Deep Learning and Recurrent Signature Based Classification for Sensor Based HAR: Addressing
Explainability and Complexity in 5G Networks
Karthikeyan R
Karthikeyan R
Department of Computer Science and Engineering, Artificial Intelligence and Data Analytics, Sri Ramachandra Institute of Higher Education and Research, Chennai, Tamil Nadu, India.
Department of Computer Science and Engineering, Saveetha school of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, Tamil Nadu, India.
Institute of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, Tamil Nadu, India.
Institute of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, Tamil Nadu, India.
When it comes to clinical applications, sensor-based human activity recognition (HAR) is invaluable, and numerous machine learning algorithms have effectively used to obtain excellent presentation. Using a variety of on-body sensors, these systems attempt to ascertain the subject's status relative to their immediate surroundings. There was a time when feature extraction was done by hand, but now more and more people are using Artificial Neural Networks (ANNs). A number of innovative approaches to HAR have surfaced since the advent of deep learning. Problems arise, however, for sensor-based HAR classification algorithms in today's communication networks. Among these, you can find solutions to problems like deal with complicated and large-scale data signals, extract characteristics from complicated datasets, and meet explainability standards. For complicated 5G networks, these difficulties become even more apparent. In particular, explainability is now critical for the broad use of sensor-based HAR in 5G networks and beyond. The research suggests a classification approach based on path signatures, recurrent signature (ReS), to address these issues. This cutting-edge model employs deep-learning (DL) approaches to circumvent the tedious feature selection challenge. Furthermore, the study investigates how to improve the ReS model's classification accuracy by using graph-based optimisation methods. To test how well the suggested framework worked, to dug deep into the publicly available dataset, which included a separate set of tasks. The paper's empirical results on AReM datasets achieved an average accuracy of 96%.
F. Serpush, M. B. Menhaj, B. Masoumi, and B. Karasfi, “Wearable Sensor-Based Human Activity Recognition in the Smart Healthcare System,” Computational Intelligence and Neuroscience, vol. 2022, pp. 1–31, Feb. 2022, doi: 10.1155/2022/1391906.
I. Dirgová Luptáková, M. Kubovčík, and J. Pospíchal, “Wearable Sensor-Based Human Activity Recognition with Transformer Model,” Sensors, vol. 22, no. 5, p. 1911, Mar. 2022, doi: 10.3390/s22051911.
V. Bijalwan, V. B. Semwal, and V. Gupta, “Wearable sensor-based pattern mining for human activity recognition: deep learning approach,” Industrial Robot: the international journal of robotics research and application, vol. 49, no. 1, pp. 21–33, Aug. 2021, doi: 10.1108/ir-09-2020-0187.
V. Uma Maheswari, S. Stephe, R. Aluvalu, A. Thirumalraj, and S. N. Mohanty, “Chaotic Satin Bowerbird Optimizer Based Advanced AI Techniques for Detection of COVID-19 Diseases from CT Scans Images,” New Generation Computing, Aug. 2024, doi: 10.1007/s00354-024-00279-w.
Y. J. Luwe, C. P. Lee, and K. M. Lim, “Wearable Sensor-Based Human Activity Recognition with Hybrid Deep Learning Model,” Informatics, vol. 9, no. 3, p. 56, Jul. 2022, doi: 10.3390/informatics9030056.
H. Park, N. Kim, G. H. Lee, and J. K. Choi, “MultiCNN-FilterLSTM: Resource-efficient sensor-based human activity recognition in IoT applications,” Future Generation Computer Systems, vol. 139, pp. 196–209, Feb. 2023, doi: 10.1016/j.future.2022.09.024.
A. Ferrari, D. Micucci, M. Mobilio, and P. Napoletano, “Deep learning and model personalization in sensor-based human activity recognition,” Journal of Reliable Intelligent Environments, vol. 9, no. 1, pp. 27–39, Jan. 2022, doi: 10.1007/s40860-021-00167-w.
S. Baswaraju, A. Thirumalraj, and B. Manjunatha, “Unlocking the Potential of Deep Learning in Knee Bone Cancer Diagnosis Using MSCSA-Net Segmentation and MLGC-LTNet Classification,” Sustainable Development Using Private AI, pp. 190–213, Jul. 2024, doi: 10.1201/9781032716749-10.
D. Bhattacharya, D. Sharma, W. Kim, M. F. Ijaz, and P. K. Singh, “Ensem-HAR: An Ensemble Deep Learning Model for Smartphone Sensor-Based Human Activity Recognition for Measurement of Elderly Health Monitoring,” Biosensors, vol. 12, no. 6, p. 393, Jun. 2022, doi: 10.3390/bios12060393.
H. M. Balaha and A. E.-S. Hassan, “Comprehensive machine and deep learning analysis of sensor-based human activity recognition,” Neural Computing and Applications, vol. 35, no. 17, pp. 12793–12831, Mar. 2023, doi: 10.1007/s00521-023-08374-7.
S. Mekruksavanich and A. Jitpattanakul, “Hybrid convolution neural network with channel attention mechanism for sensor-based human activity recognition,” Scientific Reports, vol. 13, no. 1, Jul. 2023, doi: 10.1038/s41598-023-39080-y.
