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Standardizing Smart Body Area Networks

A new European standard establishes interfaces, infrastructure, and access to any SmartBAN data/entities, promoting unified access to medical sensors and wearables.

Image by Gordon Johnson from Pixabay 

ETSI TC SmartBAN is a European Standards Organization Technical Committee that was created in 2013 for developing and maintaining an ETSI standard and specification, reports, guides, etc., relating to smart, wireless Body Area Networks (BANs). It addresses everything related to BANs in a holistic way, from tiny medical sensors located inside/on the body to remote end-user devices (e.g., hospital medical information system (MISs), caregivers’ tablets and smartphones) as well as from physical (PHY) and medium-access control (MAC) layersup to application layers. The addressed verticals are currently mainly related to eHealth, wellbeing/wellness, and personal safety, but other use cases related to other markets such as automotive are also envisioned. Figure 1 below presents an example of a considered ETSI SmartBAN end-to-end environment.

 

ETSI SmartBAN has recently published ETSI TS 103 327, a standard for Smart Body Area Networks, to establish standardized service and application interfaces and facilitators, APIs (application programming interfaces), and infrastructure for interoperability management and secure interaction and access to any SmartBAN data/entities, such as providing unified access to medical sensors/wearables measurements and information. The resulting SmartBAN reference architecture is a global and more integrated IoT reference architecture with data, device, network, semantic interoperability management, and embedded semantic analytics.

SmartBAN uses a set of low-power embedded devices, mainly sensors, wearables, or actuators, to collect and monitor vital data of a human being and his or her environment, but not exclusively. For each patient coming to an emergency room, ETSI TS 103 327 will make all medical history already available in a unified and uniquely interpretable format. It will thus enable an intelligent and accurate intervention.

Within the SmartBAN reference architecture, semantic interoperability is in particular addressed through a Web of Things (WoT) strategy, which:

  • Provides a sensor/wearable as a service that is described/represented through a common and generic reference model.
  • Enables cooperation between different SmartBANs.
  • Should lead to the creation of new cross-domain applications in order to integrate SmartBANs into the Web of Things and more global scenarios (cross domain use cases handling).

On the service and application side, generic service enablers and standardized APIs will provide secure interaction and access to SmartBAN data/entities (data transfer and sharing mechanisms included), embedded semantic analytics (device/edge/fog levels), automated alarm management, and distributed monitoring/control operations. This is a first step toward a horizontal management of Body Area Networks in multiple vertical application areas.

SmartBAN global IoT Reference Architecture (ETSI TS 103 327)

Figure 2 below depicts the high-level view of SmartBAN IoT Reference Architecture.

Figure 2 shows that the SmartBAN global IoT reference architecture is:

  • Directly mappable with the AIOTI (Alliance for Internet of Things Innovation) IoT High Level Architecture.
  • Multi-layer and Multi-Agent based.
  • oneM2M based for enabling network interoperability.
  • Semantic-based and specified on top of the SmartBAN reference model (see ETSI TS 103 378 and ETSI TS 103 327), thus addressing both data/informational and semantic interoperability. This reference model is composed of:
    • Unified metadata (i.e., data about data) describing in a unique way the measurement(s) provided by any sensors/wearables, which makes it unambiguously understandable/processable by any processes (agents), applications, or end-users (e.g., caregivers and helpers). These metadata are fully aligned with Bluetooth LE (Low Energy) profiles defined by the Continua Alliance (now taken up by Personal Connected Health Alliance) for medical devices.
    • Extended within a modular SmartBAN ontology that models extra environment-related information such as which device is providing the measurement, for which patient, at which geolocalization. This allows simple and complex semantic-based data analytics (device, edge, and fog levels) such as which patients have a body temperature below/beyond a certain threshold, which co-located patients have the same pathology.
  • Provided with both a common network level intermediation framework (oneM2M) and a unified message data format, thus addressing both network and syntactic interoperability.
  • Provided with cross-functional components for allowing non SmartBAN enabled environments to interoperate with SmartBAN.

Figure 2 also shows that the SmartBAN global IoT reference architecture in particular provides both generic network enablers/agents (as oneM2M Network Services Entities (NSEs), see Figure 2) and generic service enablers/agents (as oneM2M Common Services Entities (CSEs), see Figure 2) for in particular:

