Multi-sensor fusion in body sensor networks: State-of-the-art and research challenges

Raffaele Gravina, Parastoo Alinia, Hassan Ghasemzadeh, Giancarlo Fortino

Research output: Contribution to journalArticlepeer-review

472 Scopus citations

Abstract

Body Sensor Networks (BSNs) have emerged as a revolutionary technology in many application domains in health-care, fitness, smart cities, and many other compelling Internet of Things (IoT) applications. Most commercially available systems assume that a single device monitors a plethora of user information. In reality, BSN technology is transitioning to multi-device synchronous measurement environments; fusion of the data from multiple, potentially heterogeneous, sensor sources is therefore becoming a fundamental yet non-trivial task that directly impacts application performance. Nevertheless, only recently researchers have started developing technical solutions for effective fusion of BSN data. To the best of our knowledge, the community is currently lacking a comprehensive review of the state-of-the-art techniques on multi-sensor fusion in the area of BSN. This survey discusses clear motivations and advantages of multi-sensor data fusion and particularly focuses on physical activity recognition, aiming at providing a systematic categorization and common comparison framework of the literature, by identifying distinctive properties and parameters affecting data fusion design choices at different levels (data, feature, and decision). The survey also covers data fusion in the domains of emotion recognition and general-health and introduce relevant directions and challenges of future research on multi-sensor fusion in the BSN domain.

Original languageEnglish (US)
Pages (from-to)1339-1351
Number of pages13
JournalInformation Fusion
Volume35
DOIs
StatePublished - May 1 2017
Externally publishedYes

Keywords

  • Data-level fusion
  • Decision-level fusion
  • Feature-level fusion
  • Human activity recognition
  • Multi-sensor data fusion

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Information Systems
  • Hardware and Architecture

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