Hum Factors

Objective:To gather information on the (a) types of wearable sensors, particularly personal activity monitors, currently used by occupational safety and health (OSH) professionals, (b) potential benefits of using such technologies in the workplace, and (c) perceived barriers preventing the widespread adoption of wearable sensors in industry.Background:Wearable sensors are increasingly being promoted as a means to improve employee health and well-being and there is mounting evidence supporting their use as exposure assessment and personal health tools. Despite this, many workplaces have been hesitant to adopt these technologies.Methods:An electronic survey was emailed to 28,428 registered members of the American Society of Safety Engineers (ASSE) and 1,302 professionals certified by the Board of Certification in Professional Ergonomics (BCPE).Results:A total of 952 valid responses were returned. Over half of respondents described being in favor of using wearable sensors to track OSH-related risk factors and relevant exposure metrics at their respective workplaces. However, barriers including concerns regarding employee privacy/confidentiality of collected data, employee compliance, sensor durability, the cost/benefit ratio of using wearables, and good manufacturing practice requirements were described as challenges precluding adoption.Conclusion:The broad adoption of wearable technologies appears to depend largely on the scientific community\u2019s ability to successfully address the identified barriers.Application:Investigators may use the information provided to develop research studies that better address OSH practitioner concerns and that help technology developers operationalize wearable sensors to improve employee health and well-being.T42 OH008436/OH/NIOSH CDC HHSUnited States/2022-07-22T00:00:00Z29320232PMC930713011686vault:4300

Exploring the Unprecedented Privacy Risks of the Metaverse

Thirty study participants playtested an innocent-looking "escape room" game in virtual reality (VR). Behind the scenes, an adversarial program had accurately inferred over 25 personal data attributes, from anthropometrics like height and wingspan to demographics like age and gender, within just a few minutes of gameplay. As notoriously data-hungry companies become increasingly involved in VR development, this experimental scenario may soon represent a typical VR user experience. While virtual telepresence applications (and the so-called "metaverse") have recently received increased attention and investment from major tech firms, these environments remain relatively under-studied from a security and privacy standpoint. In this work, we illustrate how VR attackers can covertly ascertain dozens of personal data attributes from seemingly-anonymous users of popular metaverse applications like VRChat. These attackers can be as simple as other VR users without special privilege, and the potential scale and scope of this data collection far exceed what is feasible within traditional mobile and web applications. We aim to shed light on the unique privacy risks of the metaverse, and provide the first holistic framework for understanding intrusive data harvesting attacks in these emerging VR ecosystems

Enhancing data privacy and security in Internet of Things through decentralized models and services

exploits a Byzantine Fault Tolerant (BFT) blockchain, in order to perform collaborative and dynamic botnet detection by collecting and auditing IoT devices\u2019 network traffic flows as blockchain transactions. Secondly, we take the challenge to decentralize IoT, and design a hybrid blockchain architecture for IoT, by proposing Hybrid-IoT. In Hybrid-IoT, subgroups of IoT devices form PoW blockchains, referred to as PoW sub-blockchains. Connection among the PoW sub-blockchains employs a BFT inter-connector framework. We focus on the PoW sub-blockchains formation, guided by a set of guidelines based on a set of dimensions, metrics and bounds

Visions and Challenges in Managing and Preserving Data to Measure Quality of Life

Health-related data analysis plays an important role in self-knowledge, disease prevention, diagnosis, and quality of life assessment. With the advent of data-driven solutions, a myriad of apps and Internet of Things (IoT) devices (wearables, home-medical sensors, etc) facilitates data collection and provide cloud storage with a central administration. More recently, blockchain and other distributed ledgers became available as alternative storage options based on decentralised organisation systems. We bring attention to the human data bleeding problem and argue that neither centralised nor decentralised system organisations are a magic bullet for data-driven innovation if individual, community and societal values are ignored. The motivation for this position paper is to elaborate on strategies to protect privacy as well as to encourage data sharing and support open data without requiring a complex access protocol for researchers. Our main contribution is to outline the design of a self-regulated Open Health Archive (OHA) system with focus on quality of life (QoL) data.Comment: DSS 2018: Data-Driven Self-Regulating System