Xinyu Song
ABSTRACT
Mobile wearable sensors, with advantages such as real-time monitoring, portability, data sharing and connectivity, increased user engagement, and multifunctionality, have gained significant attention from researchers. Their application in identifying, interpreting, and evaluating human behaviors is becoming increasingly prominent. However, inconsistencies in experimental conditions among researchers and the complex dependencies between components and modules within systems may impact the reproducibility, comparability, stability, and maintainability of studies. To address these challenges, this paper presents a unified solution for research on mobile wearable sensors in the context of human behavior. Specifically, it introduces a middleware model called HAR-IMB. This model encompasses input sensor data, feature transformation, model selection, and prediction results. To substantiate the model's efficacy, the study conducts performance analyses on different deep learning models using the WISDM dataset and UCI-HAR dataset as examples. The experimental results demonstrate promising outcomes across various models. Furthermore, the middleware model exhibits scalability. In the future, it can be further enhanced and refined to adapt to more complex experimental environments and application scenarios.
- Shoya Ishimaru, Kensuke Hoshika, Kai Kunze, Koichi Kise, and Andreas Dengel. 2017. Towards reading trackers in the wild: detecting reading activities by EOG glasses and deep neural networks. In Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 ACM International Symposium on Wearable Computers (UbiComp '17). Association for Computing Machinery, New York, NY, USA, 704–711. https://doi.org/10.1145/3123024.3129271Google Scholar
Digital Library
- Karthikeswaran, D., Sengottaiyan, N., & Anbukaruppusamy, S. (2019). Automatic Human Activity Recognition in Video Surveillance System Using Versatile Quadric Activity Portion Classification Method. J. Medical Imaging Health Informatics, 9, 1393-1400.Google ScholarCross Ref
- Alanazi, Mubarak A., Abdullah K. Alhazmi, Osama Alsattam, Kara Gnau, Meghan Brown, Shannon Thiel, Kurt Jackson, and Vamsy P. Chodavarapu. 2022. "Towards a Low-Cost Solution for Gait Analysis Using Millimeter Wave Sensor and Machine Learning" Sensors 22, no. 15: 5470. https://doi.org/10.3390/s22155470.Google ScholarCross Ref
- Rex Liu, Albara Ah Ramli, Huanle Zhang, Esha Datta, Xin Liu (2022). An Overview of Human Activity Recognition Using Wearable Sensors: Healthcare and Artificial Intelligence. In: Tekinerdogan, B., Wang, Y., Zhang, LJ. (eds) Internet of Things – ICIOT 2021. ICIOT 2021. Lecture Notes in Computer Science(), vol 12993. Springer, Cham. https://doi.org/10.1007/978-3-030-96068-1_1.Google ScholarDigital Library
- Saurabh Gupta, 2021.Deep learning based human activity recognition (HAR) using wearable sensor data, International Journal of Information Management Data Insights, Volume 1, Issue 2,2021,100046,ISSN 2667-0968,https://doi.org/10.1016/j.jjimei.2021.100046.Google ScholarCross Ref
- Sasank Reddy, Min Mun, Jeff Burke, Deborah Estrin, Mark Hansen, and Mani Srivastava. 2010. Using mobile phones to determine transportation modes. ACM Trans. Sen. Netw. 6, 2, Article 13 (February 2010), 27 pages. https://doi.org/10.1145/1689239.1689243Google ScholarDigital Library
- Tim van Kasteren, Athanasios Noulas, Gwenn Englebienne, and Ben Kröse. 2008. Accurate activity recognition in a home setting. In Proceedings of the 10th international conference on Ubiquitous computing (UbiComp '08). Association for Computing Machinery, New York, NY, USA, 1–9. https://doi.org/10.1145/1409635.1409637Google ScholarDigital Library
- Donald J. Patterson, Dieter Fox, Henry Kautz, and Matthai Philipose. 2005. Fine-grained activity recognition by aggregating abstract object usage. In Proceedings of the Ninth IEEE International Symposium on Wearable Computers (ISWC '05). IEEE Computer Society, USA, 44–51. https://doi.org/10.1109/ISWC.2005.22Google ScholarDigital Library
- Paulo J. S. Ferreira, João M. P. Cardoso, and João Mendes-Moreira. 2020. kNN Prototyping Schemes for Embedded Human Activity Recognition with Online Learning. Computers 9, 4: 96. https://doi.org/10.3390/computers9040096Google ScholarCross Ref
- K. G. Manosha Chathuramali and R. Rodrigo, 2012. Faster human activity recognition with SVM, International Conference on Advances in ICT for Emerging Regions (ICTer2012), Colombo, Sri Lanka, pp. 197-203, doi: 10.1109/ICTer.2012.6421415.Google ScholarCross Ref
- Thomas Phan. 2014. Improving activity recognition via automatic decision tree pruning. In Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct Publication (UbiComp '14 Adjunct). Association for Computing Machinery, New York, NY, USA, 827–832. https://doi.org/10.1145/2638728.2641310Google ScholarDigital Library
- Nurwulan. N R, Selamaj. G, (2020).Random Forest for Human Daily Activity Recognition. Journal of Physics Conference Series, 1655:012087.Google ScholarCross Ref
- Saad Albawi, Tareq Abed Mohammed, Saad Al-Zawi, 2017. Understanding of a convolutional neural network, 2017 International Conference on Engineering and Technology (ICET), Antalya, Turkey, pp. 1-6, doi: 10.1109/ICEngTechnol.2017.8308186.Google ScholarCross Ref
- Klaus Greff, Rupesh K. Srivastava, Jan Koutník, Bas R. Steunebrink and Jürgen Schmidhuber, 2017. LSTM: A Search Space Odyssey, in IEEE Transactions on Neural Networks and Learning Systems, vol. 28, no. 10, pp. 2222-2232, Oct. 2017, doi: 10.1109/TNNLS.2016.2582924.Google ScholarCross Ref
- Hadim, S., Mohamed, N.,2016. Middleware: middleware challenges and approaches for wireless sensor networks, IEEE Distributed Systems Online, vol. 7, no. 3, pp. 1-1, March 2006, doi: 10.1109/MDSO.2006.19.Google ScholarDigital Library
Index Terms
- Deep Learning-Based Wearable Human Activity Recognition: Model and Performance Analysis
Recommendations
A survey on wearable sensor modality centred human activity recognition in health care
Highlights- Discuss limitations and strengths of different sensor modalities.
- Focus on the ...
AbstractIncreased life expectancy coupled with declining birth rates is leading to an aging population structure. Aging-caused changes, such as physical or cognitive decline, could affect people's quality of life, result in injuries, mental ...
Human activity recognition from multiple sensors data using deep CNNs
AbstractSmart devices with sensors now enable continuous measurement of activities of daily living. Accordingly, various human activity recognition (HAR) experiments have been carried out, aiming to convert the measures taken from smart devices into ...
Artificial intelligence in human activity recognition: a review
The various activities of human movements have been discussed for several years, such as sports activities, daily life activities, and so on. Their detection and classification have given crucial information about a person's behaviour and health status. ...
Comments