ABSTRACT
The fall of the elderly population is a significant source of serious illness. Various wearable fall warning devices have been created recently to ensure older people's health. However, most of these devices are dependent on local data processing. This paper presents a new algorithm used in wearable sensors to track a real-time fall effectively and focuses on fall detection via fuzzy-as-a-service based on IEEE 1855-2016, Java fuzzy markup language and service-oriented architecture. Fuzzy logic systems (FLSs) have revealed their capability in ambient intelligence (AmI) applications. However, FLS deployment requires committed and quasi-scalable hardware/software systems. Sharing FLSs capability as web services allows flexibility, transparency, load balancing, efficient allocation of resources and ultimately cost-effectiveness. In this study, wearable sensors (i.e., accelerometer and gyroscope) that stimulate human activity monitoring using a rule-dependent FLS are demonstrated. Research findings exhibit that the proposed algorithm could easily differentiate between fall and non-fall occurrences with an accuracy, sensitivity and specificity of 90%, 88.89% and 91.67%, respectively.
Supplemental Material
- Giovanni Acampora, Bruno Di Stefano, and Autilia Vitiello. 2016. IEEE 1855™: The First IEEE Standard Sponsored by IEEE Computational Intelligence Society [Society Briefs]. IEEE Computational Intelligence Magazine 11, 4 (2016), 4--6.Google ScholarCross Ref
- Federico Bianchi, Stephen J Redmond, Michael R Narayanan, Sergio Cerutti, and Nigel H Lovell. 2010. Barometric pressure and triaxial accelerometry-based falls event detection. IEEE Transactions on Neural Systems and Rehabilitation Engineering 18, 6 (2010), 619--627.Google ScholarCross Ref
- Alan K Bourke and Gerald M Lyons. 2008. A threshold-based fall-detection algorithm using a bi-axial gyroscope sensor. Medical engineering & physics 30, 1 (2008), 84--90.Google Scholar
- Eduardo Casilari, José-Antonio Santoyo-Ramón, and José-Manuel Cano-García. 2017. Analysis of public datasets for wearable fall detection systems. Sensors 17, 7 (2017), 1513.Google ScholarCross Ref
- Kuang-Hsuan Chen, Yu-Wei Hsu, Jing-Jung Yang, and Fu-Shan Jaw. 2017. Enhanced characterization of an accelerometer-based fall detection algorithm using a repository. Instrumentation Science & Technology 45, 4 (2017), 382--391.Google ScholarCross Ref
- Yueng Santiago Delahoz and Miguel Angel Labrador. 2014. Survey on fall detection and fall prevention using wearable and external sensors. Sensors 14, 10 (2014), 19806--19842.Google ScholarCross Ref
- Poi Voon Er and Kok Kiong Tan. 2018. Non-intrusive fall detection monitoring for the elderly based on fuzzy logic. Measurement 124 (2018), 91--102.Google ScholarCross Ref
- Martin Gjoreski, Hristijan Gjoreski, Mitja Luštrek, and Matjaž Gams. 2016. How accurately can your wrist device recognize daily activities and detect falls? Sensors 16, 6 (2016), 800.Google ScholarCross Ref
- Han Wen Guo, Yi Ta Hsieh, Yu Shun Huang, Jen Chien Chien, Koichi Haraikawa, and Jiann Shing Shieh. 2015. A threshold-based algorithm of fall detection using a wearable device with tri-axial accelerometer and gyroscope. In 2015 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS). IEEE, 54--57.Google ScholarCross Ref
- R Jan Gurley, Nancy Lum, Merle Sande, Bernard Lo, and Mitchell H Katz. 1996. Persons found in their homes helpless or dead. New England Journal of Medicine 334, 26 (1996), 1710--1716.Google ScholarCross Ref
- Farrukh Hijaz, Nabeel Afzal, Talal Ahmad, and Osman Hasan. 2010. Survey of fall detection and daily activity monitoring techniques. In 2010 International Conference on Information and Emerging Technologies. IEEE, 1--6.Google ScholarCross Ref
- Quoc T Huynh, Uyen D Nguyen, Lucia B Irazabal, Nazanin Ghassemian, and Binh Q Tran. 2015. Optimization of an accelerometer and gyroscope-based fall detection algorithm. Journal of Sensors 2015 (2015).Google Scholar
