2435 939
Full Length Article
Journal of Intelligent Systems and Internet of Things
Volume 3 , Issue 1, PP: 18-31 , 2021 | Cite this article as | XML | Html |PDF

Title

A Novel Artificial Intelligence Based Internet of Things for Fall Detection of Elderly Care Monitoring

  Noushini Nikeetha 1 ,   Kirubasri G.V. 2 ,   Haritha Sasikumar 3 ,   Yazhini Tamilanban 4 ,   Jagruti Patil 5 ,   Gopinath 6 *

1  Department of Biomedical Engineering, VelTech Multitech Dr.Rangarajan Dr.Sakunthala Engineering College, Chennai, India
    (Noushini1Nikeetha2gmail.com)

2  Department of Biomedical Engineering, VelTech Multitech Dr.Rangarajan Dr.Sakunthala Engineering College, Chennai, India.
    (kirubavelu1611@gmail.com)

3  Department of Biomedical Engineering, VelTech Multitech Dr.Rangarajan Dr.Sakunthala Engineering College, Chennai, India
    (HarithaSasikumar4@gmail.com)

4  Department of Biomedical Engineering, VelTech Multitech Dr.Rangarajan Dr.Sakunthala Engineering College, Chennai, India
    (Yazhini amilanban2gmail.ccom)

5  Department of Biomedical Engineering,L.D. College of Engineering, Ahmedabad.India
    (Jagruti2Patil@gmail.com)

6  Bharath Institute of Higher Education and Research,Chennai, India
    (Gopinath12@gmail.com)


Doi   :   https://doi.org/10.54216/JISIoT.030102

Received: February 07, 2021 Accepted: June 11, 2021

Abstract :

A fall of an older adult often leads to severe injuries and is found to be a significant reason for the death due to post-traumatic complications. Many falls happen in the home atmosphere and prevail unrecognized. Thus, the need for reliable early fall detection is necessary for fast help. Lately, the emergence of wearables, smartphones, IoT, etc., made it possible to develop systems fall detection which aids in the remote monitoring of the elderly. The goal is to allow intelligent algorithms and smartphones to detect falls for elderly care and to monitor them regularly. This work presents the Artificial Intelligence of Things for Fall Detection (AIOTFD) system using a slime mould algorithm (SMA) to optimize the final data. The features extracted using SqueezeNet further CNN based SMA used for data optimization. The validation of the AIOTFD model performance is evaluated through the Multiple Cameras Fall Dataset (MCFD) and UR Fall Detection dataset (URFD). The empirical results accentuated the assuring realization of the model compared to other state-of the art methods.The obtained results shows our proposed AIOTFD attains accuracy of 99.82% and 99.79% and databases can be used for additional investigation and optimizations to increase the recognition rate to enhance the independent life of the elderly.

Keywords :

Fall detection system; Elderly Care; Remote Monitoring; Artificial Intelligence; Internet of Things

References :

1.      W. K. A. Hasan; A. M. Abood and M. Habbal, "A Review of Blockchain-based on IoT applications (challenges and future research directions)," 2020 5th International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications (CITISIA), 2020, pp. 1-7, doi: 10.1109/CITISIA50690.2020.9371814.

2.      T. F. Bernadus; L. B. Subekti and Y. Bandung, IoT-Based Fall Detection and Heart Rate Monitoring System for Elderly Care," 2019 International Conference on ICT for Smart Society (ICISS), 2019, pp. 1-6, doi: 10.1109/ICISS48059.2019.8969845.

3.      https://www.igor-tech.com/news-and-insights/articles/iot-in-healthcare-enhancing-medical-environments-with-innovative-solutions

4.       https://vilmate.com/blog/why-use-ai-enabled-iot-in-healthcare/

5.      T. Vaiyapuri; E. Laxmi Lydia; M. Y. Sikkandar; V. G. Díaz, I. V. Pustokhina and D. A. Pustokhin, Internet of Things and Deep Learning Enabled Elderly Fall Detection Model for Smart Homecare, IEEE Access, 2021, doi: 10.1109/ACCESS.2021.3094243.

6.      M. J. A. Nahian et al., Towards an Accelerometer-Based Elderly Fall Detection System Using Cross-Disciplinary Time Series Features, IEEE Access, vol. 9, pp. 39413-39431, 2021, doi: 10.1109/ACCESS.2021.3056441.

7.      Mahendran RK; Velusamy P; Ramadoss P; Shanmugapriyan J ;Pandian P, An efficient priority-based convolutional auto-encoder approach for electrocardiogram signal compression in Internet of Things based healthcare system, Trans Emerging Tel Tech,2021;32: e4115. https://doi.org/10.1002/ett.4115.

8.      Mrozek, D., Koczur, A. and MaƂysiak-Mrozek, B., Fall detection in older adults with mobile IoT devices and machine learning in the cloud and on the edge. Information Sciences, 537,2020 pp.132-147.

9.      Mahendran, R. K. and Velusamy, P., A secure fuzzy extractor based biometric key authentication scheme for body sensor network in Internet of Medical Things, Computer Communications, 2020,153, 545-552.

10.    

11.   Ogawa, Y. and Naito, K., Fall detection scheme based on temperature distribution with IR array sensor. In 2020 IEEE International Conference on Consumer Electronics (ICCE) ,2020, (pp. 1-5). IEEE

12.   Nooruddin, Sheikh, Md Milon Islam, and Falguni Ahmed Sharna, An IoT based device-type invariant fall detection system, Internet of Things ,2020, 100130.

