Generalized and Improved Human Activity Recognition for Real-Time Wellness Monitoring
DOI:
https://doi.org/10.46604/peti.2024.13900Keywords:
lifestyle, healthcare monitoring, human activity recognition, accelerometer data, machine learningAbstract
Human activity categorization using smartphone data can be useful for physicians in real-time data monitoring in sports or lifestyle monitoring. The goal of this research is to develop a methodology that can identify strong machine-learning classifiers applied to various human activity datasets. The first step is pre-processing the data, followed by feature extraction, selection, and classification. Relying on a single dataset does not yield high confidence in the findings. Instead, examining multiple datasets is crucial for a comprehensive understanding, as it avoids the pitfalls of basing conclusions on one dataset alone. Multiple datasets and classifiers are applied in different experiments to achieve improved and generalized human activity recognition performance. Experimental results of the support vector machine (SVM) with its generalized performance of 99% encourage us to use the trained SVM-based model to monitor normal human activities inside the home, in the park, in the gym, etc. enhancing wellness monitoring.
References
F. Laricchia, “Wearables - Statistics & Facts,” https://www.statista.com/topics/1556/wearable-technology/, May 16, 2024.
A. V. L. N. Sujith, G. S. Sajja, V. Mahalakshmi, S. Nuhmani, and B. Prasanalakshmi, “Systematic Review of Smart Health Monitoring Using Deep Learning and Artificial Intelligence,” Neuroscience Informatics, vol. 2, no. 3, article no. 100028, September 2022.
S. Brownsell, D. Bradley, S. Blackburn, F. Cardinaux and M. S. Hawley, “A Systematic Review of Lifestyle Monitoring Technologies,” Journal of Telemedicine and Telecare, vol. 17, no. 4, pp. 185-189, June 2011.
F. Cardinaux, S. Brownsell, D. Bradley, and M. S. Hawley, “A Home Daily Activity Simulation Model for the Evaluation of Lifestyle Monitoring Systems,” Computers in Biology and Medicine, vol. 43, no. 10, pp. 1428-1436, October 2013.
R. A. A. Rosaline, N. P. Ponnuviji, T. C. S. Lakshmi, and G. Manisha, “Enhancing Lifestyle and Health Monitoring of Elderly Populations Using CSA-TkELM Classifier,” Knowledge-Based Systems, vol. 276, article no. 110758, September 2023.
S. Oniga, A. Tisan, and R. Bólyi, “Activity and Health Status Monitoring System,” IEEE 26th International Symposium on Industrial Electronics, pp. 2027-2031, June 2017.
M. M. Hassan, M. Z. Uddin, A. Mohamed, and A. Almogren, “A Robust Human Activity Recognition System Using Smartphone Sensors and Deep Learning,” Future Generation Computer Systems, vol. 81, pp. 307-313, April 2018.
R. A. Voicu, C. Dobre, L. Bajenaru, and R. I. Ciobanu, “Human Physical Activity Recognition Using Smartphone Sensors,” Sensors, vol. 19, no. 3, article no. 458, February 2019.
S. Tsokov, M. Lazarova, and A. Aleksieva-Petrova, “Accelerometer-Based Human Activity Recognition Using 1D Convolutional Neural Network,” IOP Conference Series: Materials Science and Engineering, vol. 1031, article no. 012062, November 2020.
N. Ahmed, J. I. Rafiq, and M. R. Islam, “Enhanced Human Activity Recognition Based on Smartphone Sensor Data Using Hybrid Feature Selection Model,” Sensors, vol. 20, no. 1, article no. 317, January 2020.
A. Ignatov, “Real-Time Human Activity Recognition From Accelerometer Data Using Convolutional Neural Networks,” Applied Soft Computing, vol. 62, pp. 915-922, January 2018.
J. Kang, J. Shin, J. Shin, D. Lee, and A. Choi, “Robust Human Activity Recognition by Integrating Image and Accelerometer Sensor Data Using Deep Fusion Network,” Sensors, vol. 22, no. 1, article no. 174, January 2022.