- Quoc T Huynh, Uyen D Nguyen, Su V Tran, Afshin Nabili, and Binh Q Tran. 2013. Fall detection system using combination accelerometer and gyroscope. In Proc. of the Second Int. l Conf. on Advances in Electronic Devices and Circuits (EDC 2013).Google Scholar
- Chin-Feng Lai, Sung-Yen Chang, Han-Chieh Chao, and Yueh-Min Huang. 2010. Detection of cognitive injured body region using multiple triaxial accelerometers for elderly falling. IEEE Sensors Journal 11, 3 (2010), 763--770.Google ScholarCross Ref
- Charles J Lord and David P Colvin. 1991. Falls in the elderly: Detection and assessment. In Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society Volume 13: 1991. IEEE, 1938--1939.Google Scholar
- Bhavesh Pandya, Amir Pourabdollah, and Ahmad Lotfi. 2020. Fuzzy-as-a-Service for Real-Time Human Activity Recognition Using IEEE 1855-2016 Standard. In International Conference on Fuzzy Systems. IEEE. (submitted).Google ScholarCross Ref
- Amir Pourabdollah, Christian Wagner, Giovanni Acampora, and Ahmad Lotfi. 2018. Fuzzy Logic As-a-Service for Ambient Intelligence Environments. In 2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 1--7.Google Scholar
- Arkham Zahri Rakhman, Lukito Edi Nugroho, et al. 2014. Fall detection system using accelerometer and gyroscope based on smartphone. In 2014 The 1st International Conference on Information Technology, Computer, and Electrical Engineering. IEEE, 99--104.Google ScholarCross Ref
- José M Soto-Hidalgo, Jose M Alonso, Giovanni Acampora, and Jesús Alcalá-Fdez. 2018. JFML: A java library to design fuzzy logic systems according to the IEEE Std 1855-2016. IEEE Access 6 (2018), 54952--54964.Google ScholarCross Ref
- Angela Sucerquia, José David López, and Jesús Francisco Vargas-Bonilla. 2018. Real-life/real-time elderly fall detection with a triaxial accelerometer. Sensors 18, 4 (2018), 1101.Google ScholarCross Ref
- Deidre Wild, US Nayak, and B Isaacs. 1981. How dangerous are falls in old people at home? Br Med J (Clin Res Ed) 282, 6260 (1981), 266--268.Google ScholarCross Ref
- Ahmet Yazar, Fatih Erden, and A Enis Cetin. 2014. Multi-sensor ambient assisted living system for fall detection. In Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP'14). 1--3.Google Scholar
- Hongtao Zhang, Muhannand Alrifaai, Keming Zhou, and Huosheng Hu. 2020. A novel fuzzy logic algorithm for accurate fall detection of smart wristband. Transactions of the Institute of Measurement and Control 42, 4 (2020), 786--794.Google ScholarCross Ref
Index Terms
- Fuzzy logic web services for real-time fall detection using wearable accelerometer and gyroscope sensors
Recommendations
Elderly Assistance Using Wearable Sensors by Detecting Fall and Recognizing Fall Patterns
UbiComp '18: Proceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable ComputersFalling is a serious threat to the elderly people. One severe fall can cause hazardous problems like bone fracture or may lead to some permanent disability or even death. Thus, it has become the need of the hour to continuously monitor the activities of ...
Fall Detection Using Accelerometer, Gyroscope & Impact Force Calculation on Android Smartphones
CHIuXiD '18: Proceedings of the 4th International Conference on Human-Computer Interaction and User Experience in Indonesia, CHIuXiD '18Thailand has become an aging society. An accidental fall is a common problem found for the elderly. This research developed an Android mobile application for fall detection. The application compared threshold values of accelerometer and gyroscope ...
IoT based fall detection and ambient assisted system for the elderly
AbstractFalls are considered as risky for the elderly people because it may affect the health of the people. So, in the recent years many elderly fall detection methods has been developed. In the present years many fall detection method had been developed ...
Comments