Time-Distributed Layer Convolutions with Long Short-Term Memory for Human Activity Recognition and Result Comparison with Various Machine Learning Models
Received: 7 February 2025 | Revised: 10 March 2025, 25 March 2025, and 29 March 2025 | Accepted: 30 March 2025 | Online: 4 June 2025
Corresponding author: Avinash K. Gulve
Abstract
Human Activity Recognition (HAR) is an essential area of research with many applications in healthcare, security, and entertainment. One of the main challenges in HAR is the variability in human behavior and reactions to similar inputs, which complicates accurate prediction. This study investigates the utilization of deep learning techniques in enhancing the HAR accuracy. The proposed method uses a Time-Distributed Layer (TDL) framework with LSTM to achieve effective feature extraction and temporal pattern recognition from sensor data. The proposed approach was compared with traditional machine learning models, such as Logistic Regression (LR), Support Vector Machines (SVM), Decision Trees (DT), and Random Forests (RF), to evaluate its effectiveness. The experimental results demonstrate that deep learning models significantly outperform traditional approaches, achieving 97.57% accuracy with TDL-LSTM and 97.81% accuracy with LSTM-TDL, while conventional methods exhibit lower performance. The comparison highlights the advantages of deep learning methods in capturing both spatial and temporal dependencies, resulting in more robust HAR systems. Overall, this study demonstrates the superiority of LSTM-based architectures over traditional models, paving the way for future advances in real-world HAR applications, including wearable devices and intelligent monitoring systems.
Keywords:
Human Activity Recognition (HAR), Time Distributed layer (TDL), Long Short-Term Memory (LSTM)Downloads
References
A. E. Minarno, W. A. Kusuma, and H. Wibowo, "Performance Comparison Activity Recognition using Logistic Regression and Support Vector Machine," in 2020 3rd International Conference on Intelligent Autonomous Systems (ICoIAS), Singapore, Feb. 2020, pp. 19–24.
A. Hayat, F. Morgado-Dias, B. P. Bhuyan, and R. Tomar, "Human Activity Recognition for Elderly People Using Machine and Deep Learning Approaches," Information, vol. 13, no. 6, Jun. 2022, Art. no. 275.
A. Bulling, U. Blanke, and B. Schiele, "A tutorial on human activity recognition using body-worn inertial sensors," ACM Computing Surveys, vol. 46, no. 3, Jan. 2014, Art. no. 31.
D. Anguita, A. Ghio, L. Oneto, X. Parra, and J. L. Reyes-Ortiz, "Human Activity Recognition on Smartphones Using a Multiclass Hardware-Friendly Support Vector Machine," in Ambient Assisted Living and Home Care, 2012, pp. 216–223.
D. Anguita, A. Ghio, L. Oneto, X. Parra Perez, and J. L. Reyes Ortiz, "A public domain dataset for human activity recognition using smartphones," presented at the Proceedings of the 21th International European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, 2013, pp. 437–442. [Online]. Available: https://upcommons.upc.edu/handle/2117/20897.
D. Anguita, A. Ghio, L. Oneto, F. X. Llanas Parra, and J. L. Reyes Ortiz, "Energy efficient smartphone-based activity recognition using fixed-point arithmetic," Journal of universal computer science, vol. 19, no. 9, pp. 1295–1314, 2013.
J. L. Reyes Ortiz, A. Ghio, D. Anguita, X. Parra Perez, J. Cabestany Moncusí, and A. Català Mallofré, "Human activity and motion disorder recognition: towards smarter interactive cognitive environments," presented at the Proceedings of the 21th International European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, 2013, pp. 403–412. [Online]. Available: https://upcommons.upc.edu/handle/2117/20901.
K. Chen, D. Zhang, L. Yao, B. Guo, Z. Yu, and Y. Liu, "Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges, and Opportunities," ACM Computing Surveys, vol. 54, no. 4, Feb. 2021, Art. no. 77.
