Fall Detection Using Neural Network Based on Internet of Things Streaming Data
DOI:
https://doi.org/10.21928/uhdjst.v4n2y2020.pp91-98Keywords:
Fall Detection, Internet of Things, Artificial Neural Networks, Machine LearningAbstract
Fall event has become a critical health problem among elderly people. We propose a fall detection system that analyzes real-time streaming data from the Internet of Things (IoT) to detect irregular patterns related to fall. We train a deep neural network model using accelerometer data from an online physical activity monitoring dataset named, MobiAct. An IBM Cloud-based IoT data processing framework is used to manage streaming data. About 96.71% of accuracy is achieved in assessing the performance of the proposed model.
References
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[45] O. Aziz, M. Musngi, E. J. Park, G. Mori and S. N. Robinovitch. “A comparison of accuracy of fall detection algorithms (threshold-based vs. machine learning) using waist-mounted tri-axial accelerometer signals from a comprehensive set of falls and non-fall trials”. International Federation for Medical and Biological Engineering, vol. 55, no. 1, pp. 45-55, 2017.
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[2] Mahfuz. “Detecting Detecting Irregular Patterns in IoT Streaming Data for Fall Detection Irregular Patterns in IoT Streaming Data for Fall Detection”. 2018 IEEE 9th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON), 2018.
[3] E. Bahmani, M. Jamshidi and A. Shaltooki. “Breast cancer prediction using a hybrid data mining model”. International Journal on Informatics Visualization, vol. 3, no. 4, pp. 327-331, 2019.
[4] J. Liu. “Development and evaluation of a prior-to-impact fall event detection algorithm”. IEEE Transactions on Biomedical Engineering, vol. 61, no. 7, pp. 2135-2140, 2014.
[5] P. Pierleoni. “A high reliability wearable device for elderly fall detection”. IEEE Sensors Journal, vol. 15, no. 8, pp. 4544-4553, 2015.
[6] R. Freitas. “Wearable Sensor Networks Supported by Mobile Devices for Fall Detection”. SENSORS, IEEE, 2014.
[7] X. Zhuang. “Acousticfalldetection Using Gaussian Mixture Models and Gmm Super Vectors”. 2009 Ieee International Conference on Acoustics, Speech and Signal Processing, 2009.
[8] Y. Li. “Efficient source separation algorithms for acoustic fall detection using a microsoft kinect”. IEEE Transactions on Biomedical Engineering, vol. 61, no. 3, pp. 745-755, 2014.
[9] R. Hamedanizad, E. Bahmani, M. Jamshidi and A. M. Darwesh. “Employing data mining techniques for predicting opioid withdrawal in applicants of health centers”. Journal of Science and Technology, vol. 3, no. 2, pp. 33-40, 2019.
[10] A. Shaltooki and M. Jamshidi. “The use of data mining techniques in predicting the noise emitted by the trailing edge of aerodynamic objects”. International Journal on Informatics Visualization, vol. 3Z, no. 4, pp. 388-393, 2019.
[11] A. S. Cook. “Falls in the Medicare population: incidence, associated factors, and impact on health care”. Physical Therapy, vol. 2009, no. 2, pp. 324-332, 2009.
[12] P. Kannus. “Fall-induced injuries and deaths among older adults”. JAMA, vol. 281, no. 20, pp. 1895-1899, 1999.
[13] Y. Cheng. “A Fall Detection System Based on SensorTag and Windows 10 IoT Core”. 15th International Conference on Mechanical Science and Engineering, pp. 238-244, 2015.
[14] O. Ojetola, E. I. Gaura and J. Brusey. “Fall Detection with Wearable Sensors Safe (Smart Fall Detection)”. Intelligent Environments, 2011 7th International Conference on, pp. 318-321, 2011.
[15] S. M. S. Forbes. “Fall prediction using behavioural modelling from sensor data insmarthomes”. Artificial Intelligence Review, vol. 53, pp, 1071-1091, 2019.
