Multi-Layer Perceptron Neural Network and Internet of Things for Improving the Walking Stick with Daily Travel Surveillance of Suburban Elderly

MultiLayer Perceptron Neural Network and Internet of Things for Improving the Walking Stick with Daily Travel Surveillance of Suburban Elderly
  IJETT-book-cover           
  
© 2021 by IJETT Journal
Volume-69 Issue-12
Year of Publication : 2021
Authors : Sumitra Nuanmeesri, Lap Poomhiran
DOI :  10.14445/22315381/IJETT-V69I12P235

How to Cite?

Sumitra Nuanmeesri, Lap Poomhiran, "MultiLayer Perceptron Neural Network and Internet of Things for Improving the Walking Stick with Daily Travel Surveillance of Suburban Elderly," International Journal of Engineering Trends and Technology, vol. 69, no. 12, pp. 317-327, 2021. Crossref, https://doi.org/10.14445/22315381/IJETT-V69I12P235

Abstract
Many countries are entering the era of the elderly, causing the population of the elderly to increase steadily. However, these elderly people still want to be self-reliant, especially walking anywhere without needing a caretaker. Thereby, the walking sticks have become a daily tool to support and walk for the elderly. This paper proposed improving the walking stick as an intelligent cane that is a walking aid and monitoring tool for the daily travel surveillance of suburban elderly in Thailand. The intelligent cane’s daily travel surveillance forecasting model was built by applying the Multi-Layer Perceptron Neural Network. Further, the performance of the model accuracy was enhanced by synthesizing imbalanced data based on Synthetic Minority Over-sampling Technique. The effectiveness of the model showed that the prediction accuracy was 96.89%, the precision was 97.62%, the recall was 98.80%, and F-measure was 98.21%. Moreover, the developed intelligent cane architectures allow their family to monitor, track and communicate with the elderly using the Internet of Things technology and real-time camera by remote control via the mobile application. As a result, this work showed that the suburban elderly could perceive, learn, and appreciate the recent technology necessary for their life.

Keywords
Elderly, Internet of Things, Multi-Layer Perceptron Neural Network, SMOTE, Walking stick.

