Multilayer Neural Network Based Fall Alert System Using IOT
Keywords:
Fall, IoT, neural network , sensors, accelerometer, GSM.Abstract
Fall in elderly people staying alone is a health concern which draws the attention of researchers in past years. In this research, we develop an elderly monitoring system using advanced technology based on sensors and the Internet of Things. In this system, older people can avoid any interaction with healthcare institutions like nursing home and hospitals. We present this challenge by establishing a smart setup to monitor human behavior through accelerometer, pulse sensor and GSM. To implement this concept, we used an alert system to the personal care assistant, to monitor the data using different learning methods. Based on Multi-layer Neural Network technique data are collected from the sensors, then processed and passed to the server in the form of an alert through the buzzer, SMS, email or voice message. The result obtained show an accuracy level of 96% compared to other classifiers.
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References
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[16] Engel, William, and Wei Ding. “Reliable and Practical Fall Prediction Using Artificial Neural Network.” 2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD), 2017, pp.1867–71, doi:10.1109/FSKD.2017.8393052.
[2] Xu, Tao, et al. “New Advances and Challenges of Fall Detection Systems: A Survey.” Applied Sciences, vol. 8, no. 3, 2018, p. 418, doi:10.3390/app8030418.
[3] Yi, Won-Jae, and Jafar Saniie. “Patient Centered Real-Time Mobile Health Monitoring System.” E-Health Telecommunication Systems and Networks, vol. 05, no. 04, 2016, pp. 75–94, doi:10.4236/etsn.2016.54007.
[4] Yi, Won-Jae, et al. “Design Flow of Neural Network Application for IoT Based Fall Detection System.” 2018 IEEE International Conference on Electro/Information Technology (EIT), 2018, pp. 0578–82, doi:10.1109/EIT.2018.8500179.
[5] Santos, Guto, et al. “Accelerometer-Based Human Fall Detection Using Convolutional Neural Networks.” Sensors, vol. 19, no. 7, 2019, p. 1644, doi:10.3390/s19071644.
[6] Ajerla, Dharmitha, et al. “A Real-Time Patient Monitoring Framework for Fall Detection.” Wireless Communications and Mobile Computing, vol. 2019, 2019, pp. 1–13, doi:10.1155/2019/9507938.
[7] Satija, Udit, et al. “Real-Time Signal Quality-Aware ECG Telemetry System for IoT-Based Health Care Monitoring.” IEEE Internet of Things Journal, vol. 4, no. 3, 2017, pp. 815–23, doi:10.1109/JIOT.2017.2670022.
[8] Muhammad, Ghulam, et al. “Smart Health Solution Integrating IoT and Cloud: A Case Study of Voice Pathology Monitoring.” IEEE Communications Magazine, vol. 55, no. 1, 2017, pp. 69–73, doi:10.1109/MCOM.2017.1600425CM.
[9] Delahoz, Yueng, and Miguel Labrador. “Survey on Fall Detection and Fall Prevention Using Wearable and External Sensors.” Sensors, vol. 14, no. 10, 2014, pp. 19806–42, doi:10.3390/s141019806.
[10]Boudra, Hasna, et al. “An Intelligent Medical Monitoring System Based on Sensors and Wireless Sensor Network.” 2014 International Conference on Advances in Computing, Communications and Informatics (ICACCI), 2014, pp. 1650–56, doi:10.1109/ICACCI.2014.6968205.
[11] Saadeh, Wala, et al. “A Patient-Specific Single Sensor IoT-Based Wearable Fall Prediction and Detection System.” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 27, no. 5, 2019, pp. 995–1003, doi:10.1109/TNSRE.2019.2911602.
[12]Gia, Tuan Nguyen, et al. “Customizing 6LoWPAN Networks towards Internet-of-Things Based Ubiquitous Healthcare Systems.” 2014 NORCHIP, 2014, pp. 1–6, doi:10.1109/NORCHIP.2014.7004716.
[13] Baker, Stephanie B., et al. “Internet of Things for Smart Healthcare: Technologies, Challenges, and Opportunities.” IEEE Access, vol. 5, 2017, pp. 26521–44, doi:10.1109/ACCESS.2017.2775180.
[14] Jefiza, Adlian, et al. “Fall Detection Based on Accelerometer and Gyroscope Using Back Propagation.” 2017 4th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI), 2017, pp. 1–6, doi:10.1109/EECSI.2017.8239149.
[15] Nukala, Bhargava, et al. “Real-Time Classification of Patients with Balance Disorders vs. Normal Subjects Using a Low-Cost Small Wireless Wearable Gait Sensor.” Biosensors, vol. 6, no. 4, 2016, p. 58, doi:10.3390/bios6040058.
[16] Engel, William, and Wei Ding. “Reliable and Practical Fall Prediction Using Artificial Neural Network.” 2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD), 2017, pp.1867–71, doi:10.1109/FSKD.2017.8393052.
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2019-12-27
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