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A Novel Algorithm for Healthcare Sensor Data Fusion and Anomaly Detection

A Novel Algorithm for Healthcare Sensor Data Fusion and Anomaly Detection
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Author(s): Anshit Mukherjee (Abacus Institute of Engineering and Management, India), Biswadip Basu Mallik (Institute of Engineering and Management, University of Engineering and Management, India), Avishek Gupta (Abacus Institute of Engineering and Management, India)and Monalisa Halder (Abacus Institute of Engineering and Management, India)
Copyright: 2025
Pages: 30
Source title: Exploration of Transformative Technologies in Healthcare 6.0
Source Author(s)/Editor(s): Piyush Kumar (IILM University, Gurugram, India), Pankaj Rahi (Institute of Health Management and Research, Bangalore, India), S.D. Gupta (Institute of Health Management and Research, Bangalore, India), Kirti Udayai (Max Healthcare Institute Limited, India)and Prashant Singh (Independent Researcher, India)
DOI: 10.4018/979-8-3693-7210-4.ch008

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Abstract

Health care sensor data analytics is one of the most disruptive innovations in personal health care since it uses information from sensors to improve patient care. In this chapter, a new algorithm for Multi-Modal Sensor Data Fusion and Anomaly Detection is presented; some important issues, including data authenticity, confidentiality and integrity, are discussed. The proposed method builds on machine learning and artificial intelligence to deal with large sets of data to comprehend in order to enhance diagnosis and solutions oriented towards patient care. The algorithm is then validated through a series of tests and outperforms different approaches used in the same field to diagnose problems by analyzing sensor data. The findings strongly argue for a robust data infrastructure that covers acquisition, distribution, archival, analysis, and presentation. Thus, this paper underlines deficiencies of current approaches and underscores the applicability of analytical techniques for radical transformation of healthcare processes alongside with adherence to ethical considerations.

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