Zitong Zhou
ABSTRACT
The development of human activity monitoring has allowed for simulation and prediction in a variety of application scenarios. The battery-free wearable sensor is performed on sternum level clothing, mainly by script execution. It is essential to monitor human health, sleep, etc. With the increasing population in the world, the aging population is also becoming larger and larger. Hospitals and nursing homes are in great need to prevent injuries such as older people falling indoors. Existing methods construct the wireless wearable human body multi-physiological parameter monitoring system with wireless sensor network and central monitoring module unit. However, this system has a high false alarm rate and low accuracy in predicting human activities.To solve this problem, we constructed an activity detection method based on several machine learning algorithms, including Decision Tree,Gradient Boosting Decision Tree (GBDT), Adaboosting, Bagging and Random Forest. Although the decision tree is quite intuitionistic, ensemble learning models can improve the accuracy of prediction significantly by combining a lot of decision trees. By comparing the errors of each model, the most suitable monitoring model for predicting the physiological events of the elderly was selected. It is found that Random Forest outperforms the other methods, and achieves 99.97% on the training data and 98.8% accuracy on testing data, respectively.
- Muhammad, A. , Klein, N. , Laerhoven, K. V. , & David, K. . (2011). A Feature Set Evaluation for Activity Recognition with Body-Worn Inertial Sensors. Workshop on Interactive Human Behavior Analysis in Open Or Public Spaces.Google Scholar
- Yusuke Takei, Kiyoshi Matsumoto, & Isao Shimoyama. (2016). Wearable sweat monitoring sensor based on Ionic Liquid Gel. 2016 IEEE 29th International Conference on Micro Electro Mechanical Systems (MEMS). IEEE.Google ScholarCross Ref
- Torres, R. L. S. , Ranasinghe, D. C. , & Shi, Q. . (2014). Evaluation of Wearable Sensor Tag Data Segmentation Approaches for Real Time Activity Classification in Elderly. Mobile and Ubiquitous Systems: Computing, Networking, and Services. Mobiquitous 2013. Springer International Publishing.Google Scholar
- Shinmoto Torres Roberto, Visvanathan Renuka, Hoskins Stephen, van den Hengel Anton, & Ranasinghe Damith. . Effectiveness of a batteryless and wireless wearable sensor system for identifying bed and chair exits in healthy older people. Sensors, 16(4), 546-.Google Scholar
- Asanga Wickramasinghe, & Damith C. Ranasinghe. (2015). Recognising Activities in Real Time Using Body Worn Passive Sensors With Sparse Data Streams: To Interpolate or Not To Interpolate?. 12th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services.Google Scholar
- Shangguang Wang, Cunqun Fan, Ching-Hsien Hsu, Qibo Sun, & Fangchun Yang. (2017). A vertical handoff method via self-selection decision tree for internet of vehicles. IEEE Systems Journal, 10(3), 1183-1192.Google ScholarCross Ref
- Patrick, J., Heagerty, Scott, L., & Zeger. . Marginalized multilevel models and likelihood inference.Google Scholar
- Freund, Y. , & Schapire, R. E. . (1995). A decision-theoretic generalization of on-line learning and an application to boosting. Proceedings of the Second European Conference on Computational Learning Theory. Springer-Verlag.Google Scholar
- Breiman, & Leo. . Bagging predictors. Machine Learning, 24(2), 123-140.Google Scholar
- Tin Kam Ho. (1995). Random decision forests. Document Analysis and Recognition, 1995. Proceedings of the Third International Conference on. IEEE Computer Society.Google ScholarCross Ref
- Chen, T., Tong, H., & Benesty, M. (2016). Xgboost: extreme gradient boosting.Google ScholarDigital Library
- Kaminski, Bogumil, Jakubczyk, Michal, Szufel, & Przemyslaw. (2018). A framework for sensitivity analysis of decision trees. Central European journal of operations research: CEJOR.]Google Scholar
- Kégl, Balázs. (2013). The return of adaboost.mh: multi-class hamming trees. Computer Science.Google Scholar
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