In recent years, wearable computing has been proliferating at a rapid pace and has enabled a wide range of applications such as mobile communication, sensory integration, behavioral modeling and health care monitoring, etc. Many challenges need to be addressed to enable the integration of multiple sensing/communication technologies for various applications with low energy consumption and low transmission rate. A key design requirement for future wearable health care services is to be able to provide prompt, reliable, secure ubiquitous access to health care services to users and health care providers anytime, anywhere. This special issue explores recent advances in designing, implementing, and deploying novel protocols, architectures, and applications in wearable computing for health care. This Special issue opens with an article on a big data platform for wearable data in health care, BA Semantic Big Data Platform for Integrating Heterogeneous Wearable Data in Healthcare,^ authored by Mezghani et al. The diversity, variety, and volume of wearable data related to healthcare make data processing and analytics increasingly difficult. The authors introduce a semantic big data architecture that extends the basic NIST Cloud and Big Data reference architectures with smart mechanisms based on ontology to give meaning to the silos of heterogeneous data. Hence, the authors propose a Wearable Healthcare Ontology that enables the aggregation of the distributed heterogeneous data coming from wearables to make better-informed health related decisions and create new valuable information. The second article, BMulti-Level MAC-Layer QoS Design in Wireless Body Area Network^, by Zhang et al., proposes a multiple level-based QoS design at the MAC layer for a tree topology structuredWBAN. The QoS provisioning is divided into three priority levels, i.e., priority of user, priority of data and priority over time. The time slot-based CSMA/CA mechanism in the beacon mode of IEEE 802.15.4 is used for the implementation of the QoS framework. The simulation results demonstrate the effectiveness of the multi-level QoS provisioning in WBAN. Wearable, non-invasive biosensors, with their portability and low cost, have been applied to a variety of clinical scenarios such as post-traumatic stress disorder, drug addiction, HIV therapy, stress and epilepsy. In the article, BiMStrong: Deployment of a Biosensor System to Detect Cocaine Use^, by Carreiro et al., a parameter trajectory description method to analyze the data (such as skin temperature, motion, etc.) collected from bio-sensors is proposed. The authors also presented the detection of drug use by analyzing several parameters. The research is well motivated by the problem of behavior interventions. The authors further prove the feasibility of use the mobile biosensor system in the detection of substance abuse. The authors have collected real data sets. In the future, using mobile bio-sensor system to improve human well-being is a very promising area aimed at reducing health care costs. The objective of wireless body area sensor networks is to make communication at and near the human body possible in order to measure different body attributes. In the article on wireless body area sensor networks, namely BA Fatigue Measuring Protocol for Wireless Body Area Sensor Networks^, Akram et al. propose a fatigue measurement The Special Issue for this editorial can be found at the following link: http://link.springer.com/journal/10916/39/12/page/1
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