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Automatic Human Activity Detection Using Novel Deep Learning Architecture

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Artificial Intelligence for Sustainable Development

Part of the book series: EAI/Springer Innovations in Communication and Computing ((EAISICC))

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Abstract

Human Activity Detection (HAD) refers to the process of categorizing and identifying human motion. HAD studies have received significant attention in the estimation of frequency and duration. This has facilitated the use of sophisticated assistive technologies and significant manual examination. The artificial intelligence used by HAD enables experts to deduce human behavior based on data obtained from sources like wearables or physical objects. Deep tissue massage is becoming more often used for HAD (healthcare-associated infections), particularly in relation to everyday tasks and functions. HAD primarily focuses on discerning activities such as ambulation, locomotion, leaping, and engaging in recreational activities. As part of this research project, a new Convolutional Neural Network framework for HAD has been developed. The results of the experiments show that the proposed model performs better than traditional machine learning techniques in correctly recognizing and classifying human actions.

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References

  1. S. Park, H. O. Lee, Y. M. Hwang, S. -K. Ko and B. -T. Lee, “Enhanced Prediction Model for Human Activity Using an End-to-End Approach,” in IEEE Internet of Things Journal, vol. 10, no. 7, pp. 6031–6041, 1 April1, 2023, https://doi.org/10.1109/JIOT.2022.3223674.

  2. Y. Tang, L. Zhang, H. Wu, J. He and A. Song, “Dual-Branch Interactive Networks on Multichannel Time Series for Human Activity Recognition,” in IEEE Journal of Biomedical and Health Informatics, vol. 26, no. 10, pp. 5223–5234, Oct. 2022, https://doi.org/10.1109/JBHI.2022.3193148.

  3. F. Duan, T. Zhu, J. Wang, L. Chen, H. Ning and Y. Wan, “A Multitask Deep Learning Approach for Sensor-Based Human Activity Recognition and Segmentation,” in IEEE Transactions on Instrumentation and Measurement, vol. 72, pp. 1–12, 2023, Art no. 2514012, https://doi.org/10.1109/TIM.2023.3273673.

  4. W. Kong, L. He and H. Wang, “Exploratory Data Analysis of Human Activity Recognition Based on Smart Phone,” in IEEE Access, vol. 9, pp. 73355–73364, 2021, https://doi.org/10.1109/ACCESS.2021.3079434.

  5. R. Mojarad, F. Attal, A. Chibani and Y. Amirat, “Automatic Classification Error Detection and Correction for Robust Human Activity Recognition,” in IEEE Robotics and Automation Letters, vol. 5, no. 2, pp. 2208–2215, April 2020, https://doi.org/10.1109/LRA.2020.2970667.

  6. Z. Yu, Adnan Zahid, Ahmad Taha., “An Intelligent Implementation of Multi-Sensing Data Fusion With Neuromorphic Computing for Human Activity Recognition,” in IEEE Internet of Things Journal, vol. 10, no. 2, pp. 1124–1133, 15 Jan.15, 2023, https://doi.org/10.1109/JIOT.2022.3204581.

  7. V. Dutta and T. Zielinska, “Prognosing Human Activity Using Actions Forecast and Structured Database,” in IEEE Access, vol. 8, pp. 6098–6116, 2020, https://doi.org/10.1109/ACCESS.2020.2963933.

  8. P. William, G. R. Lanke, D. Bordoloi, A. Shrivastava, A. P. Srivastavaa and S. V. Deshmukh, “Assessment of Human Activity Recognition based on Impact of Feature Extraction Prediction Accuracy,” 2023 4th International Conference on Intelligent Engineering and Management (ICIEM), London, United Kingdom, 2023, pp. 1–6, https://doi.org/10.1109/ICIEM59379.2023.10166247.

  9. S. Park, H. O. Lee, Y. M. Hwang, S. -K. Ko and B. -T. Lee, “Enhanced Prediction Model for Human Activity Using an End-to-End Approach,” in IEEE Internet of Things Journal, vol. 10, no. 7, pp. 6031–6041, 1 April1, 2023, https://doi.org/10.1109/JIOT.2022.3223674.

  10. S. Charan, M. S. Saravanan. and R. Surendran., “Prediction of Sufficient Accuracy for Human Activity Recognition using Novel Long Short Term Memory in Compared with Decision Tree,” 2023 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS), Erode, India, 2023, pp. 609–614, https://doi.org/10.1109/ICSCDS56580.2023.10104587.

  11. M. S. Saravanan and S. Charan, “Prediction of Insufficient Accuracy for Human Activity Recognition using Convolutional Neural Network in Compared with Support Vector Machine,” 2022 5th International Conference on Contemporary Computing and Informatics (IC3I), Uttar Pradesh, India, 2022, pp. 1915–1919, https://doi.org/10.1109/IC3I56241.2022.10072905.

  12. Paliwal and S. Gochhait, “Classification of Machine Learning and Power Requirements for HAI (Human Activity Identification),” 2023 International Conference on Inventive Computation Technologies (ICICT), Lalitpur, Nepal, 2023, pp. 199–204, https://doi.org/10.1109/ICICT57646.2023.10134470.