K. Muralidharan, R. Anirudh, G. Rithvik, P. Saket, A. A. Reghunaath, and M. P. Gopinath, "1D Convolution approach to human activity recognition using sensor data and comparison with machine learning algorithms," International Journal of Cognitive Computing in Engineering, vol. 2, pp. 130–143, Jun. 2021.
L. B. Marinho, A. H. de Souza Junior, and P. P. Rebouças Filho, "A New Approach to Human Activity Recognition Using Machine Learning Techniques," in Intelligent Systems Design and Applications, Cham, 2017, pp. 529–538.
L. Xu, W. Yang, Y. Cao, and Q. Li, "Human activity recognition based on random forests," in 2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD), Guilin, Jul. 2017, pp. 548–553.
N. Dua, S. N. Singh, V. B. Semwal, and S. K. Challa, "Inception inspired CNN-GRU hybrid network for human activity recognition," Multimedia Tools and Applications, vol. 82, no. 4, pp. 5369–5403, Feb. 2023.
R. Liu, A. A. Ramli, H. Zhang, E. Henricson, and X. Liu, "An Overview of Human Activity Recognition Using Wearable Sensors: Healthcare and Artificial Intelligence," in Internet of Things – ICIOT 2021, 2022.
S. Kiranyaz, M. Zabihi, A. B. Rad, T. Ince, R. Hamila, and M. Gabbouj, "Real-time phonocardiogram anomaly detection by adaptive 1D Convolutional Neural Networks," Neurocomputing, vol. 411, pp. 291–301, Oct. 2020.
S. Kiranyaz, O. Avci, O. Abdeljaber, T. Ince, M. Gabbouj, and D. J. Inman, "1D convolutional neural networks and applications: A survey," Mechanical Systems and Signal Processing, vol. 151, Apr. 2021, Art. no. 107398.
S. Kiranyaz, T. Ince, O. Abdeljaber, O. Avci, and M. Gabbouj, "1-D Convolutional Neural Networks for Signal Processing Applications," in ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brighton, United Kingdom, May 2019, pp. 8360–8364.
Y. D. Zhang, S. C. Satapathy, D. S. Guttery, J. M. Górriz, and S. H. Wang, "Improved Breast Cancer Classification Through Combining Graph Convolutional Network and Convolutional Neural Network," Information Processing & Management, vol. 58, no. 2, Mar. 2021, Art. no. 102439.
S. M. Lee, S. M. Yoon, and H. Cho, "Human activity recognition from accelerometer data using Convolutional Neural Network," in 2017 IEEE International Conference on Big Data and Smart Computing (BigComp), Jeju Island, South Korea, Feb. 2017, pp. 131–134.
S. Sengupta et al., "A review of deep learning with special emphasis on architectures, applications and recent trends," Knowledge-Based Systems, vol. 194, Apr. 2020, Art. no. 105596.
S. Wan, L. Qi, X. Xu, C. Tong, and Z. Gu, "Deep Learning Models for Real-time Human Activity Recognition with Smartphones," Mobile Networks and Applications, vol. 25, no. 2, pp. 743–755, Apr. 2020.
S. Dargan, M. Kumar, M. R. Ayyagari, and G. Kumar, "A Survey of Deep Learning and Its Applications: A New Paradigm to Machine Learning," Archives of Computational Methods in Engineering, vol. 27, no. 4, pp. 1071–1092, Sep. 2020.
D. A. Jorge Reyes-Ortiz, "Human Activity Recognition Using Smartphones." UCI Machine Learning Repository, 2013.
Y. Liu, S. Liu, Y. Wang, F. Lombardi, and J. Han, "A Stochastic Computational Multi-Layer Perceptron with Backward Propagation," IEEE Transactions on Computers, vol. 67, no. 9, pp. 1273–1286, Sep. 2018.
N. H. Duong, M. T. Nguyen, T. H. Nguyen, and T. P. Tran, "Application of Seasonal Trend Decomposition using Loess and Long Short-Term Memory in Peak Load Forecasting Model in Tien Giang," Engineering, Technology & Applied Science Research, vol. 13, no. 5, pp. 11628–11634, Oct. 2023.
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