International Journal of Engineering and Manufacturing (IJEM)
IJEM Vol. 12, No. 2, 8 Apr. 2022
Cover page and Table of Contents: PDF (size: 567KB)
Towards Ambient Assisted Living (AAL): Design of an IoT-based Elderly Activity Monitoring System
Full Text (PDF, 567KB), PP.1-10
Views: 0 Downloads: 0
Author(s)
Index Terms
Ambient Assisted Living (AAL), Internet of Things (IoT), Supervised Learning
Abstract
This paper presents a design and development of an IoT-based system to real-time track elders' physical activities using accelerometer sensor data. The objective behind conducting such research is to overcome the lack of ability to monitor physical activities. Especially with the development of the socio-economic sector, the number of elders who live in isolated areas such as elderly homes have increased rapidly. In such a case with declining cognitive abilities, the healthcare of these elderly personalities becomes vulnerable. This research project fulfilled the necessity of a system to capture the vital details about those people. The Internet of Things (IoT) and cloud-based applications have become a significant part of the Information and Technology sector. Realtime monitoring is a concept tightly coupled with IoT cloud cloud-native application for this application is an excellent example of that.Further, the requirement of a low-cost system was fulfilled by using hardware components such as NodeMCU and accelerometer sensors. The designed and developed system is composed of a cost-effective wrist-worn device capable of capturing hand movement on three different arises. Hence, the detected signals are transmitted to a master node to process and recognize the activity according to the detected signal. Another significant aspect of the project is using machine learning techniques to recognize the four different activities such as walking, sitting, sleeping, and standing. The use of supervised machine learning techniques is evaluated to overcome the barriers of real-time activity recognition. Further different supervised machine learning algorithms were used and evaluated, which were extracted from existing literature. The project was conducted while accomplishing the machine learning life cycle stages, and it has significantly benefitted from generating highly accurate final results for the overall system. Further different supervised machine learning algorithms were used and evaluated, which were extracted from existing literature. The supervised machine learning algorithm Decision Tree Classifier used for this study. Using the Decision Classifier Tree algorithm succeeded in gaining more than 80% of model accuracy. Since the research topic comes under a classification type-oriented problem, the testing process of the model has been done using the confusion matrix for the trained model.
Cite This Paper
I.D.M.S Rupasinghe, M.W.P Maduranga, " Towards Ambient Assisted Living (AAL): Design of an IoT-based Elderly Activity Monitoring System ", International Journal of Engineering and Manufacturing (IJEM), Vol.12, No.2, pp. 1-10, 2022. DOI: 10.5815/ijem.2022.02.01
Reference
[1]R. Priya, N. Suman, and A. A. A. Utsav, "Internet of Things (IoT)- Review and It's Multiple Classification,": 2021 Third International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV), 2021.
[2]P. Dudhe, N. Kadam and M. S. D. R. M. Hushangabade, "Internet of Things (IoT): An overview and its applications," in 2017 International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS), India, 2017.
[3]S. A. Sheena Angra, "Machine learning and its applications: A review," in 2017 International Conference on Big Data Analytics And Computational Intelligence (ICBDAC), India, 2017.
[4]P. Louridas and C. Ebert, "Machine Learning," IEEE Software, vol. 33, no. 5, 2016.
[5]J. Li, Y. Wang and H. T. Junyu Lai, "Ambient assisted living," China Communications, vol. 13, no. 5, 2016.
[6]S. F. Mohamed Ahmed Hail, "IoT for AAL: An Architecture via Information-Centric Networking," in 2015 IEEE Globecom Workshops, San Diago, USA, 2015.
[7]L. Mainetti, L. Patrono, and I. S. Andrea Secco, "An IoT-aware AAL system for elderly people," in 2016 International Multidisciplinary Conference on Computer and Energy Science (SpliTech), Split, Croatia, 2016.
