Details
| Original language | English |
|---|---|
| Title of host publication | 2015 17th International Conference on E-Health Networking, Application and Services, HealthCom 2015 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 640-643 |
| Number of pages | 4 |
| ISBN (electronic) | 9781467383257 |
| Publication status | Published - 2015 |
| Event | 17th International Conference on E-Health Networking, Application and Services, HealthCom 2015 - Boston, United States Duration: 13 Oct 2015 → 17 Oct 2015 |
Publication series
| Name | 2015 17th International Conference on E-Health Networking, Application and Services, HealthCom 2015 |
|---|
Abstract
This paper presents an Android application supporting digital analysis of human gait patterns in a far more flexible way. The application uses the Bluetooth connection and a Shimmer 2R sensor to receive the raw accelerometer signals from typical and atypical gait, followed by a real-time data processing and monitoring to assess the gait quality. On the whole, 7 features per acceleration direction are made available for users. The range of features can be easily redefined and adjusted for the application in diverse type of gait/limb impairments. The user can choose a variety of features, allowing to determine gait abnormalities, quantify gait and walking performance and display them on the screen. The outcomes can be saved on the device or transmitted to treating physicians for a later control of the subject's improvements and the efficiency of physiotherapy treatments in motor rehabilitation. The proposed software solution bears a great potential to be commercialized and deployed to support digital healthcare and therapy.
Keywords
- Android, Digital healthcare, Feature extraction and classification, Gait pattern, Rehabilitation
ASJC Scopus subject areas
- Medicine(all)
- Health Policy
- Health Professions(all)
- Health Information Management
- Computer Science(all)
- Computer Networks and Communications
- Computer Science(all)
- Computer Science Applications
- Medicine(all)
- Health Informatics
- Medicine(all)
- Surgery
- Social Sciences(all)
- Health(social science)
Sustainable Development Goals
Cite this
- Standard
- Harvard
- Apa
- Vancouver
- BibTeX
- RIS
2015 17th International Conference on E-Health Networking, Application and Services, HealthCom 2015. Institute of Electrical and Electronics Engineers Inc., 2015. p. 640-643 7454582 (2015 17th International Conference on E-Health Networking, Application and Services, HealthCom 2015).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - An Android-based application for digital gait performance analysis and rehabilitation
AU - Alcaraz, Javier Conte
AU - Moghaddamnia, Sanam
AU - Peissig, Jurgen
PY - 2015
Y1 - 2015
N2 - This paper presents an Android application supporting digital analysis of human gait patterns in a far more flexible way. The application uses the Bluetooth connection and a Shimmer 2R sensor to receive the raw accelerometer signals from typical and atypical gait, followed by a real-time data processing and monitoring to assess the gait quality. On the whole, 7 features per acceleration direction are made available for users. The range of features can be easily redefined and adjusted for the application in diverse type of gait/limb impairments. The user can choose a variety of features, allowing to determine gait abnormalities, quantify gait and walking performance and display them on the screen. The outcomes can be saved on the device or transmitted to treating physicians for a later control of the subject's improvements and the efficiency of physiotherapy treatments in motor rehabilitation. The proposed software solution bears a great potential to be commercialized and deployed to support digital healthcare and therapy.
AB - This paper presents an Android application supporting digital analysis of human gait patterns in a far more flexible way. The application uses the Bluetooth connection and a Shimmer 2R sensor to receive the raw accelerometer signals from typical and atypical gait, followed by a real-time data processing and monitoring to assess the gait quality. On the whole, 7 features per acceleration direction are made available for users. The range of features can be easily redefined and adjusted for the application in diverse type of gait/limb impairments. The user can choose a variety of features, allowing to determine gait abnormalities, quantify gait and walking performance and display them on the screen. The outcomes can be saved on the device or transmitted to treating physicians for a later control of the subject's improvements and the efficiency of physiotherapy treatments in motor rehabilitation. The proposed software solution bears a great potential to be commercialized and deployed to support digital healthcare and therapy.
KW - Android
KW - Digital healthcare
KW - Feature extraction and classification
KW - Gait pattern
KW - Rehabilitation
UR - http://www.scopus.com/inward/record.url?scp=84966668420&partnerID=8YFLogxK
U2 - 10.1109/HealthCom.2015.7454582
DO - 10.1109/HealthCom.2015.7454582
M3 - Conference contribution
AN - SCOPUS:84966668420
T3 - 2015 17th International Conference on E-Health Networking, Application and Services, HealthCom 2015
SP - 640
EP - 643
BT - 2015 17th International Conference on E-Health Networking, Application and Services, HealthCom 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 17th International Conference on E-Health Networking, Application and Services, HealthCom 2015
Y2 - 13 October 2015 through 17 October 2015
ER -