Exploring the Impact of Commercial Wearable Activity Trackers on Body Awareness and Body Representations: A Mixed-Methods Study on Self-tracking

Wearable trackers are believed to enhance users’ self-knowledge, but their impact on the relationship that people have with their own bodies is relatively unexplored. This study aims to shed light on the potential of physiological data collected by a commercial wearable activity tracker to influence how users relate with their own bodies, specifically their body awareness, body image, body consciousness, and body surveillance. Additionally, the study seeks to determine whether this change in body perception improves or worsens the users’ relation with their own bodies. We recruited 321 first-time wearable users, including a control group. Participants in the experimental group (N = 225) completed a set of scales and questionnaires addressing body awareness and representations before and after wearing a Fitbit for four months, and 20 of them were further interviewed about their experience. The findings indicate that participants’ overall view of their bodies was not influenced by the device. However, the Fitbit did increase the awareness of bodily sensations, particularly for women. Moreover, we describe how participants made sense of the data displayed by the Fitbit, which was also used as an emotion-regulation tool. These results can contribute to the understanding of the impact of self-tracking technologies on the users’ perceptions of their own body and provide insights for future research in this field

Spartan Daily, October 25, 2016

Volume 147, Issue 23https://scholarworks.sjsu.edu/spartan_daily_2016/1063/thumbnail.jp

Solar Smart Hat for Augmented Reality Wearables

Some augmented reality (AR) wearables such as glasses include powerful features such as live transcription. However, such features are constrained by the relatively low processing power and limited battery capacity of AR wearables. , necessitated by the small size and limited space availability. AR wearables need to be taken off for charging since there is no convenient way to charge the device during use. This disclosure describes a smart hat, a head-worn accessory that includes a solar panel and battery. The smart hat can be connected to an AR wearable via an interface and can charge the AR wearable. The smart hat may also include a processor and memory that enables the AR wearable to offload processing or storage tasks to the smart hat by performing data transfer via the interface

The Internet of Things Will Thrive by 2025

This report is the latest research report in a sustained effort throughout 2014 by the Pew Research Center Internet Project to mark the 25th anniversary of the creation of the World Wide Web by Sir Tim Berners-LeeThis current report is an analysis of opinions about the likely expansion of the Internet of Things (sometimes called the Cloud of Things), a catchall phrase for the array of devices, appliances, vehicles, wearable material, and sensor-laden parts of the environment that connect to each other and feed data back and forth. It covers the over 1,600 responses that were offered specifically about our question about where the Internet of Things would stand by the year 2025. The report is the next in a series of eight Pew Research and Elon University analyses to be issued this year in which experts will share their expectations about the future of such things as privacy, cybersecurity, and net neutrality. It includes some of the best and most provocative of the predictions survey respondents made when specifically asked to share their views about the evolution of embedded and wearable computing and the Internet of Things

Human tagging

Several technologies—some new, some familiar—are being used to identify, authenticate, and track people on the go. Human tagging offers many novel benefits but also raises serious privacy concerns, and the laws, regulations, and policy guidelines regarding its practice are inconsistent and unevenly applied

Security and Privacy Analysis of Medical Wearables

The release of Google glass and the Apple and Samsung smart watches in the past two years pushed wearables to the forefront of technology. The realm of medical wearables will specifically see a huge growth as wearables become more common place. There are documented cases of security and privacy breaches in the five main potential breaching areas: wearable device itself, Bluetooth communication, smartphone or personal computer app, Wi-Fi data exchange, cloud storage. Privacy policies for individual wearables are not always in the best interest of the individual and government regulations on wearables security does not always fully vet wearables. Interviews with industry professionals, both clinical and research, concluded that doctors are not very knowledgeable about wearables and are not very worried about security, the public does not understand security of these devices, and the security concerns should not stop the progress that is being made in this field

Edge and cluster computing as enabling infrastructure for Internet of Medical Things

(c) 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.The continuous adoption of fitness and medical smart sensors are boosting the development of Internet of Medical Things (IoMT), reshaping and revolutionizing Healthcare. This digital transformation is paving the way to new forms of care based on real-time analysis of huge amounts of data produced by sensors, which is seen as a basis for improving clinical efficiency and helping to save lives. A medical sensor typically produces several KBs of data per second so the collection and analysis of these data can be approached with Big Data technologies. The aim of this paper is to present and evaluate a hybrid architecture for real-time anomaly detection from data streams coming from sensors attached to patients. The architecture includes an edge computing data staging platform based on Raspberry Pi 3 for data logging, data transformation in RDF triple and data streaming towards a cluster computing running Apache Kafka for collecting RDFStreams, Apache Flink for running a parallel version of the Hierarchical Temporal Memory algorithm and Cassandra for data storing. The different layers of the architecture have been evaluated in terms of both CPU performance and memory usage using the REALDISP dataset.Peer ReviewedPostprint (author's final draft