Human Activity Recognition Using Deep Learning Models on the UCI HAR Dataset
Keywords:
Human Activity Recognition, Deep Learning, CNN-LSTM, UCI HAR Dataset, Wearable Sensors.Abstract
Human Activity Recognition (HAR) has emerged as a critical component of wearable sensing and intelligent healthcare systems, necessitating robust and computationally efficient deep learning architectures. This study presents an empirical experimental evaluation of three deep learning models 1D Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and a hybrid CNN–LSTM using the UCI HAR dataset under a subject-independent protocol. Raw inertial signals were segmented, normalized, and processed through standardized preprocessing pipelines to ensure reproducibility. Performance was assessed using accuracy, precision, recall, F1-score, cross-validation stability, and computational efficiency metrics. Results indicate that the CNN–LSTM architecture achieves the highest test accuracy (94.87%) and demonstrates improved robustness with lower variance and reduced sensitivity to signal perturbations compared to standalone models. Computational analysis confirms that the hybrid configuration maintains feasible inference latency for real-time applications despite moderate increases in parameter size. The findings validate the effectiveness of integrated spatiotemporal feature learning and provide a reproducible benchmark for future research on deep learning–based HAR systems in wearable and IoT contexts.
Downloads
References
Abbaspour, S., Fotouhi, F., Sedaghatbaf, A., Fotouhi, H., Vahabi, M., & Linden, M. (2020). A comparative analysis of hybrid deep learning models for human activity recognition. Sensors, 20(19), 5707.
Abdel-Basset, M., Hawash, H., Chakrabortty, R. K., Ryan, M., Elhoseny, M., & Song, H. (2020). ST-DeepHAR: Deep learning model for human activity recognition in IoHT applications. IEEE Internet of Things Journal, 8(6), 4969-4979.
Akter, M., Ansary, S., Khan, M. A. M., & Kim, D. (2023). Human activity recognition using attention-mechanism-based deep learning feature combination. Sensors, 23(12), 5715.
Ankita, Rani, S., Babbar, H., Coleman, S., Singh, A., & Aljahdali, H. M. (2021). An efficient and lightweight deep learning model for human activity recognition using smartphones. Sensors, 21(11), 3845.
Arshad, M., Jaskani, F. H., Sabri, M. A., Ashraf, F., Farhan, M., Sadiq, M., & Raza, H. (2021). Hybrid machine learning techniques to detect real time human activity using UCI dataset. EAI Endorsed Transactions on Internet of Things, 7(26), e1-e1.
Balaha, H. M., & Hassan, A. E. S. (2023). Comprehensive machine and deep learning analysis of sensor-based human activity recognition. Neural Computing and Applications, 35(17), 12793-12831.
Bhattacharya, D., Sharma, D., Kim, W., Ijaz, M. F., & Singh, P. K. (2022). Ensem-HAR: An ensemble deep learning model for smartphone sensor-based human activity recognition for measurement of elderly health monitoring. Biosensors, 12(6), 393.
Challa, S. K., Kumar, A., & Semwal, V. B. (2022). A multibranch CNN-BiLSTM model for human activity recognition using wearable sensor data. The Visual Computer, 38(12), 4095-4109.
Challa, S. K., Kumar, A., Semwal, V. B., & Dua, N. (2023). An optimized deep learning model for human activity recognition using inertial measurement units. Expert Systems, 40(10), e13457.
Dahou, A., Al-qaness, M. A., Abd Elaziz, M., & Helmi, A. (2022). Human activity recognition in IoHT applications using arithmetic optimization algorithm and deep learning. Measurement, 199, 111445.
Garcia-Gonzalez, D., Rivero, D., Fernandez-Blanco, E., & Luaces, M. R. (2023). Deep learning models for real-life human activity recognition from smartphone sensor data. Internet of Things, 24, 100925.
Gupta, S. (2021). Deep learning based human activity recognition (HAR) using wearable sensor data. International Journal of Information Management Data Insights, 1(2), 100046.
Ignatov, A. (2018). Real-time human activity recognition from accelerometer data using convolutional neural networks. Applied Soft Computing, 62, 915-922.
Kaya, Y., & Topuz, E. K. (2024). Human activity recognition from multiple sensors data using deep CNNs. Multimedia Tools and Applications, 83(4), 10815-10838.
Khatun, M. A., Yousuf, M. A., Ahmed, S., Uddin, M. Z., Alyami, S. A., Al-Ashhab, S., ... & Moni, M. A. (2022). Deep CNN-LSTM with self-attention model for human activity recognition using wearable sensor. IEEE Journal of Translational Engineering in Health and Medicine, 10, 1-16.
Kumar, P., Chauhan, S., & Awasthi, L. K. (2024). Human Activity Recognition (HAR) Using Deep Learning: Review, Methodologies, Progress and Future Research Directions: P. Kumar et al. Archives of Computational Methods in Engineering, 31(1), 179-219.
Mekruksavanich, S., & Jitpattanakul, A. (2021). Biometric user identification based on human activity recognition using wearable sensors: An experiment using deep learning models. Electronics, 10(3), 308.
Nafea, O., Abdul, W., Muhammad, G., & Alsulaiman, M. (2021). Sensor-based human activity recognition with spatio-temporal deep learning. Sensors, 21(6), 2141.
Pang, Y. H., Ping, L. Y., Ling, G. F., Yin, O. S., & How, K. W. (2022). Stacked deep analytic model for human activity recognition on a uci har database. F1000Research, 10, 1046.
Patil, B. U., & Ashoka, D. V. (2023). Data integration based human activity recognition using deep learning models. Karbala International Journal of Modern Science, 9(1), 11.
Sarveshwaran, V., & Joseph, I. T. (2022). Investigation on human activity recognition using deep learning. Procedia Computer Science, 204, 73-80.
Sharen, H., Anbarasi, L. J., Rukmani, P., Gandomi, A. H., Neeraja, R., & Narendra, M. (2024). WISNet: A deep neural network based human activity recognition system. Expert Systems with Applications, 258, 124999.
Thu, N. T. H., & Han, D. S. (2021). HiHAR: A hierarchical hybrid deep learning architecture for wearable sensor-based human activity recognition. IEEE Access, 9, 145271-145281.
Tong, L., Ma, H., Lin, Q., He, J., & Peng, L. (2022). A novel deep learning Bi-GRU-I model for real-time human activity recognition using inertial sensors. IEEE Sensors Journal, 22(6), 6164-6174.
Tufek, N., Yalcin, M., Altintas, M., Kalaoglu, F., Li, Y., & Bahadir, S. K. (2019). Human action recognition using deep learning methods on limited sensory data. IEEE Sensors Journal, 20(6), 3101-3112.
Wan, S., Qi, L., Xu, X., Tong, C., & Gu, Z. (2020). Deep learning models for real-time human activity recognition with smartphones. mobile networks and applications, 25(2), 743-755.
Xia, K., Huang, J., & Wang, H. (2020). LSTM-CNN architecture for human activity recognition. Ieee Access, 8, 56855-56866.
