Comparative Analysis of Various Hybrid Neural Network Models to Determine Human Activities using Inertial Measurement Units
Keywords:
Human Activity Recognition, IMU, Hybrid Deep Neural network, Wearables, Phones, CNN, BiLSTM, BiGRUAbstract
Human Activity Recognition (HAR) holds a pivotal role in a diverse range of applications that impact various aspects of human life. Advancements in sensor technology and the integration of IoT have expanded the scope of research in HAR through the utilization of deep learning algorithms. End-to-end learning is provided by the advanced deep learning paradigm from complex and amorphous data. Smartphones and IoT wearables are now widely employed in Ambience Assisted Living, e-health monitoring, fitness tracking, biometrics, smart cities, IIoT and other applications. Wearables and Smartphones employ Inertial measurement units (IMU) for the detection of human activities. This research proposes different hybrid neural network model built using GRU, bidirectional GRU, LSTM and bidirectional LSTM with CNN. WISDM, USCHAD, and MHEALTH activity recognition datasets are used to test the method. The hybrid model outperforms the other activity recognition algorithms in terms of accuracy.
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Gupta N, Gupta SK, Pathak RK, Jain V, Rashidi P, Suri JS. Human activity recognition in artificial intelligence framework: a narrative review. Artif Intell Rev. 2022;55(6):4755-4808. doi:10.1007/s10462-021-10116-x
Yang, J.B., Nguyen, M.N., San, P.P., Li, X.L., Krishnaswamy, S., 2015. Deep convolutional neural networks on multichannel time series for human activity recognition, in: IJCAI, Buenos Aires, Argentina, pp. 25–31
Bengio, Y., 2013. Deep learning of representations: Looking forward, in: International Conference on Statistical Language and Speech Processing, Springer. pp. 1–37
M. S. Siraj and M. A. R. Ahad, "A Hybrid Deep Learning Framework using CNN and GRU-based RNN for Recognition of Pairwise Similar Activities," 2020 Joint 9th International Conference on Informatics, Electronics & Vision (ICIEV) 2020 4th International Conference on Imaging, Vision & Pattern Recognition (icIVPR), 2020, pp. 1-7, doi: 10.1109/ICIEVicIVPR48672.2020.9306630.
Morales, F.J.O., Roggen, D., 2016. Deep convolutional feature transfer across mobile activity recognition domains, sensor modalities and locations, in: Proceedings of the 2016 ACM International Symposium on Wearable Computers, ACM. pp. 92–99
Ravi D., Wong, C., Lo, B., Yang, G.Z., 2016. Deep learning for human activity recognition: A resource efficient implementation on low-power devices, in: Wearable and Implantable Body Sensor Networks (BSN), 2016 IEEE 13th International Conference on, IEEE. pp. 71–76.
Chen, Y., Xue, Y., 2015. A deep learning approach to human activity recognition based on single accelerometer, in: Systems, Man, and Cybernetics (SMC), 2015 IEEE International Conference on, IEEE. pp. 1488–1492.
Al-sheikh, M.A., Selim, A., Niyato, D., Doyle, L., Lin, S., Tan, H.P., 2016. Deep activity recognition models with triaxial accelerometers. AAAI workshop .
Park, Jiho et al. “Deep neural networks for activity recognition with multi-sensor data in a smart home.” 2018 IEEE 4th World Forum on Internet of Things (WF-IoT) (2018): 155-160.
Chen, J.X., Jiang, D., & Zhang, Y.N. (2019). A Hierarchical Bidirectional GRU Model With Attention for EEG-Based Emotion Classification. IEEE Access, 7, 118530-118540.
Murad A, Pyun J-Y. Deep Recurrent Neural Networks for Human Activity Recognition. Sensors. 2017; 17(11):2556. https://doi.org/10.3390/s17112556
L. Al-awneh, B. Mohsen, M. Al-Zinati, A. Shatnawi and M. Al-Ayyoub, "A Comparison of Unidirectional and Bidirectional LSTM Networks for Human Activity Recognition," 2020 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), 2020, pp. 1-6, doi: 10.1109/PerComWorkshops48775.2020.9156264.
Q. Tao, F. Liu, Y. Li and D. Sidorov, "Air Pollution Forecasting Using a Deep Learning Model Based on 1D Convnets and Bidirectional GRU," in IEEE Access, vol. 7, pp. 76690-76698, 2019, doi: 10.1109/ACCESS.2019.2921578.
