Rehab-aaService: a cloud-based motor rehabilitation digital assistant
Pages 305 - 308
Abstract
Body Sensor Networks (BSNs) are playing an important role in the ongoing revolution of the health-care system, introducing the domain of the so-called m-Health. The integration of BSN applications with Cloud-computing technologies is an emerging approach, promising to favor the diffusion of many m-Health services in real life. Among them, motor rehabilitation is one of the application areas where this is particularly true. Monitoring rehabilitation patients via communication networks and mobile computing systems is a crucial aspect since the idea of tying the opportunity to follow and monitor the patient at all post-admission stages through remote monitoring allows to substantially reduce the costs associated with the process. On the other hand, patients that can safely perform rehabilitation and be monitored remotely will get benefit in terms of comfort, physical stress, and economic cost. This paper introduces a motor rehabilitation digital assistant, called Rehab-aaService, based on a three-tier architecture that includes wearable motion sensor nodes, a personal mobile device, and a Cloud-based back-end supported by the BodyCloud middleware.
References
[1]
J. Balogun, N. T. Farina, E. Fay, K. Rossmann, and L-Pozyc. Energy Cost Determination Using a Portable Accelerometer. Physical Therapy, 66:1102--1107, 1986.
[2]
F. Bellifemine, G. Fortino, R. Giannantonio, R. Gravina, A. Guerrieri, and M. Sgroi. SPINE: A domain-specific framework for rapid prototyping of WBSN applications. Software Practice and Experience, 41(3):237--265, 2011.
[3]
M. Chen, S. Gonzalez, A. Vasilakos, H. Cao, and V. C. Leung. Body area networks: a survey. Mobile networks and applications, 16(2):171--193, 2011.
[4]
CoRehab-Website. www.corehab.it, Feb. 2014.
[5]
C. Costa, D. Tacconi, R. Tomasi, F. Calva, and V. Terreri. RIABLO: a game system for supporting orthopedic rehabilitation. In Proc. of the biannual Conf. of the Italian Chapter of SIGCHI, 2013.
[6]
G. Fortino, R. Giannantonio, R. Gravina, P. Kuryloski, and R. Jafari. Enabling Effective Programming and Flexible Management of Efficient Body Sensor Network Applications. IEEE Trans. on Human-Machine Systems, 43(1):115--133, 2013.
[7]
G. Fortino, D. Parisi, V. Pirrone, and G. D. Fatta. BodyCloud: A SaaS approach for community Body Sensor Networks. Future Generation Computer Systems, 35(6):62--79, 2014.
[8]
A. Hadjidj, M. Souil, A. Bouabdallaha, Y. Challal, and H. Owen. Wireless Sensor Networks for Rehabilitation Applications: Challenges and Opportunities. J. of Network and Computer Applications, 36, 2013.
[9]
L. Hale, K. Williams, C. Ashton, T. Connole, H. McDowell, and C. Taylor. Reliability of RT3 accelerometer for measuring mobility in people with multiple sclerosis: Pilot study. J. Rehabilitation Research & Development, 44(4), 2007.
[10]
PamSys-Website. www.biosensics.com, Feb. 2014.
[11]
S. Patel, H. Park, P. Bonato, L. Chan, and M. Rodgers. A review of wearable sensors and systems with application in rehabilitation. J. of NeuroEngineering and Rehabilitation, 9(21), 2012.
[12]
I. Raso, R. Hervas, and J. Bravo. m-Physio: Personalized Accelerometer-based Physical Rehabilitation Platform. In Proc. of the 4th Int. Conf. on Mobile Ubiquitous Computing, Systems, Services and Technologies, 2010.
[13]
N. Raveendranathan, S. Galzarano, V. Loseu, R. Gravina, R. Giannantonio, M. Sgroi, R. Jafari, and G. Fortino. From Modeling to Implementation of Virtual Sensors in Body Sensor Networks. IEEE Sensors J., 12(3):583--593, 2012.
[14]
Restlet-Website. http://restlet.org, Feb. 2014.
[15]
R. Nerino, L. Contin, W. G. da Silva Pinto, G. Massazza, M. Actis, P. Capacchione, A. Chimienti, and G. Pettiti. A BSN based service for post-surgical knee rehabilitation at home. In Proc. of the 8th Int. Conf. on Body Area Networks, 2013.
Recommendations
Using cloud-assisted body area networks to track people physical activity in mobility
BodyNets '15: Proceedings of the 10th EAI International Conference on Body Area NetworksThis paper describes a novel BSN-based integrated system for detecting, monitoring, and securely recording human physical activities using wearable sensors, a personal mobile device, and a Cloud-computing infrastructure supported by the BodyCloud ...
Using Orientation Sensors to Control a FES System for Upper-Limb Motor Rehabilitation
Bioinformatics and Biomedical EngineeringAbstractContralaterally controlled functional electrical stimulation (CCFES) is a recent therapy aimed at improving the recovery of impaired limbs after stroke. For hemiplegic patients, CCFES uses a control signal from the non-impaired side of the body to ...
Cooperative sensors: a new wired body-sensor-network approach for wearable biopotential measurement
MOBIHEALTH'15: Proceedings of the 5th EAI International Conference on Wireless Mobile Communication and HealthcareCooperative sensors are a novel measurement architecture based on active dry electrodes that allows the acquisition of biopotential signals (e.g., electrocardiogram, ECG) on patients in a comfortable and easy-to-integrate manner. This paper starts by ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
May 2014
459 pages
ISBN:9781631900112
- General Chair:
- Andreas Hein,
- Program Chairs:
- Susanne Boll,
- Friedrich Köhler
In-Cooperation
Publisher
ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering)
Brussels, Belgium
Publication History
Published: 20 May 2014
Check for updates
Author Tags
Qualifiers
- Research-article
Conference
PervasiveHealth '14
PervasiveHealth '14: 8th International Conference on Pervasive Computing Technologies for Healthcare
May 20 - 23, 2014
Oldenburg, Germany
Acceptance Rates
Overall Acceptance Rate 55 of 116 submissions, 47%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 123Total Downloads
- Downloads (Last 12 months)8
- Downloads (Last 6 weeks)3
Reflects downloads up to 22 Dec 2024
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in