S. Kobayashi, T. Hasegawa, T. Miyoshi, and M. Koshino, “MarNASNets: Toward CNN Model Architectures Specific to Sensor-Based Human Activity Recognition,” IEEE Sensors Journal, vol. 23, no. 16, pp. 18708–18717, Aug. 2023, doi: 10.1109/jsen.2023.3292380.
A. Dahou, M. A. A. Al-Qaness, M. A. Elaziz, and A. M. Helmi, “MLCNNwav: Multilevel Convolutional Neural Network With Wavelet Transformations for Sensor-Based Human Activity Recognition,” IEEE Internet of Things Journal, vol. 11, no. 1, pp. 820–828, Jan. 2024, doi: 10.1109/jiot.2023.3286378.
C. Anitha, T. Rajesh Kumar, R. Balamanigandan, and R. Mahaveerakannan, “Fault Diagnosis of Tenessee Eastman Process with Detection Quality Using IMVOA with Hybrid DL Technique in IIOT,” SN Computer Science, vol. 4, no. 5, Jun. 2023, doi: 10.1007/s42979-023-01851-9.
S. Geravesh and V. Rupapara, “Artificial neural networks for human activity recognition using sensor based dataset,” Multimedia Tools and Applications, vol. 82, no. 10, pp. 14815–14835, Oct. 2022, doi: 10.1007/s11042-022-13716-z.
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,” IEEE Transactions on Instrumentation and Measurement, vol. 72, pp. 1–12, 2023, doi: 10.1109/tim.2023.3273673.
D.-A. Nguyen, C. Pham, and N.-A. Le-Khac, “Virtual Fusion With Contrastive Learning for Single-Sensor-Based Activity Recognition,” IEEE Sensors Journal, vol. 24, no. 15, pp. 25041–25048, Aug. 2024, doi: 10.1109/jsen.2024.3412397.
Y. Zhou et al., “AutoAugHAR: Automated Data Augmentation for Sensor-based Human Activity Recognition,” Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, vol. 8, no. 2, pp. 1–27, May 2024, doi: 10.1145/3659589.
H. Park, G. H. Lee, J. Han, and J. K. Choi, “Multiclass autoencoder-based active learning for sensor-based human activity recognition,” Future Generation Computer Systems, vol. 151, pp. 71–84, Feb. 2024, doi: 10.1016/j.future.2023.09.029.
Ray, L. S. S., Geißler, D., Liu, M., Zhou, B., Suh, S., & Lukowicz, P. “ALS-HAR: Harnessing Wearable Ambient Light Sensors to Enhance IMU-based HAR”. arXiv preprint arXiv:2408.09527. (2024).
P. K. Lakineni, R. Balamanigandan, T. Rajesh Kumar, V. Sathyendra Kumar, R. Mahaveerakannan, and C. Swetha, “Securing the E-records of Patient Data Using the Hybrid Encryption Model with Okamoto–Uchiyama Cryptosystem in Smart Healthcare,” Proceedings of Data Analytics and Management, pp. 499–511, 2023, doi: 10.1007/978-981-99-6553-3_38.
A. Sasi Kumar, T. Rajesh Kumar, R. Balamanigandan, R. Meganathan, R. Karwa, and R. Mahaveerakannan, “Cuttlefish Algorithm-Based Deep Learning Model to Predict the Missing Data in Healthcare Application,” Proceedings of Data Analytics and Management, pp. 513–528, 2023, doi: 10.1007/978-981-99-6553-3_39.
F. Palumbo, C. Gallicchio, R. Pucci, and A. Micheli, “Human activity recognition using multisensor data fusion based on Reservoir Computing,” Journal of Ambient Intelligence and Smart Environments, vol. 8, no. 2, pp. 87–107, Mar. 2016, doi: 10.3233/ais-160372.
Z. Wang, T. Liu, X. Wu, and C. Liu, “A diagnosis method for imbalanced bearing data based on improved SMOTE model combined with CNN-AM,” Journal of Computational Design and Engineering, vol. 10, no. 5, pp. 1930–1940, Aug. 2023, doi: 10.1093/jcde/qwad081.
L. Sun, Y. Wang, Y. Ren, and F. Xia, “Path signature-based XAI-enabled network time series classification,” Science China Information Sciences, vol. 67, no. 7, Jun. 2024, doi: 10.1007/s11432-023-3978-y.
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Karthikeyan R
Karthikeyan R
Department of Computer Science and Engineering, Artificial Intelligence and Data Analytics, Sri Ramachandra Institute of Higher Education and Research, Chennai, Tamil Nadu, India.
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Cite this article
Karthikeyan R, Usha S, Dineshbabu V, Jeena R, Anitha Govindaram and Jegatheesan
A, “Deep Learning and Recurrent Signature Based Classification for Sensor Based HAR: Addressing
Explainability and Complexity in 5G Networks”, Journal of Machine and Computing, pp. 1058-1068,
October 2024. doi:10.53759/7669/jmc202404098.