Zartasha Baloch, Faisal Karim Shaikh, Mukhtiar Ali Unar, "CNN-LSTM-Based Late Sensor Fusion for Human Activity Recognition in Big Data Networks", Wireless Communications and Mobile Computing, vol. 2022, ArticleID 3434100, 16 pages, 2022
Ding, Jianyang & Wang, Yong. (2019). WiFi CSI based Human Activity Recognition Using Deep Recurrent NeuralNetwork. IEEE Access. PP.1-1.10.1109/ACCESS.2019.2956952
Thappa, Kshav & Zubaer, Md & Lamichhane, Barsha & Yang, Sung-Hyun. (2020). A Deep Machine Learning Method for Concurrent and Interleaved Human Activity Recognition. Sensors. 20. 5770. 10.3390/s20205770.
Chen J, Sun Y, Sun S. Improving Human Activity Recognition Performance by Data Fusion and Feature Engineering. Sensors. 2021; 21(3):692. https://doi.org/10.3390/s21030692
Abbaspour S, Fotouhi F, Sedaghatbaf A, Fotouhi H, Vahabi M, Linden M. A Comparative Analysis of Hybrid Deep Learning Models for Human Activity Recognition. Sensors. 2020; 20(19):5707. https://doi.org/10.3390/s20195707
Mi Zhang and Alexander A. Sawchuk. 2012. USC-HAD: a daily activity dataset for ubiquitous activity recognition using wearable sensors. In Proceedings of the 2012 ACM Conference on Ubiquitous Computing (UbiComp '12). Association for Computing Machinery, New York, NY, USA, 1036–1043. DOI:https://doi.org/10.1145/2370216.2370438
M. Shoaib, H. Scholten and P. J. M. Havinga, "Towards Physical Activity Recognition Using Smartphone Sensors," 2013 IEEE 10th International Conference on Ubiquitous Intelligence and Computing and 2013 IEEE 10th International Conference on Autonomic and Trusted Computing, 2013, pp. 80-87, doi: 10.1109/UIC-ATC.2013.43.
Bao L., Intille S.S. (2004) Activity Recognition from User- Annotated Acceleration Data. In: Ferscha A., Mattern F. (eds) Pervasive Computing. Pervasive 2004. Lecture Notes in Computer Science, vol 3001. Springer, Berlin,Heidelberg. https://doi.org/10.1007/978-3-540-24646-6_1.
Rahn, V. X., Zhou, L., Klieme, E., & Arnrich, B. (2021). Optimal Sensor Placement for Human Activity Recognition with a Minimal Smartphone-IMU Setup. In SENSORNETS (pp. 37-48).
Dehghani, A., Sarbishei, O., Glatard, T. and Shihab, E., 2019. A quantitative comparison of overlapping and non- overlapping sliding windows for human activity recognition using inertial sensors. Sensors, 19(22), p.5026.
Janidarmian, Majid & Roshan Fekr, Atena & Radecka, Katarzyna & Zilic, Zeljko. (2017). A Comprehensive Analysis on Wearable Acceleration Sensors in Human Activity Recognition. Sensors. 17. 529 10.3390/s17030529.
Banos, Oresti & Galvez, Juan & Damas, Miguel & Pomares, Hector & Rojas, Ignacio. (2014). Window Size Impact in Human Activity Recognition. Sensors (Basel, Switzerland). 14. 6474-99 10.3390/s140406474.
Zheng, Xiaochen, Meiqing Wang, and Joaquín Ordieres-Meré. 2018. "Comparison of Data Preprocessing Approaches for Applying Deep Learning to Human Activity Recognition in the Context of Industry 4.0" Sensors 18, no. 7: 2146.
https://doi.org/10.3390/s18072146
D. Ravi, C. Wong, B. Lo and G. Yang, "Deep learning for human activity recognition: A resource efficient implementation on low-power devices," 2016 IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN), 2016, pp. 71-76, doi: 10.1109/BSN.2016.7516235
Atrey, Pradeep & Hossain, M. & El Saddik, Abdulmotaleb & Kankanhalli, Mohan. (2010). Multimodal fusion for multimedia analysis: A survey. Multimedia Syst 16. 345-379.10.1007/s00530-010-0182-0.
Liggins, M.E.; Hall, D.L.; Llinas, J. Handbook of Multisensor Data Fusion:Theory and Practice; CRC Press: Boca Raton, FL, USA, 2009
USC-HAD Dataset. http://sipi.usc.edu/HAD
Weiss, Gary. WISDM Smartphone and Smartwatch Activity and Biometrics Dataset. UCI Machine Learning Repository,2019,https://doi.org/10.24432/C5HK59. http://archive.ics.uci.edu/ml/datasets/mhealth+dataset
Banos,Oresti, Garcia,Rafael, and Saez,Alejandro. (2014). MHEALTH Dataset. UCI Machine Learning Repository. https://doi.org/10.24432/C5TW22.
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