Reference
[1] Ministry of Social Development and Human Security, Elderly Person Act, B.E. 2546. 7th ed. Bangkok, Thailand: Theppenvanish Press. (2010).
[2] National Statistical Office, Report on the 2020 survey of the older persons in Thailand, Bangkok, Thailand: National Statistical Office, (2021).
[3] C.Phromphak, Aging society in Thailand. Academic Office, The Secretariat of the Senate. 3(16) (2013).
[4] P.Suwannarat, T.Thaweewannakij, S.Kaewsanmung, L. Mato and S. Amatachaya, Walking devices used by community-dwelling elderly: Proportion, types, and associated factors, Hong Kong Physiotherapy Journal. 33(1) (2015) 34-41.
[5] C. Limsakul, Gait aids, walking aids; 2017. Accessed: Feb. 12, 2019, Available from: https://meded.psu.ac.th/binlaApp/class05/388_571_2 /Walking_aids/index.html
[6] X. Yang and X. Li, Design of intelligent walking stick for the elderly based on user experience research, International Conference on Environment and Water Resources Engineering. 179 (2020) 02079.
[7] A.Lachtar, T. Val and A.Kachouri, 3DCane: A monitoring system for the elderly using a connected walking stick, International Journal of Computer Science and Information Security. 14(8) (2016) 1-8.
[8] O.Kessentini, R.Dalce, I.Megdiche andR.Bastide, Towards predicting frailty symptoms through a smart walking stick, International Conference on Performance Evaluation and Modeling in Wired and Wireless Networks. (2018) 1-7.
[9] Amazon, MOXIN Multi-Function Intelligent Elderly Crutches Walking Cane Stick, with LED Light Smart Radio Alarm for Seniors; 2020. Accessed: May 5, 2020, Available from: https://www.amazon.com/moxin-multi-function-intelligent-elderly-crutches/dp/b07xmnwybt/
[10] Listen Technology,Led lighting intelligent multi-functional electronic cane; 2017, Accessed: May 5, 2020, Available from: http://www.cnlisten. com/en/product/Led-lighting-cane.html
[11] BBC, Fujitsu makes smart walking stick to help elderly; 2020. Accessed: May 5, 2020, Available from: https://www.bbc.com/news/technology-21620624/
[12] M. H. A. Wahab, A. A. Talib, H. A. Kadir, A. Johari, and A.Noraziah, Smart cane: Assistive cane for visually-impaired people, International Journal of Computer Science Issues. 8(4) (2011) 21-7.
[13] S. Srinivasan and M.Rajesh, Smart Walking Stick. International Conference on Trends in Electronics and Informatics (ICOEI).(2019) 576-579.
[14] S. Nuanmeesri, Development of low-cost auto robot for plastic floating garbage collection using IoT, International Journal of Engineering and Advanced Technology. 9(2) (2019) 3727-32.
[15] S. Nuanmeesri and L. Poomhiran, Applying the internet of things, speech recognition, and Apriori algorithm for improving the walking stick to help navigate for the blind person, International Journal of Scientific & Technology Research. 9(9 (2020) 179-84.
[16] Y. Wang and L.Long, Towards predicting frailty symptoms through a smart walking stick, International Conference on Communication Technology. 227 (2018) 02021
[17] IdeaConnection, The iCane smart walking stick, Accessed: Aug. 3, 2019. Available from: https://www.ideaconnection.com/new-inventions/the-icane-smart-walking-stick-11865.html.
[18] A.Lachtar, A.Kachouria, and T.Val, Real-time monitoring of elderly using their connected walking stick, International Conference on Smart, Monitored and Controlled Cities. (2017) 48-52.
[19] J. Wang, M. Saada, H. Cai, and Q.Meng, Walking real-time motion detection based on walking stick equipped with MPU and Raspberry Pi, Robotics and Autonomous Systems Conference. (2019) 118-20.
[20] A.Dabir, R.Solkar, M.Kumbhar, and G.Narayanan, GPS and IoT equipped smart walking stick, International Conference on Communication and Signal Processing. (2018) 0322-6.
[21] L.Boppana, V. Jain and R.Kishore, Smart stick for elderly, International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData).(2019) 259-66.
[22] I. G. Fernandez, S. A. Ahmad, and C.Wada, Inertial sensor-based instrumented cane for real-time walking cane kinematics estimation, Sensors. 20 (2020) 46-75.
[23] S.Eeshwaroju, P.Jakkula, and S.Ganesan, Smart Stick an IoT-based Product Idea for Farmers and Senior Citizens, International Conference on Computing and Information Technology. (2020) 1-6.
[24] M. G.Tsipouras, Uterine EMG signals spectral analysis for pre-term birth prediction, Engineering, Technology & Applied Science Research. 8(5) (2018) 3310-5.
[25] P.Palwisut, Improving decision tree technique in imbalanced data sets using SMOTE for internet addiction disorder data, Information Technology Journal. 12(1) (2016) 54-63.
[26] C. Conley and R.Rauth, The emergence of long-life learning; 2020. Accessed: Dec. 2, 2020. Available from: http://dx.doi.org/10.13140/R G.2.2.19860.12162.
[27] N. M. Nordstrom, Lifelong learning - Encourage elders to exercise mind, body, and spirit, Aging Well. 3(2) (2010) 27.
[28] N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P.Kegelmeyer, SMOTE: Synthetic Minority Over-Sampling Technique, Journal of Artificial Intelligence Research. 16 (2002) 321-357.
[29] M.Sokolova and G. Lapalme, A systematic analysis of performance measures for classification tasks, Information Processing & Management. 45(4) (2009) 427-437.
[30] S.Boubaker, S. Kamel and M.Kchaou, Prediction of daily global solar radiation using resilient-propagation artificial neural network and historical data: A case study of Hail, Saudi Arabia. Engineering, Technology & Applied Science Research. 10(1) (2020) 5228-5232.
[31] I. H. Witten, E. Frank, M. A.Hall andC. J. Pal, Deep learning. In: Witten IH, Frank E, Hall MA, Pal CJ, editors. Data Mining. 4th ed. Morgan Kaufmann. (2017) 417-466.
[32] R.Ramezanian, A.Peymanfar and S. B. Ebrahimi, An integrated framework of genetic network programming and multi-layer perceptron neural network for prediction of daily stock return: An application in Tehran stock exchange market, Applied Soft Computing. 82 (2019) 105551.
[33] S. Theodoridis, Bayesian learning: Approximate inference and nonparametric models, In Theodoridis S, editor, Machine Learning. 2nd ed. Academic Press. (2020) 647-730.
[34] D. M. W. Powers, Evaluation: From precision, recall and F-measure to ROC, informedness, markedness & correlation, Journal of Machine Learning Technologies. 2(1 (2011) 37-63.
[35] S. Nuanmeesri, Mobile application for the purpose of marketing, product distribution, and location-based logistics for elderly farmers, Applied Computing and Informatics. (2020) (in press).
[36] N.Thanachawengsakul and P.Wannapiroon, Development of a learning ecosystem using digital knowledge engineering through MOOCs knowledge repository system, International Journal of Engineering Pedagogy11(1) (2021) 35-48.
[37] R. Likert, A technique for the measurement of attitudes, Archives of Psychology. 22(140) (1932) 5-55.
[38] N. V. Hanh, N. T. Long, N. T.Duyen, P. T. T.Canh, N. T. Long, M. D. Thang, Teaching engineering ethics through a psychology course, International Journal of Engineering Pedagogy. 11(1) (2021) 16-34.