Best of Bodynets 2014: Editorial

A wireless body sensor network (or simply BSN) is a networked collection of wearable (programmable) sensor nodes that can communicate among themselves and also with other smart devices and other ambient sensors [1], [2]. The sensor nodes have computation, storage, wireless transmission, and different sensing capabilities depending on the physical transducer(s) they are equipped with. Common physiological signals/data include body motion, skin temperature, heart rate, skin conductivity, brain and muscle activities, and biomarkers. Interconnection of the BSN nodes with smart devices such as smartphones and IoT devices means they can be easily incorporated with the existing or emerging network architectures. The BSNs could be also Cloud-based [3] to be supported by a flexible storage and processing infrastructure to perform both online and offline analyses of data streams. A wide range of application scenarios is enabled by BSN technologies, even though mHealth applications probably represent the most emblematic and diffused example. Specifically, BSN-based systems can be used to directly monitor several vital signs continuously and non-invasively, as tiny wireless sensors are placed on the skin and sometimes integrated with the garments. These signals can, in turn, allow inferring the onset or progression of different diseases (e.g., cardiovascular or neurodegenerative diseases) at an early stage or supporting rehabilitation, e.g., of lower or upper limbs after injuries. Moreover, BSNs are strategic enablers for many other application domains such as: e-Sport, e-Fitness, and e-Wellness, where the goal is to help people maintain physical and mental wellness; e-Factory to support monitoring the safety of employees working on the field; e-Social, where the objective is to monitor emotional states of stand-alone persons or of people while they meet. Much research effort is also focused on the use of smartphones to enable the aforementioned domains of m-Health, e-Wellness and m-Sociality. However many issues still exist in the BSN research area from several points of view: hardware (e.g., new biosensor boards), communications (e.g., more efficient MAC-level protocols), distributed software architectures (e.g., collaborative smartphoneand/or BSN-based platforms [4]), and advanced data processing algorithms. This special issue has been conceived as follow-up of the International Conference BodyNets 2014, London (United Kingdom), and to address some of the aforementioned issues. The six articles in this special issue are extended papers selected from those presented at Bodynets 2014. Such contributions address many research challenges related to BSNs and related applications. These include: smartphone-based methods and systems for real-time mHealth monitoring, wearable sensor-based methods and systems for the estimation of the circadian rhythm stability for supporting the realization of biomedical studies, novel methods for human action clustering based on BSN inertial data streams, definition of stress detection methods for just-in-time interventions in pervasive and affective applications, methods and system for the characterization of gait in patients affected by chronic diseases (e.g., Parkinson disease), methods and commercial tools for detecting daily activities through mood inference. The paper “Real-Time Tele-Monitoring of Patients with Chronic Heart-Failure Using a Smartphone: Lessons Learned” authored by Daniel Aranki, Gregorij Kurillo, Posu Yan, David M. Liebovitz, and Ruzena Bajcsy, focused on identifying system and usability challenges correlated to tele-monitoring of patients with Chronic Heart-Failure (CHF). Specifically, tele-monitoring is carried out via smartphones. The study was conducted on a pilot composed of 15 subjects and conceived to evaluate the feasibility of the proposed smartphone-based tele-monitoring in the real world and elicit its requirements, privacy implications, usability, and other challenges encountered by the participants and healthcare providers. Their system is able to assess patient activity based onminute-by-minute energy expenditure estimated from embedded accelerometers, monitor relative user location via GPS to track outdoors activity and measure walking distance. Moreover, it also allows for daily surveys to inquire about patients’ vital signs and general cardiovascular symptoms. Although the system was developed for CHF-affected individuals, the challenges, privacy considerations, and lessons learned from this pilot study apply to other chronic health conditions, such as diabetes and hypertension, which would certainly benefit from continuous monitoring throughm-Health technologies. In the paper entitled “AWearable Sensor Systemwith Circadian Rhythm Stability Estimation for Prototyping Biomedical Studies” by Benjamin L. Smarr, David C. Burnett, Sahar M.Mesri, Kristofer S.J. Pister, andLance J. Kriegsfeld, authors present an open-source, modifiable, and user-reconfigurable wearable sensor system capable of enabling biomedical investigations not feasible with currently-available devices. In their experimentations, the developed armband device was configured tomeasure skin temperature, light, and activity across days to detect internal circadian rhythms. Their monitoring is fundamental as the instability of internal circadian rhythms is linked to risk of many diseases, including mental illness such as depression, and has predictive power for personal affective state. Such novel sensor device enables long-term biomedical monitoring whereas the majority of G. Fortino is with the Department of Informatics, Modeling, Electronics and Systems, University of Calabria, Via P. Bucci, cubo 41C, Rende 87036, Italy. E-mail: [email protected]. G.-Z. Yang is with Hamlyn Centre, Imperial College, Exhibition Road, London SW7 2AZ, United Kingdom. E-mail: [email protected].