Y. Li and L. Wang, “Human Activity Recognition Based on Residual Network and BiLSTM,” Sensors, vol. 22, no. 2, article no. 635, January 2022.
A. Bayat, M. Pomplun, and D. A. Tran, “A Study on Human Activity Recognition Using Accelerometer Data From Smartphones,” Procedia Computer Science, vol. 34, pp. 450-457, 2014.
M. Straczkiewicz, P. James, and J. P. Onnela, “A Systematic Review of Smartphone-Based Human Activity Recognition Methods for Health Research,” npj Digital Medicine,” vol. 4, article no. 148, 2021.
N. Gupta, S. K. Gupta, R. K. Pathak, V. Jain, P. Rashidi, and J. S. Suri, “Human Activity Recognition in Artificial Intelligence Framework: A Narrative Review,” Artificial Intelligence Review, vol. 55, no. 6, pp. 4755-4808, August 2022.
S. J. Kang and C. H. Ahn, “The Effects of Home-Based Stair and Normal Walking Exercises on Lower Extremity Functional Ability, Fall Risk Factors, and Cardiovascular Health Risk Factors in Middle-Aged Older Women,” Journal of Exercise Rehabilitation, vol. 15, no. 4, pp. 584-591, August 2019.
J. T. Hancock and T. M. Khoshgoftaar, “Survey on Categorical Data for Neural Networks,” Journal of Big Data, vol. 7, article no. 28, April 2020.
S. Zhang, X. Li, M. Zong, X. Zhu, and D. Cheng, “Learning K for KNN Classification,” ACM Transactions on Intelligent Systems and Technology, vol. 8, no. 3, article no. 43, May 2017.
C. C. Chang and C. J. Lin, “LIBSVM: A Library for Support Vector Machines,” ACM Transactions on Intelligent Systems and Technology, vol. 2, no. 3, article no. 27, April 2011.
M. Pal, “Random Forest Classifier for Remote Sensing Classification,” International Journal of Remote Sensing, vol. 26, no. 1, pp. 217-222, 2005.
A. El Helou, “Sensor HAR Recognition App,” https://www.mathworks.com/matlabcentral/fileexchange/54138-sensor-har-recognition-app, October 15, 2022.
C. Shi, B. Wei, S. Wei, W. Wang, H. Liu, and J. Liu, “A Quantitative Discriminant Method of Elbow Point for the Optimal Number of Clusters in Clustering Algorithm,” EURASIP Journal on Wireless Communications and Networking, vol. 2021, article no. 31, 2021.
C. L. Chowdhary, M. Alazab, A. Chaudhary, S. Hakak, and T. R. Gadekallu, Computer Vision and Recognition Systems Using Machine and Deep Learning Approaches: Fundamentals, Technologies and Applications, Stevenage: Institution of Engineering and Technology, pp. 351-384, 2021.
J. Reyes-Ortiz, D. Anguita, A. Ghio, L. Oneto, and X. Parra, “Human Activity Recognition using Smartphones,” https://archive.ics.uci.edu/dataset/240/human+activity+recognition+using+smartphones, December 18, 2012.
N. Sikder and A. A. Nahid, “KU-HAR: An Open Dataset for Heterogeneous Human Activity Recognition,” Pattern Recognition Letters, vol. 146, pp. 46-54, June 2021.
B. J. Philip, M. Abdelrazek, A. Bonti, S. Barnett, and J. Grundy, “Data Collection Mechanisms in Health and Wellness Apps: Review and Analysis,” JMIR mHealth and uHealth, vol. 10, no. 3, article no. e30468, March 2022.
Q. A. Memon and A. F. Mustafa, “Exploring Mobile Health in a Private Online Social Network,” International Journal of Electronic Healthcare, vol. 8, no. 1, pp. 51-75, September 2015.
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