Stefan Mehner
ABSTRACT
Due to demographic changes in developed industrial countries and a better medical care system, the number of elderly people who still live in their home environment is rapidly growing because there they feel more comfortable and independent as in a clinical environment or in a residential care home. The elderly often live alone and receive only irregular visits. Due to impaired physical skills the probability of falls significantly increases. The detection of falls is a crucial aspect in the care of elderly. Falls are often detected very late with severe consequential damages. There are existing approaches for automatic fall detection. They usually deploy special external devices. Elderly people often do not accept these devices because they expose their frailty. In this paper, we present a location-independent fall detection method implemented as a smartphone application for an inconspicuous use in nearly every situation of the daily life. The difficulty of our approach is in the low resolution range of integrated acceleration sensors and the limited energy supply of the smartphone. As solution, we apply a modular threshold-based algorithm which uses the acceleration sensor with moderate energy consumption. Its fall detection rate is in the average of current relevant research.
- S. Abbate et al. Monitoring of human movements for fall detection and activities recognition in elderly care using wireless sensor network: A survey. pages 147--166, December 2010.Google Scholar
- A. K. Bourke et al. Evaluation of a threshold-based tri-axial accelerometer fall detection algorithm. Gait Posture, 26(2):194--199, July 2007.Google ScholarCross Ref
- A. K. Bourke et al. Evaluation of waist-mounted tri-axial accelerometer based fall-detection algorithms during scripted and continuous unscripted activities. Journal of Biomechanics, 43(15):3051--3057, November 2010.Google ScholarCross Ref
- J. Chen et al. Wearable sensors for reliable fall detection. Conference Proceedings - IEEE Engineering in Medicine and Biology Society, 4:3551--3554, 2005.Google Scholar
- S. Fudickar et al. Fall-detection simulator for accelerometers with in-hardware preprocessing. In Proceedings of the 5th International Conference on PErvasive Technologies Related to Assistive Environments, PETRA '12, New York, NY, USA, 2012. ACM. Google ScholarDigital Library
- Future Shape. Sensfloor. http://www.future-shape.de/de/technologies/11/sensfloor, January 2013.Google Scholar
- Z. Z. Htike et al. A monocular view-invariant fall detection system for the elderly in assisted home environments. Intelligent Environments, pages 40--46, 2011. Google ScholarDigital Library
- N. Jia. Fall detection application by using 3-axis accelerometer adxl345. http://www.analog.com/static/imported-files/application_notes/AN-1023.pdf, 2009.Google Scholar
- I. Joint Commission Resources. Reducing the Risk of Falls in Your Health Care Organizaton. Improving health care quality and safety. Joint Commission Resources, 2005.Google Scholar
- I. S. Joint Commission Resources. Reducing the Risk of Patient Harm Resulting from Falls. Joint Commission Resources, 2008.Google Scholar
- M. Kangas et al. Comparison of low-complexity fall detection algorithms for body attached accelerometers. Gait Posture, 28(2):285--291, August 2008.Google ScholarCross Ref
- C. Karth. Fusion of sensor data for robust fall detection in assisted living. Diploma thesis, University of Potsdam, Germany, 2012. (in German).Google Scholar
- S. Lord et al. Falls in Older People: Risk Factors and Strategies for Prevention. Cambridge University Press, 2007.Google ScholarCross Ref
- M. Mubashir et al. A survey on fall detection: Principles and approaches. Neurocomputing, 2012. Google ScholarDigital Library
- Nokia. Sensors. http://doc.qt.nokia.com/qtmobility/sensors-api.html, January 2013.Google Scholar
- N. Noury et al. Fall detection - principles and methods. Engineering in Medicine and Biology Society, 2007. EMBS 2007. 29th Annual International Conference of the IEEE, pages 1663--1666, 2007.Google ScholarCross Ref
- M. Shoaib, R. Dragon, and J. Ostermann. View-invariant fall detection for elderly in real home environment. Image and Video Technology (PSIVT), 2010 Fourth Pacific-Rim Symposium, pages 52--57, 2010. Google ScholarDigital Library
- M. Warner et al. The Complete Guide to Alzheimer's-Proofing Your Home. Purdue University Press, 2000.Google Scholar
Index Terms
- Location-independent fall detection with smartphone
Recommendations
Semi-supervised fall detection algorithm using fall indicators in smartphone
ICUIMC '12: Proceedings of the 6th International Conference on Ubiquitous Information Management and CommunicationFall injury is a health-threatening incident that may cause instant death. There are many research interests aimed to detect fall incidents as early as possible. Fall detection is envisioned critical on ICT-assisted healthcare future. In this paper, we ...
Reliable and secure body fall detection algorithm in a wireless mesh network
BodyNets '13: Proceedings of the 8th International Conference on Body Area NetworksFalls in elderly is one of the most serious causes of severe injury and lack in immediate medical help makes these injuries life threatening. An automatic fall detection system, presented in this research, will help reduce the arrival time of medical ...
A smartphone-based fall detection system
Falls are a major cause of injuries and hospital admissions among elderly people. Thus, the caregiving process and the quality of life of older adults can be improved by adopting systems for the automatic detection of falls. This paper presents a ...
Comments