13.   Xu, G., Deep convolutional neural network to detect JUNIWARD, In Proceedings of the 5th ACM Workshop on Information Hiding and Multimedia Security, pp. 67-73.

14.   B. A. Y. Alqaralleh, S. N. Mohanty, D. Gupta, A. Khanna, K. Shankar and T. Vaiyapuri, "Reliable Multi-Object Tracking Model Using Deep Learning and Energy Efficient Wireless Multimedia Sensor Networks," in IEEE Access, 2020, vol. 8, pp. 213426-213436, doi: 10.1109/ACCESS.2020.3039695.

15.   A. Rajagopal et al., "Fine-Tuned Residual Network-Based Features with Latent Variable Support Vector Machine-Based Optimal Scene Classification Model for Unmanned Aerial Vehicles," in IEEE Access, vol. 8, pp. 118396-118404, 2020, doi: 10.1109/ACCESS.2020.3004233.

16.   V. Porkodi et al., Resource Provisioning for Cyber–Physical–Social System in Cloud-Fog-Edge Computing Using Optimal Flower Pollination Algorithm, IEEE Access, vol. 8, pp. 105311-105319, 2020, doi: 10.1109/ACCESS.2020.2999734.

17.   Li, Shimin; Chen, Huiling; Wang, Mingjing; Heidari, Ali Asghar; Mirjalili, Seyedali , Slime mould algorithm: A new method for stochastic optimization,  Future Generation Computer Systems : 300–323. doi:10.1016/j.future.2020.03.055. ISSN 0167-739X.

18.   Patino-Ramirez, Fernando; Boussard, Aurèle; Arson, Chloé; Dussutour, Audrey, Substrate composition directs slime molds behavior, Scientific Reports, 9 (1): 15444. doi:10.1038/s41598-019-50872-z. ISSN 2045-2322.

19.   Zubaidi, Salah L.; Abdulkareem, Iqbal H.; Hashim, Khalid S.; Al-Bugharbee, Hussein; Ridha, Hussein Mohammed; Gharghan, Sadik Kamel; Al-Qaim, Fuod F.; Muradov, Magomed et al., Hybridised Artificial Neural Network Model with Slime Mould Algorithm: A Novel Methodology for Prediction of Urban Stochastic Water Demand, Water, 12 (10): 2692. doi:10.3390/w12102692.

20.   Kumar, C.; Raj, T. Dharma; Premkumar, M.; Raj, T. Dhanesh, T. Dhanesh, A new stochastic slime mould optimization algorithm for the estimation of solar photovoltaic cell parameters, Optik, 223: 165277. doi:10.1016/j.ijleo.2020.165277. ISSN 0030-4026.

 

21.    Mostafa, Manar; Rezk, Hegazy; Aly, Mokhtar; Ahmed, Emad M.,A new strategy based on slime mould algorithm to extract the optimal model parameters of solar PV panel, Sustainable Energy Technologies and Assessments, 42: 100849. doi:10.1016/j.seta.2020.100849. ISSN 2213-1388.


Cite this Article as :
Style #
MLA Noushini Nikeetha, Kirubasri G.V., Haritha Sasikumar, Yazhini Tamilanban , Jagruti Patil, Gopinath. "A Novel Artificial Intelligence Based Internet of Things for Fall Detection of Elderly Care Monitoring." Journal of Intelligent Systems and Internet of Things, Vol. 3, No. 1, 2021 ,PP. 18-31 (Doi   :  https://doi.org/10.54216/JISIoT.030102)
APA Noushini Nikeetha, Kirubasri G.V., Haritha Sasikumar, Yazhini Tamilanban , Jagruti Patil, Gopinath. (2021). A Novel Artificial Intelligence Based Internet of Things for Fall Detection of Elderly Care Monitoring. Journal of Journal of Intelligent Systems and Internet of Things, 3 ( 1 ), 18-31 (Doi   :  https://doi.org/10.54216/JISIoT.030102)
Chicago Noushini Nikeetha, Kirubasri G.V., Haritha Sasikumar, Yazhini Tamilanban , Jagruti Patil, Gopinath. "A Novel Artificial Intelligence Based Internet of Things for Fall Detection of Elderly Care Monitoring." Journal of Journal of Intelligent Systems and Internet of Things, 3 no. 1 (2021): 18-31 (Doi   :  https://doi.org/10.54216/JISIoT.030102)
Harvard Noushini Nikeetha, Kirubasri G.V., Haritha Sasikumar, Yazhini Tamilanban , Jagruti Patil, Gopinath. (2021). A Novel Artificial Intelligence Based Internet of Things for Fall Detection of Elderly Care Monitoring. Journal of Journal of Intelligent Systems and Internet of Things, 3 ( 1 ), 18-31 (Doi   :  https://doi.org/10.54216/JISIoT.030102)
Vancouver Noushini Nikeetha, Kirubasri G.V., Haritha Sasikumar, Yazhini Tamilanban , Jagruti Patil, Gopinath. A Novel Artificial Intelligence Based Internet of Things for Fall Detection of Elderly Care Monitoring. Journal of Journal of Intelligent Systems and Internet of Things, (2021); 3 ( 1 ): 18-31 (Doi   :  https://doi.org/10.54216/JISIoT.030102)
IEEE Noushini Nikeetha, Kirubasri G.V., Haritha Sasikumar, Yazhini Tamilanban, Jagruti Patil, Gopinath, A Novel Artificial Intelligence Based Internet of Things for Fall Detection of Elderly Care Monitoring, Journal of Journal of Intelligent Systems and Internet of Things, Vol. 3 , No. 1 , (2021) : 18-31 (Doi   :  https://doi.org/10.54216/JISIoT.030102)