Multimode Data Fusion Based Remote Healthcare Framework

Based on the data analysis technology of Internet of things, big data and multimodal physiological characteristics, it is possible to realize more timely and accurate telemonitoring of health care by monitoring the information of human body and intelligent analysis of unstructured big data in real time. This paper proposes a remote healthcare framework based on multimodal health data fusion, which integrates the health data acquisition system based on body sensor network, big data fusion cloud computing and cloud service platform, and remote collaborative diagnosis and treatment service expert decision support system. The development and application of framework rely on many aspects, such as data acquisition, data fusion, cloud computing, expert decision support and so on. Human body sign information contains a variety of modal unstructured data, and data fusion is needed at different levels.

[1]  E. A. Mary Anita,et al.  A Survey of Big Data Analytics in Healthcare and Government , 2015 .

[2]  Tao Huang,et al.  Promises and Challenges of Big Data Computing in Health Sciences , 2015, Big Data Res..

[3]  Ishwarappa,et al.  A Brief Introduction on Big Data 5Vs Characteristics and Hadoop Technology , 2015 .

[4]  Otilia Kocsis,et al.  Telemonitoring system for home rehabilitation of patients with COPD , 2015, 2015 E-Health and Bioengineering Conference (EHB).

[5]  Mohammad Kazem Akbari,et al.  An effective model for store and retrieve big health data in cloud computing , 2016, Comput. Methods Programs Biomed..

[6]  William Holderbaum,et al.  Application of data fusion techniques and technologies for wearable health monitoring. , 2017, Medical engineering & physics.

[7]  George K. Karagiannidis,et al.  Efficient Machine Learning for Big Data: A Review , 2015, Big Data Res..

[8]  Shuai Huang,et al.  Integration of Data Fusion Methodology and Degradation Modeling Process to Improve Prognostics , 2016, IEEE Transactions on Automation Science and Engineering.

[9]  Giulio Iannello,et al.  On the remote detection of COPD-related worrisome events , 2016, 2016 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI).

[10]  Hassan Ghasemzadeh,et al.  Multi-sensor fusion in body sensor networks: State-of-the-art and research challenges , 2017, Inf. Fusion.

[11]  H. Ewald,et al.  A Zigbee-Based Wearable Physiological Parameters Monitoring System , 2012, IEEE Sensors Journal.

[12]  Carmen C. Y. Poon,et al.  Advances in multi-sensor fusion for body sensor networks: Algorithms, architectures, and applications , 2019, Inf. Fusion.