[16] S. K. Gharghan. “Accurate fall detection and localization for elderly people based on neural network and energy-efficient wireless sensor network”. Energies, vol. 11, pp. 1-32, 2018.
[17] D. Yacchirema. “Fall detection system for elderly people using IoT and big data”. Procedia Computer Science, vol. 130, pp. 603-610, 2018.
[18] C. C. H. Hsu. “FallCare+: An IoT Surveillance System for Fall Detection”. Proceedings of the 2017 IEEE International Conference on Applied System Innovation IEEE-ICASI 2017 Meen, pp. 921- 922, 2017.
[19] Ahmad S. “Real-Time Anomaly Detection for Streaming Analytics”. arXiv, 2016.
[20] S. C. Tan. “Fast Anomaly Detection for Streaming Data”. Proceedings of the 22nd International Joint Conference on Artificial Intelligence, pp. 1511-1516, 2011.
[21] M. Gupta. “Outlier detection for temporal data: A survey”. IEEE Transactions on Knowledge and Data Engineering, vol. 26, no. 9, pp. 2250-2267, 2014.
[22] S. Sadik and L. Gruenwald. “Research Issues in Outlier Detection for Data Streams”. Vol. 15. SIGKDD Explorations, pp. 33-40, 2013.
[23] T. Özdemir and B. Barshan. “Detecting falls with wearable sensors using machine learning techniques”. Sensors, vol. 14, no. 6, pp. 10691-10708, 2014.
[24] H. Kerdegari, K. Samsudin, A. R. Ramli and S. Mokaram. “Development of wearable human fall detection system using multilayer perceptron neural network”. International Journal of Computational Intelligence Systems, vol. 6, no. 1, pp. 127-136, 2013.
[25] T. Nukala, N. Shibuya, A. Rodriguez and J. Tsay. “An efficient and robust fall detection system using wireless gait analysis sensor with artificial neural network (ANN) and support vector machine (SVM) algorithms”. Open Journal of Applied Biosensor, vol. 3, pp. 29-39, 2014.
[26] T. Theodoridis, V. Solachidis, N. Vretos and P. Daras. “Human fall detection from acceleration measurements using a recurrent neural network”. In: Precision Medicine Powered by pHealth and Connected Health. Springer, Berlin, Germany, pp. 145-149, 2018.
[27] M. Musci, D. De Martini, N. Blago, T. Facchinetti and M. Piastra. “Online Fall Detection using Recurrent Neural Networks”. arXiv, 2018.
[28] G. Vavoulas, C. Chatzaki, T. Malliotakis, M. Pediaditis and M. Tsiknakis. “The MobiAct Dataset: Recognition of Activities of Daily Living Using Smartphones”. ICT4AgeingWell, pp. 143-151, 2016.
[29] D. Dharmitha, M. Sazia and Z. Farhana. “Fall Detection from Physical Activity Monitoring Data”. Presented at the International SIGKDD workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications (BigMine), London, 2018.
[30] D. Ajerla, S. Mahfuz and F. H. Zulkernine. “A Real-Time Patient Monitoring Framework for Fall Detection”. Queen’s University, Kingston, Ontario, Canada, 2018.
[31] D. Ajerla. “Fall Detection from Physical Activity Monitoring Data”. BIGMINE, London, UK, 2018.
[32] T. Xu, Y. Zhou and J. Zhu. “New advances and challenges of fall detection systems: A survey”. Applied Science, vol. 8, no. 3, p. 418, 2018.
[33] H. Chen, (Eds.). “Ngu3Fall Detection Using Smartwatch Sensor Data with Accessor Architecture”. Springer International Publishing, Berlin, Germany, pp. 81-93, 2017.
[34] E. C. J. Santoyo-Ramón. “UMAFall: Fall Detection Dataset (Universidad deMalaga)”. Available from: https://www.figshare. com/articles/UMA_ADL_FALL_Dataset_zip/4214283. [Last accessed on 2018 Jun 04].
