Development of a Fall Detection System Based on Neural Network Featuring IoT-Technology
Accidental falls are considered a major cause of accidents that could lead to serious injuries, paralysis, psychological damage, and even deaths, especially for the elderly. Therefore in this project, a neural network-based fall detection system that could automatically detect a fall event is proposed. The system is enhanced with Internet-of-Things (IoT) features that could reduce the response time and efficiently improve the prognosis of fall victims. A 10 Degree of Freedom (DOF) Inertial Measurement Unit (IMU) module is connected to an Intel Edison with Mini Breakout board and mounted on a wearable waist-worn device to continuously record body movements. A backpropagation neural network algorithm has been developed to accurately distinguish falls from different postural transitions during activities of daily living (ADL). A body temperature and heart-pulse monitoring device were developed for this system to provide the medical personnel additional information on the body condition of the fall victim. Using the latest IoT-technology, the system can be connected to the internet and provides a continuous and real-time monitoring capability. Once a fall accident happens, the system will be automatically triggered. This will activate an Android App through the Wi-Fi network that will then send an emergency SMS with the actual location and body conditions of the victim to a recipient. A series of falls and ADL simulations were performed by a group of subjects to test and validate the performance of the system. The experiment results showed that the proposed system could obtain a sensitivity of 95.5%, specificity of 96.4%, and accuracy of 96.3%.
W. Qu, F. Lin, and W. Xu, "A Real-time Low-complexity Fall Detection System On The Smartphone," in Connected Health: Applications, Systems and Engineering Technologies (CHASE), 2016 IEEE First International Conference on, 2016, pp. 354-356.
S. SABRI, "Department of Statistics, Malaysia," 2016.
Ramachandran, Anita, and Anupama Karuppiah. "A survey on recent advances in wearable fall detection systems." BioMed research international 2020, 2020.
Kavya, Thathupara Subramanyan, et al. "Fall Detection System for Elderly People using Vision-Based Analysis." Science And Technology 23.1, 2020, pp. 69-83.
Casilari, Eduardo, Raúl Lora-Rivera, and Francisco García-Lagos. "A wearable fall detection system using deep learning." International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems. Springer, Cham, 2019.
N. Anwar and M. Abdullah, "Through-the-wall human sensing based on change detection," in 2015 IEEE International Conference on Imaging Systems and Techniques (IST), 2015, pp. 1-5.
Y. Li, K. Ho, and M. Popescu, "Efficient source separation algorithms for acoustic fall detection using a Microsoft Kinect," IEEE Transactions on Biomedical Engineering, vol. 61, pp. 745-755, 2014.
A. Kassim, M. S. Jamri, M. S. M. Aras, M. Rashid, and M. Yaacob, "Design and development of obstacle detection and warning device for above abdomen level," in Control, Automation and Systems (ICCAS), 2012 12th International Conference on, 2012, pp. 410-413.
B. Aguiar, T. Rocha, J. Silva, and I. Sousa, "Accelerometer-based fall detection for smartphones," in Medical Measurements and Applications (MeMeA), 2014 IEEE International Symposium on, 2014, pp. 1-6.
X. Yuan, S. Yu, Q. Dan, G. Wang, and S. Liu, "Fall detection analysis with wearable MEMS-based sensors," in Electronic Packaging Technology (ICEPT), 2015 16th International Conference on, 2015, pp. 1184-1187.
G. Shi, J. Zhang, C. Dong, P. Han, Y. Jin, and J. Wang, "Fall detection system based on inertial mems sensors: Analysis design and realization," in Cyber Technology in Automation, Control, and Intelligent Systems (CYBER), 2015 IEEE International Conference on, 2015, pp. 1834-1839.
A. T. Özdemir, "An analysis on sensor locations of the human body for wearable fall detection devices: Principles and practice," Sensors, vol. 16, p. 1161, 2016.
A. Z. Rakhman and L. E. Nugroho, "Fall detection system using accelerometer and gyroscope based on smartphone," in Information Technology, Computer and Electrical Engineering (ICITACEE), 2014 1st International Conference on, 2014, pp. 99-104.
L. N. V. Colon, Y. DeLaHoz, and M. Labrador, "Human fall detection with smartphones," in Communications (LATINCOM), 2014 IEEE Latin-America Conference on, 2014, pp. 1-7.
J. Jacob, T. Nguyen, D. Y. Lie, S. Zupancic, J. Bishara, A. Dentino, et al., "A fall detection study on the sensors placement location and a rule-based multi-thresholds algorithm using both accelerometer and gyroscopes," in Fuzzy Systems (FUZZ), 2011 IEEE International Conference on, 2011, pp. 666-671.
P. Pierleoni, A. Belli, L. Palma, L. Pernini, and S. Valenti, "A versatile ankle-mounted fall detection device based on attitude heading systems," in Biomedical Circuits and Systems Conference (BioCAS), 2014 IEEE, 2014, pp. 153-156.
S. Electronics. (2017). Intel® Edison and Mini Breakout Kit. Available: https://www.sparkfun.com/products/13025
N. Nuttaitanakul and T. Leauhatong, "A novel algorithm for detection human falling from accelerometer signal using wavelet transform and neural network," in Information Technology and Electrical Engineering (ICITEE), 2015 7th International Conference on, 2015, pp. 215-220.
J. L. Chua, Y. C. Chang, and W. K. Lim, "Intelligent visual based fall detection technique for home surveillance," in Computer, Consumer and Control (IS3C), 2012 International Symposium on, 2012, pp. 183-187.
M. Vallejo, C. V. Isaza, and J. D. Lopez, "Artificial neural networks as an alternative to traditional fall detection methods," in Engineering in Medicine and Biology Society (EMBC), 2013 35th Annual International Conference of the IEEE, 2013, pp. 1648-1651.
C. Dinh and M. Struck, "A new real-time fall detection approach using fuzzy logic and a neural network," in Wearable Micro and Nano Technologies for Personalized Health (pHealth), 2009 6th International Workshop on, 2009, pp. 57-60.
H. J. Mohd, A. Ismail, T. Ahmad Izzuddin, M. F. Sulaima, and M. S. Mokhtar, "Feasibility study of vehicular heatstroke avoidance system for children," The International Journal of Engineering And Science, vol. 4, pp. 14-18, 2015.
T. A. Izzuddin, M. Ariffin, Z. H. Bohari, R. Ghazali, and M. H. Jali, "Movement intention detection using neural network for quadriplegic assistive machine," in Control System, Computing and Engineering (ICCSCE), 2015 IEEE International Conference on, 2015, pp. 275-280.
A. Z. Shukor, M. F. Miskon, M. H. Jamaluddin, F. bin Ali, M. F. Asyraf, and M. B. bin Bahar, "A new data glove approach for Malaysian sign language detection," Procedia Computer Science, vol. 76, pp. 60-67, 2015.
- There are currently no refbacks.
ISSN : 2590-3551, eISSN : 2600-8122
Best viewed using Mozilla Firefox, Google Chrome and Internet Explorer with the resolution of 1280 x 800