A framework for collaborative computing and multi-sensor data fusion in body sensor networks Giancarlo Fortino a,⇑ , Stefano Galzarano a , Raffaele Gravina a , Wenfeng Li b a Department of Informatics, Modeling, Electronics and Systems (DIMES), University of Calabria, Via P. Bucci, 87036 Rende, CS, Italy b School of Logistics Engineering, Wuhan University of Technology, 430063 Wuhan, China article info Article history: Available online xxxx Keywords: Body sensor networks SPINE Collaborative computing Multi-sensor data fusion Emotion detection Handshake detection abstract Body Sensor Networks (BSNs) have emerged as the most effective technology enabling not only new e-Health methods and systems but also novel applications in human-centered areas such as electronic health care, fitness/welness systems, sport performance monitoring, interactive games, factory workers monitoring, and social physical interaction. Despite their enormous potential, they are currently mostly used only to monitor single individuals. Indeed, BSNs can proactively interact and collaborate to foster novel BSN applications centered on collaborative groups of individuals. In this paper, C-SPINE, a frame- work for Collaborative BSNs (CBSNs), is proposed. CBSNs are BSNs able to collaborate with each other to fulfill a common goal. They can support the development of novel smart wearable systems for cyberphysical pervasive computing environments. Collaboration therefore relies on interaction and synchronization among the CBSNs and on collaborative distributed computing atop the collaborating CBSNs. Specifically, collaboration is triggered upon CBSN proximity and relies on service-specific proto- cols allowing for managing services among the collaborating CBSNs. C-SPINE also natively supports multi-sensor data fusion among CBSNs to enable joint data analysis such as filtering, time-dependent data integration and classification. To demonstrate its effectiveness, C-SPINE is used to implement e-Shake, a collaborative CBSN system for the detection of emotions. The system is based on a multi- sensor data fusion schema to perform automatic detection of handshakes between two individuals and capture of possible heart-rate-based emotion reactions due to the individuals’ meeting. Ó 2014 Elsevier B.V. All rights reserved. 1. Introduction In the last years, the progress of science and medicine allowed to considerably augment the average life expectancy. On the basis of recent studies, in 2050 life expectancy will be 80 years for men and 85 years for women whereas the population of the World hav- ing more than 65 years is projected to augment from 500 millions to one billions in 2030 [1]. The augmentation of elderly population will largely and specifically affect any health care system. At the same time, especially in the more developed countries, there is an always growing interest in maintaining, and improving the quality of life, and consequently health and wellness. ICT technol- ogies and, in particular, domain-specific Wireless Sensor Networks [2] named Wireless Body Sensor Networks (BSNs) [3,4] have enor- mous possibilities for positively affecting the daily life of people. A BSN is constituted by a set of programmable wearable sensors that communicate with a local and/or personal coordinator device (or simply coordinator) to provide real-time, continuous and non- invasive monitoring of assisted livings. The sensor nodes include computation, memory, and wireless communication capabilities, a constrained power supply (usually a battery), and different on-board sensors based on specific physical transducer(s). Usual dimensions of physiological sensing, for strictly medical and non-medical purposes, include body motion, skin temperature, heartbeat rate, muscular activity, breathing volume and rate, skin conductivity, and brain activity. Wearable sensors can be positioned on the skin and/or in the garments. The coordinator is usually a tablet/smartphone or a personal computer, and can specifically enable monitoring in realtime, remote storage of long term, and analysis both online and offline. BSNs can facilitate and empower many human-centered application domains such as elderly assistance at home, early prevention or detection of diseases (e.g. heart attacks, Parkinson, diabetes), post trauma rehabilitation after a surgery, detection of gestures and motions, fitness, medical assistance in disaster events, cognitive and emotion recognition. Moreover, BSNs are effective http://dx.doi.org/10.1016/j.inffus.2014.03.005 1566-2535/Ó 2014 Elsevier B.V. All rights reserved. ⇑ Corresponding author. Tel.: +39 0984 494063; fax: +39 0984 494713. E-mail addresses: g.fortino@unical.it (G. Fortino), sgalzarano@deis.unical.it (S. Galzarano), rgravina@deis.unical.it (R. Gravina), liwf@whut.edu.cn (W. Li). Information Fusion xxx (2014) xxx–xxx Contents lists available at ScienceDirect Information Fusion journal homepage: www.elsevier.com/locate/inffus Please cite this article in press as: G. Fortino et al., A framework for collaborative computing and multi-sensor data fusion in body sensor networks, Informat. Fusion (2014), http://dx.doi.org/10.1016/j.inffus.2014.03.005