Wearables Meet IoT: Synergistic Personal Area Networks (SPANs)

Abstract

Wearable monitoring and mobile health (mHealth) revolutionized healthcare diagnostics and delivery, while the exponential increase of deployed “things” in the Internet of things (IoT) transforms our homes and industries. “Things” with embedded activity and vital sign sensors that we refer to as “smart stuff” can interact with wearable and ambient sensors. A dynamic, ad-hoc personal area network can span multiple domains and facilitate processing in synergistic personal area networks—SPANs. The synergy of information from multiple sensors can provide: (a) New information that cannot be generated from existing data alone, (b) user identification, (c) more robust assessment of physiological signals, and (d) automatic annotation of events/records. In this paper, we present possible new applications of SPANs and results of feasibility studies. Preliminary tests indicate that users interact with smart stuff—in our case, a smart water bottle—dozens of times a day and sufficiently long to collect vital signs of the users. Synergistic processing of sensors from the smartwatch and objects of everyday use may provide user identification and assessment of new parameters that individual sensors could not generate, such as pulse wave velocity (PWV) and blood pressure. As a result, SPANs facilitate seamless monitoring and annotation of vital signs dozens of times per day, every day, every time the smart object is used, without additional setup of sensors and initiation of measurements. SPANs creates a dynamic “opportunistic bubble” for ad-hoc integration with other sensors of interest around the user, wherever they go. Continuous long-term monitoring of user’s activity and vital signs can provide better diagnostic procedures and personalized feedback to motivate a proactive approach to health and wellbeing.

ETSI SmartBAN Architecture: The Global Vision for Smart Body Area Networks

“…In SmartBAN network, frequency hopping is not supported, which deviates it, e.g., from BLE. [11] SmartBAN utilizes Gaussian frequency shift keying (GFSK) with a modulation index h = 0.5 and a bandwidth-bit period duration BT = 0.5 [4]. To improve transmission reliability, SmartBAN uses FEC coding but also repetition coding.…”

“…The biomechanical mobility model is based on the CM3B channel model, proposed by IEEE in [4]. However, model integrates the impact of human body shadowing, caused by the mobility, in the pathloss calculations.…”

“…As can be seen, e.g., from [1], [2], [3], [4], the use of BAN has been proposed for wearables quite a long time. As the wireless technology has become more mature and the miniaturization of components and devices has evolved, we assume that the BAN exploitation will grow fast during the coming years.…”

Fully-Textile, Wearable Chipless Tags for Identification and Tracking Applications

“…The Internet of Things (IoT) relies on smart environments, equipped with wireless sensor networks [1] and synergistic personal area networks [2,3], in which the user must be uniquely identified, while he/she interacts with the IoT infrastructure. One challenge, however, still remains the need to facilitate a seamless integration of IoT solutions with the surrounding context [4][5][6], and especially with people.…”

General Conceptual Framework of Future Wearables in Healthcare: Unified, Unique, Ubiquitous, and Unobtrusive (U4) for Customized Quantified Output

“…To create an efficient user-based profile, the user's general information consists of parameters such as age, sex, height, weight (which should be set manually), historical medical records, and lifestyle (e.g., being a smoker, which should be obtained via observation); the living location would also be helpful because of atmospheric conditions. However, profile customization includes personalized data and transmission of the physiological/psychological data, which raises security concerns and creates the potential risk of endangering personal privacy [10,54,55]. Although a number of approaches have been developed and studied for data security and privacy protection [56][57][58][59], the more recent and advanced approaches concentrate on data sharing and security via blockchain [60,61].…”