A Survey on Wearable Technology: History, State-of-the-Art and Current Challenges

Wearable devices provide an alternative pathway to clinical diagnostics by exploiting various physical, chemical and biological sensors to mine physiological (biophysical and/or biochemical) information in real time (preferably, continuously) and in a non-invasive or minimally invasive manner. These sensors can be worn in the form of glasses, jewellery, face masks, wristwatches, fitness bands, tattoo-like devices, bandages or other patches, and textiles. Wearables such as smartwatches have already proved their capability for the early detection and monitoring of the progression and treatment of various diseases, such as COVID-19 and Parkinson disease, through biophysical signals. Next-generation wearable sensors that enable the multimodal and/or multiplexed measurement of physical parameters and biochemical markers in real time and continuously could be a transformative technology for diagnostics, allowing for high-resolution and time-resolved historical recording of the health status of an individual. In this Review, we examine the building blocks of such wearable sensors, including the substrate materials, sensing mechanisms, power modules and decision-making units, by reflecting on the recent developments in the materials, engineering and data science of these components. Finally, we synthesize current trends in the field to provide predictions for the future trajectory of wearable sensors. 

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End-to-end design of wearable sensors

The field of information security and privacy is currently attracting a lot of research interest. Simultaneously, different computing paradigms from Cloud computing to Edge computing are already forming a unique ecosystem with different architectures, storage, and processing capabilities. The heterogeneity of this ecosystem comes with certain limitations, particularly security and privacy challenges. This systematic literature review aims to identify similarities, differences, main attacks, and countermeasures in the various paradigms mentioned. The main determining outcome points out the essential security and privacy threats. The presented results also outline important similarities and differences in Cloud, Edge, and Fog computing paradigms. Finally, the work identified that the heterogeneity of such an ecosystem does have issues and poses a great setback in the deployment of security and privacy mechanisms to counter security attacks and privacy leakages. Different deployment techniques were found in the review studies as ways to mitigate and enhance security and privacy shortcomings.

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A Survey of Security in Cloud, Edge, and Fog Computing

Application of artificial intelligence in wearable devices: Opportunities and challenges.

Application of artificial intelligence in wearable devices: Opportunities and challenges

Research and development in Collaborative Indoor Positioning Systems (CIPSs) is growing steadily due to their potential to improve on the performance of their non-collaborative counterparts In contrast to the outdoors scenario, where Global Navigation Satellite System is widely adopted, in (collaborative) indoor positioning systems a large variety of technologies, techniques, and methods is being used Moreover, the diversity of evaluation procedures and scenarios hinders a direct comparison This paper presents a systematic review that gives a general view of the current CIPSs A total of 84 works, published between 2006 and 2020, have been identified These articles were analyzed and classified according to the described system’s architecture, infrastructure, technologies, techniques, methods, and evaluation The results indicate a growing interest in collaborative positioning, and the trend tend to be towards the use of distributed architectures and infrastructure-less systems Moreover, the most used technologies to determine the collaborative positioning between users are wireless communication technologies (Wi-Fi, Ultra-WideBand, and Bluetooth) The predominant collaborative positioning techniques are Received Signal Strength Indication, Fingerprinting, and Time of Arrival/Flight, and the collaborative methods are particle filters, Belief Propagation, Extended Kalman Filter, and Least Squares Simulations are used as the main evaluation procedure On the basis of the analysis and results, several promising future research avenues and gaps in research were identified

Collaborative Indoor Positioning Systems: A Systematic Review.

Anonymous attribute-based credentials in collaborative indoor positioning systems

Positioning people indoors has known an exponential growth in the last few years, especially thanks to Bluetooth Low Energy (BLE) technology and the Received Signal Strength Indicator (RSSI) technique. This approach is available in wearable devices, is easy to implement and has energy consumption advantages. However, the relative distance calculation is inaccurate, as the strength of BLE signals significantly fluctuates in indoor environments. Typical coping mechanisms, such as path-loss propagation models, require mathematical modeling and time-consuming calibration, that depend on the environment. In this paper, we propose a novel distance estimator based on RSSI-fuzzy classification of the BLE signals. Fuzzy-logic improves the robustness and accuracy of RSSI-based estimators, does not require an explicit propagation model and is easy and intuitive to (graphically) tune (using basic statistical analysis). The estimator’s suitability and the feasibility to provide an easy implementation were experimentally demonstrated in two scenarios with real-world data.

Smartphone Distance Estimation Based on RSSI-Fuzzy Classification Approach

The demand to enhance distance estimation and location accuracy in a variety of Non-Line-of-Sight (NLOS) indoor environments has boosted investigation into infrastructure-less ranging and collaborative positioning approaches. Unfortunately, capturing the required measurements to support such systems is tedious and time-consuming, as it requires simultaneous measurements using multiple mobile devices, and no such database are available in literature. This article presents a Bluetooth Low Energy (BLE) database, including Received-Signal-Strength (RSS) and Ground-Truth (GT) positions, for indoor positioning and ranging applications, using mobile devices as transmitters and receivers. The database is composed of three subsets: one devoted to the calibration in an indoor scenario; one for ranging and collaborative positioning under Non-Line-of-Sight conditions; and one for ranging and collaborative positioning in real office conditions. As a validation of the dataset, a baseline analysis for data visualization, data filtering and collaborative distance estimation applying a path-loss based on the Levenberg-Marquardt Least Squares Trilateration method are included. 

/pdf/mobile-device-based-bluetooth-low-energy-database-for-range-2vpvjzzk.pdf

Pavel Pascacio

Papers

A Survey on Wearable Technology: History, State-of-the-Art and Current Challenges

Collaborative Indoor Positioning Systems: A Systematic Review.

Anonymous attribute-based credentials in collaborative indoor positioning systems

Smartphone Distance Estimation Based on RSSI-Fuzzy Classification Approach

Mobile device-based Bluetooth Low Energy Database for range estimation in indoor environments