  • Allowing generic and secured interactions with any BAN devices (e.g., sensors, wearables, etc.). This is done through dedicated network-level interworking agents (or NSEs, see Figure 2), called Data Scanner Agents. Those agents were introduced for masking heterogeneity of medical devices and their data to any process, application, or end-user (patient, caregiver, and helper) that needs to interact with it through the SmartBAN reference architecture. A Bluetooth LE Data Scanner Agent has in particular been specified and implemented since most of actual commercial medical devices are using this technology.
  • Enabling non-SmartBAN-enabled environments to interoperate with SmartBAN. This can be handled by the SmartBAN coordinator via dedicated Data Scanner Agents or CSEs (see Figure 2).
  • BAN devices (sensors, wearables, actuators) discovery and BAN devices/data sharing, as services, at application levels, and through dedicated CSEs (see SmartBAN Service layer distributed agents depicted in Figure 2). This derives directly from the WoT (Web of Things) strategy adopted for full semantic interoperability handling.
  • Semantic data (i.e., BAN and BAN data/information, measurements included) sharing/management, embedded semantic analytics (device/edge/fog levels), automated alarm management and distributed monitoring/control operations (e.g., patient reminder, fall or stroke detection and corresponding alarm sending to caregivers/helpers, measurements similarity detection, etc.). This is handled through dedicated SmartBAN semantic layer distributed agents, such as the generic CSEs as depicted in Figure 2.

Finally, the SmartBAN Application layer depicted in Figure 2 will host all the IoT applications that are implementing a given SmartBAN application logic such as patient/caregiver notification, vital data monitoring, patient evaluation results, etc.

SmartBAN global IoT Reference Architecture clinical tests for an elderly-at-home monitoring and support use case: the SmartBAN showcase

In the context of both ETSI SmartBAN TC and EUREKA ITEA 13034 CareWare project activities, the SmartBAN global IoT reference architecture was carried out, validated, and clinically tested on an elderly at-home support/monitoring use case (SmartBAN showcase). This work was done in strong collaboration with the Office d’Hygiène Sociale, Nancy (OHS Nancy Lorraine, France). The SmartBAN-based monitoring and control platform used for the clinical tests was co-implemented (co-conception) with both OHS’s caregivers and patients. Figure 3 below presents the overview of the SmartBAN elderly-at-home support/monitoring platform implemented.

For the clinical tests, 25 elderly people were continuously monitored during one month. The building blocks of the SmatBAN IoT reference architecture that were used for the tests are depicted in Figure 4 below.

As shown in Figure 4, the elderly patient vital parameters/behaviors that were monitored and/or controlled through the implemented SmartBAN platform were:

  • BAN-side sensors: sensorTag (ambient temperature, humidity and air pressure), H10 Polar (real time heart rate), gyroscopes and accelerometers (fall detection, posture), light. All those sensors have been integrated in a wearable fabric belt built for the tests.
  • Home-side sensors: scale, oxymeter, tensiometer.

An android Smartphone (e.g., Galaxy S5/S7) was used as SmartBAN’s hub with patient’s GUI, and elderly people were remotely monitored via a SmartBAN monitoring and control IoT application installed on a tablet (see Figure 4). Local (smart BAN level) alarm management and control functionalities were handled through embedded semantic analytics (e.g., within the BAN’s hub).

All the aforementioned clinical tests were successfully completed, with a good acceptance level by both elderly patients and OHS caregivers. During the tests, the provided SmartBAN IoT application has in particular allowed patient behavior detection and/or validation (e.g., normal/average heart rate, sleep disorder, stress, vital/physiological data changes), critical situation detection and validation (e.g., body temperature or heart rate below/beyond a given threshold, fall), caregiver improved support in their daily work, and reinvolvement of patients in the medical-social processes.

Marc Girod-Genet

Marc Girod-Genet

Dr. Marc Girod-Genet received an engineering degree in Telecommunications and Advanced Techniques with distinction from EPITA, Paris, France, in 1994; a MS degree in Computer Science from Stevens Institute of Technology, Hoboken, New Jersey, USA, in 1995; and a PhD degree with distinction in Computer Science from the University of Versailles, France, in 2000.

Currently he is an Associate Professor in the Wireless Networks and Multimedia Services Department (RS2M) at the Institut Mines-Télécom - TELECOM SudParis (ex INT) and an Associate Researcher at CNRS (UMR 5157 SAMOVAR). His current research interests include wireless and mobile networks, sensor networks and BANs (Body Area Networks), eHealth and wellbeing/aging well, energy efficiency and Smart Grids, data/information modeling and ontology, data sharing environments (M2M, IoT, WoT), service discovery and service/context management environments, context awareness, and machine learning. In these areas, he has published more than 40 conference papers and has a patent in machine learning. In addition to his research achievements and extensive professional service, he is a dedicated teacher and in particular coordinates a certified training on IoT CES "Internet des objets (IoT), conception de solutions."

From March 2013, Dr. Girod-Genet has served as an ETSI rapporteur/contributor/expert within technical committees TC SmartBAN, TC SmartM2M and EP eHealth. Within the AIOTI (Alliance for Internet of Things Innovation), he has been an active member and contributor in working group on "IoT Standardization" (WG3). He also served as a Technical Program Committee member of IEEE conferences and as a reviewer for IEEE conferences and journals such as Communications Magazine and Transactions on Wireless Communications. He has been involved in organizing major international conferences symposia and events such as Globecom Wireless Communication symposium and ASWN workshop.

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