[8]P. Duarte, E. F. Coutinho, J. Body and W. V. Mossaab Hariz, "Using IoT in AAL Platforms for Older Adults: A Systematic Mapping," in 2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC), Madrid, Spain, 2020.
[9]M. Bassoil, a. Balenchi and P. C. llari de mulari, "An IoT Approach for an AAL Wi-Fi-Based Monitoring System," IEEE Transactions on Instrumentation and Measurement, vol. 66, no. 12, 2017.
[10]Shashika C M Madushan, M W P Maduranga and Ishan P I Madushanka. Design of Wi-Fi based IoT Sensor Node with Multiple Sensor Types. International Journal of Computer Applications 174(13):40-44, January 2021
[11]Cheng, O. Amft and P. L. Gernot Bahle, "Toward Higher Sensitivity in Wearable CapacitiveSensing for Activity Recognition," EEE SENSOR JOURNAL, SPECIAL ISSUE ON FLEXIBLE SENSOR AND SENSING SYSTEMS, vol. xx, no. xx.
[12]G. S. Eisa Jafari Amirbandi, "Exploring methods and systems for vision-based human activity recognition," in 2016 1st Conference on Swarm Intelligence and Evolutionary Computation (CSIEC), Bam, Iran, 2016.
[13]S. Mashiyama and T. O. Jihoon Hon, "Activity recognition using low-resolution infrared array sensor," in 2015 IEEE International Conference on Communications (ICC), London,UK, 2015.
[14]S. Karunaratna and P. Maduranga, "Artificial Intelligence on Single Board Computers: An Experiment on Sound Event Classification," 2021 5th SLAAI International Conference on Artificial Intelligence (SLAAI-ICAI), 2021, pp. 1-5, doi: 10.1109/SLAAI-ICAI54477.2021.9664746.
[15]Q. Liang, L. Yu, X. Zhai and H. N. Zaihong Wan, "Activity Recognition Based on Thermopile Imaging Array Sensor," in 2018 IEEE International Conference on Electro/Information Technology (EIT), Rochester, MI, USA, 2018.
[16]U. D. Giacomo, G. Capobianco, F. Martinelli and A. S. Francesco Mercaldo, "Wearable Devices for Human Activity Recognition and User Detection," in 2019 27th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP), Pavia, Italy, 2019.
[17]S. E. Ali, A. N. Khan and M. M. Shafaq Zia, "Human Activity Recognition System using Smart Phone based Accelerometer and Machine Learning," in 2020 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT), Bali, Indonesia, 2020.
[18]M. W. P. Maduranga and R. G. Ragel, "Comparison of load balancing methods for Raspberry-Pi Clustered Embedded Web Servers," 2016 International Computer Science and Engineering Conference (ICSEC), 2016, pp. 1-4, doi: 10.1109/ICSEC.2016.7859875.
[19]A. Jefiza, E. Pramunanto and M. H. P. Hanny Boedinoegroho, "Fall detection based on accelerometer and gyroscope using back propagation," in 2017 4th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI), Yogyakarta, Indonesia, 2017.
[20]W.-J. Li, C. Yen, Y.-S. Lin and S. H. Shu-Chu Tung, "JustIoT Internet of Things based on the Firebase real-time database," in 2018 IEEE International Conference on Smart Manufacturing, Industrial & Logistics Engineering (SMILE), Hsinchu, Taiwan, 2018.
[21]Y. S. P. Weerasinghe, M. W. P. Maduranga and M. B. Dissanayake, "RSSI and Feed Forward Neural Network (FFNN) Based Indoor Localization in WSN," 2019 National Information Technology Conference (NITC), 2019, pp. 35-40, doi: 10.1109/NITC48475.2019.9114515.
[22]M.W.P Maduranga, Ruvan Abeysekera, "TreeLoc: An Ensemble Learning-based Approach for Range Based Indoor Localization", International Journal of Wireless and Microwave Technologies(IJWMT), Vol.11, No.5, pp. 18-25, 2021.DOI: 10.5815/ijwmt.2021.05.03