Topical Collection on Mobile Systems
About this article
Cite this article
Gamecho, B., Silva, H., Guerreiro, J. et al. A Context-Aware Application to Increase Elderly Users Compliance with Physical Rehabilitation Exercises at Home via Animatronic Biofeedback. J Med Syst 39, 135 (2015). https://doi.org/10.1007/s10916-015-0296-1
- Mobile computing
- Physiological signals
- Human-computer interaction