  13. S. Charan, S. M.S. and S. R, “Prediction of Insufficient Accuracy for Human Activity Recognition with Limited Range of Age using K-Nearest Neighbor,” 2023 Second International Conference on Electronics and Renewable Systems (ICEARS), Tuticorin, India, 2023, pp. 926–930, https://doi.org/10.1109/ICEARS56392.2023.10085062.

  14. D. Nagpal and S. Gupta, “Human Activity Recognition and Prediction: Overview and Research Gaps,” 2023 IEEE 8th International Conference for Convergence in Technology (I2CT), Lonavla, India, 2023, pp. 1–5, https://doi.org/10.1109/I2CT57861.2023.10126458.

  15. T. Kumar and B. Prasad, “Prediction of Human Physical Activities With Consumer Smartphone Sensor Data,” 2021 IEEE Bombay Section Signature Conference (IBSSC), Gwalior, India, 2021, pp. 1–6, https://doi.org/10.1109/IBSSC53889.2021.9673366.

  16. N. S. Ghosh, R. Majumdar, B. Giri and A. Ghosh, “Detection of Human Activity by Widget,” 2020 8th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO), Noida, India, 2020, pp. 1330–1334, https://doi.org/10.1109/ICRITO48877.2020.9197982.

  17. R. Moola and A. Hossain, “Human Activity Recognition using Deep Learning,” 2022 URSI Regional Conference on Radio Science (USRI-RCRS), Indore, India, 2022, pp. 1–4, https://doi.org/10.23919/URSI-RCRS56822.2022.10118525.

  18. Zamichos, M. Tsourma, S. Papadopoulos, A. Drosou and D. Tzovaras, “User Profile-aware Daily Activity Prediction,” 2023 24th International Conference on Digital Signal Processing (DSP), Rhodes (Rodos), Greece, 2023, pp. 1–5, https://doi.org/10.1109/DSP58604.2023.10167947.

  19. C. Wang and Z. Peng, “Deep Learning Model for Human Activity Recognition and Prediction in Smart Homes,” 2020 International Conference on Intelligent Transportation, Big Data & Smart City (ICITBS), Vientiane, Laos, 2020, pp. 741–744, https://doi.org/10.1109/ICITBS49701.2020.00163.

  20. K. K. Krishnaprabha and C. K. Raju, “Predicting Human Activity from Mobile Sensor Data Using CNN Architecture,” 2020 Advanced Computing and Communication Technologies for High Performance Applications (ACCTHPA), Cochin, India, 2020, pp. 206–210, https://doi.org/10.1109/ACCTHPA49271.2020.9213225.

  21. T. Sharma, P. Kavipriya and U. Kiruthika, “Towards Improving Human Activity Recognition Using Artificial Neural Network,” 2021 Emerging Trends in Industry 4.0 (ETI 4.0), Raigarh, India, 2021, pp. 1–5, https://doi.org/10.1109/ETI4.051663.2021.9619361.

  22. M. N. Saroja, K. R. Baskaran and P. Priyanka, “Human pose estimation approaches for human activity recognition,” 2021 International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA), Coimbatore, India, 2021, pp. 1–4, https://doi.org/10.1109/ICAECA52838.2021.9675787.

  23. S. Deepak, H. Anandakumar, S. Pavithra, V. Keerthika, and K. Nandhini, “Performance Analysis of Star Topology for Small Networks Using Riverbed,” 2022 8th International Conference on Advanced Computing and Communication Systems (ICACCS), Mar. 2022, https://doi.org/10.1109/icaccs54159.2022.9785012.

  24. C P Shirley, Kantilal Rane, Kolli Himantha Rao, B Bradley Bright, Prashant Agrawal and Neelam Rawat, “Machine learning and sensor-Based Multi-Robot System with Voice Recognition for Assisting the Visually Impaired, Journal of Machine and Computing, vol.3, no.3, pp. 206–215, July 2023. https://doi.org/10.53759/7669/jmc202303019.

  25. A. Haldorai, Ravishankar C. V, Q. S. Mahdi, and P. Devasudha, “Application of AI/ML in Network-Slicing-Based Infrastructure of the Next-Generation Wireless Networking Systems,” 2023 Fifth International Conference on Electrical, Computer and Communication Technologies (ICECCT), Feb. 2023, https://doi.org/10.1109/icecct56650.2023.10179649.

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Authors and Affiliations

  1. Centre for Future Networks and Digital Twin, Sri Eshwar College of Engineering, Coimbatore, Tamil Nadu, India

    Anandakumar Haldorai

  2. Sri Eshwar College of Engineering, Coimbatore, Tamil Nadu, India

    Babitha Lincy R, Suriya Murugan & Minu Balakrishnan

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  1. Anandakumar Haldorai
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  2. Babitha Lincy R
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  4. Minu Balakrishnan
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© 2024 European Alliance for Innovation

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Haldorai, A., R, B.L., Murugan, S., Balakrishnan, M. (2024). Automatic Human Activity Detection Using Novel Deep Learning Architecture. In: Artificial Intelligence for Sustainable Development. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-031-53972-5_23

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  • DOI: https://doi.org/10.1007/978-3-031-53972-5_23

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  • Online ISBN: 978-3-031-53972-5

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