[35] S. Ahmad, A. Lavin, S. Purdy and Z. Agha. “Unsupervised real-time anomaly detection for streaming data”. Neurocomputing, vol. 262, pp. 134-147, 2017.
[36] M. Ahmed, A. N. Mahmood and M. R. Islam. “A survey of anomaly detection techniques in financial domain”. Future Generation Computer Systems, vol. 55, pp. 278-288, 2016.
[37] S. Ahmad and S. Purdy. “Real-time Anomaly Detection for Streaming Analytics”. arXiv, 2016.
[38] M. Mohammadi, A. Al-Fuqaha, S. Sorour, and M. Guizani, “Deep Learning for IoT Big Data and Streaming Analytics: A Survey,” IEEE Communications Surveys & Tutorials, 2018.
[39] J. Meehan, C. Aslantas, S. Zdonik, N. Tatbul and J. Du. “Data Ingestion for the Connected World”. Classless Inter-Domain Routing, 2017.
[40] S. Sadik and L. Gruenwald. “Research issues in outlier detection for data streams”. ACM SIGKDD Explorations Newsletter, vol. 15, no. 1, pp. 33-40, 2014.
[41] M. Gupta, J. Gao, C. C. Aggarwal and J. Han. “Outlier detection for temporal data: A survey”. IEEE Transactions on Knowledge and Data Engineering, vol. 26, no. 9, pp. 2250-2267, 2014.
[42] E. Casilari, J. A. Santoyo-Ramón and J. M. Cano-García. “Analysis of public datasets for wearable fall detection systems”. Sensors, vol. 17, no. 7, p. 1513, 2017.
[43] J. Meehan. “Data Ingestion for the Connected World”. Creative Commons Attribution License, 2017.
[44] M. P. G. Vavoulas. “The mobiFall dataset: Fall detection and classification with a smartphone”. International Journal of Monitoring and Surveillance Technologies Research, vol. 2, no. 1, p. 13, 2014.
[45] O. Aziz, M. Musngi, E. J. Park, G. Mori and S. N. Robinovitch. “A comparison of accuracy of fall detection algorithms (threshold-based vs. machine learning) using waist-mounted tri-axial accelerometer signals from a comprehensive set of falls and non-fall trials”. International Federation for Medical and Biological Engineering, vol. 55, no. 1, pp. 45-55, 2017.
[46] G. Vavoulas1. “The MobiAct Dataset: Recognition of Activities of Daily Living using Smartphones”. In” International Conference on Information and Communication Technologies for Ageing Well and e-Health, 2016.
[47] J. R. Kwapisz, G. M. Weiss and S. A. Moore. “Activity recognition using cell phone accelerometers”. SIGKDD Explorations, vol. 12, no. 2, pp. 74-82, 2010.
[48] Y. Z. Z. Wang. “Application of Relieff Algorithm to Selecting Feature Sets for Classification of High Resolution Remote Sensing Image”. 2016 IEEE International Geoscience and Remote Sensing Symposium, pp. 755-758, 2016.
[49] MQTT. Available from: https://www.mqtt.org/getting-started. [Last accessed on 2020 Jan 01].
[50] A. Kafka. Available from: https://www.kafka.apache.org/intro. [Last accessed on 2020 Jan 05].
[51] IBM. “IBM Watson Studio IBM Watson and Cloud Platform Learning Center”, 2020. Available from: https://www.developer.ibm.com/ technologies/data-science.
[52] IBM. “IBM Streaming Analytics IBM Watson and Cloud Platform Learning Center”. 2016-07-18 2016
[53] IBM. “IBM Cloudant-IBM Watson and Cloud Platform Learning Center”, 2020. Available from: https://www.developer.ibm.com/ components/cloud-ibm.
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Published
2020-10-25
How to Cite
Kakarash, Z. A., Karim, S. H., & Mohammadi, M. (2020). Fall Detection Using Neural Network Based on Internet of Things Streaming Data. UHD Journal of Science and Technology, 4(2), 91–98. https://doi.org/10.21928/uhdjst.v4n2y2020.pp91-98
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