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Optimized Support Vector Machine for Human Activity Recognition in Smart Homes

Victor Peters, C. G. Igiri, E. O. Bennett

Abstract

Human activity recognition (HAR) plays a pivotal role in advancing smart home automation, healthcare monitoring, and personalized lifestyle interventions. Traditional HAR systems often struggle with accuracy in diverse environments due to user variability and sensor inconsistencies. This paper introduces an optimized Support Vector Machine (SVM) model enhanced through transfer learning to address these challenges. Leveraging the UCI HAR dataset, the proposed system fine-tunes a base SVM classifier, achieving an overall accuracy of 91.84%, a 10.21% improvement over conventional baselines. Key performance metrics include precision (92.3%), recall (91.8%), and F1-score (92.0%). The optimization incorporates advanced feature extraction, noise filtering, and adaptive fine-tuning, making the system suitable for real-time applications in smart homes. Empirical evaluations demonstrate superior robustness across six activities: walking, walking upstairs, walking downstairs, sitting, standing, and laying. This work contributes to HAR literature by validating transfer learning's efficacy in optimizing SVM for non-invasive, privacy-preserving activity monitoring, with implications for scalable smart environment deployments.

Keywords

Human Activity RecognitionMachine LearningSupport Vector MachinesSmart Homes

References

Ahmed, N., Rafiq, J. I., & Islam, M. R. (2019). Enhanced human activity recognition based on smartphone sensor data using hybrid feature selection model. Sensors, 20(1), 317. Barker, S., & Shenoy, P. (2017). A survey on smart home security systems. IEEE Transactions on Consumer Electronics, 63(2), 141-148. Bernardo, F., Sousa Lima, W., & Nakamura, E. F. (2024). A robust human activity recognition system for intelligent transportation systems using smartphone sensors: A position-independent approach. Applied Sciences, 14(22), 10461. Bishop, C. M. (2006). Pattern recognition and machine learning. Springer. Domingo, M. C. (2020). Smart homes: A review of the current state of the art. IEEE Transactions on Industrial Informatics, 16(4), 2345-2356. Grossi, M. (2019). A sensor-centric survey on the development of smartphone measurement and sensing systems. Measurement, 135, 572-592. Gupta, N., Gupta, S. K., Pathak, R. K., et al. (2022). Human activity recognition in artificial intelligence framework: A narrative review. Artificial Intelligence Review, 55(6), 4755-4808. Hevner, A. R., March, S. T., Park, J., & Ram, S. (2004). Design science in information systems research. MIS Quarterly, 28(1), 75-105. Jiang, Y., Chen, Y., & Zhang, W. (2020). Human activity recognition using smartphone sensor data and deep learning algorithms. IEEE Transactions on Human-Machine Systems, 50(3), 245-254. Kastner, W., Neugschwandtner, G., Soucek, S., & Newmann, H. M. (2012). Smart homes: A survey. IEEE Transactions on Consumer Electronics, 58(3), 537-544. Kumar, P. (2018). A survey on sensor data quality in smart homes. IEEE Transactions on Industrial Informatics, 14(3), 1055-1064. Kwapisz, J. R., Weiss, G. M., & Moore, S. A. (2011). Activity recognition using cell phone accelerometers. ACM SIGKDD Explorations Newsletter, 12(2), 74-82. Liciotti, D., Bernardini, M., & Frontoni, E. (2020). Human activity recognition using deep learning approaches. Journal of Intelligent Information Systems, 57(2), 267-285. Lukka, K. (2003). The constructive research approach. In Case study research in business and management (pp. 129-147). Turku School of Economics. Mekruksavanich, S., & Jitpattanakul, A. (2020). LSTM networks using smartphone data for sensor-based human activity recognition in smart homes. IEEE Access, 8, 183243-183253. Mekruksavanich, S., & Jitpattanakul, A. (2021). LSTM networks for human activity recognition using smartphone sensor data. Journal of Ambient Intelligence and Humanized Computing, 12(3), 567-581. Shoaib, M., Bosch, S., Incel, O. D., Scholten, H., & Havinga, P. J. (2015). Fusion of smartphone sensors for human activity recognition. IEEE Sensors Journal, 15(10), 5613-5622. Sutton, R. S., & Barto, A. G. (2018). Reinforcement learning: An introduction. MIT Press. Thukral, M., Dhekane, S. G., Hiremath, S. K., Haresamudram, H., & Plötz, T. (2025). Layout-agnostic human activity recognition in smart homes through textual descriptions of sensor triggers (TDOST). Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 9(1), Article 1. Wang, J., Chen, Y., & Hao, S. (2020). Human activity recognition using smartphone sensor data and machine learning algorithms. IEEE Transactions on Human-Machine Systems, 50(3), 234-245. Ye, J., Jiang, H., & Zhong, J. (2023). A graph-attention-based method for single-resident daily activity recognition in smart homes. Sensors, 23(3), 1626.

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