10,279 research outputs found
Beyond Accuracy: A Critical Review of Fairness in Machine Learning for Mobile and Wearable Computing
The field of mobile, wearable, and ubiquitous computing (UbiComp) is
undergoing a revolutionary integration of machine learning. Devices can now
diagnose diseases, predict heart irregularities, and unlock the full potential
of human cognition. However, the underlying algorithms are not immune to biases
with respect to sensitive attributes (e.g., gender, race), leading to
discriminatory outcomes. The research communities of HCI and AI-Ethics have
recently started to explore ways of reporting information about datasets to
surface and, eventually, counter those biases. The goal of this work is to
explore the extent to which the UbiComp community has adopted such ways of
reporting and highlight potential shortcomings. Through a systematic review of
papers published in the Proceedings of the ACM Interactive, Mobile, Wearable
and Ubiquitous Technologies (IMWUT) journal over the past 5 years (2018-2022),
we found that progress on algorithmic fairness within the UbiComp community
lags behind. Our findings show that only a small portion (5%) of published
papers adheres to modern fairness reporting, while the overwhelming majority
thereof focuses on accuracy or error metrics. In light of these findings, our
work provides practical guidelines for the design and development of ubiquitous
technologies that not only strive for accuracy but also for fairness
SleepNet: Attention-Enhanced Robust Sleep Prediction using Dynamic Social Networks
Sleep behavior significantly impacts health and acts as an indicator of
physical and mental well-being. Monitoring and predicting sleep behavior with
ubiquitous sensors may therefore assist in both sleep management and tracking
of related health conditions. While sleep behavior depends on, and is reflected
in the physiology of a person, it is also impacted by external factors such as
digital media usage, social network contagion, and the surrounding weather. In
this work, we propose SleepNet, a system that exploits social contagion in
sleep behavior through graph networks and integrates it with physiological and
phone data extracted from ubiquitous mobile and wearable devices for predicting
next-day sleep labels about sleep duration. Our architecture overcomes the
limitations of large-scale graphs containing connections irrelevant to sleep
behavior by devising an attention mechanism. The extensive experimental
evaluation highlights the improvement provided by incorporating social networks
in the model. Additionally, we conduct robustness analysis to demonstrate the
system's performance in real-life conditions. The outcomes affirm the stability
of SleepNet against perturbations in input data. Further analyses emphasize the
significance of network topology in prediction performance revealing that users
with higher eigenvalue centrality are more vulnerable to data perturbations.Comment: Accepted for publication in Proceedings of the ACM on Interactive,
Mobile, Wearable and Ubiquitous Technologies (IMWUT), 8 (March 2024
Wearable and mobile devices
Information and Communication Technologies, known as ICT, have undergone dramatic changes in the last 25 years. The 1980s was the decade of the Personal Computer (PC), which brought computing into the home and, in an educational setting, into the classroom. The 1990s gave us the World Wide Web (the Web), building on the infrastructure of the Internet, which has revolutionized the availability and delivery of information. In the midst of this information revolution, we are now confronted with a third wave of novel technologies (i.e., mobile and wearable computing), where computing devices already are becoming small enough so that we can carry them around at all times, and, in addition, they have the ability to interact with devices embedded in the environment. The development of wearable technology is perhaps a logical product of the convergence between the miniaturization of microchips (nanotechnology) and an increasing interest in pervasive computing, where mobility is the main objective. The miniaturization of computers is largely due to the decreasing size of semiconductors and switches; molecular manufacturing will allow for ānot only molecular-scale switches but also nanoscale motors, pumps, pipes, machinery that could mimic skinā (Page, 2003, p. 2). This shift in the size of computers has obvious implications for the human-computer interaction introducing the next generation of interfaces. Neil Gershenfeld, the director of the Media Labās Physics and Media Group, argues, āThe world is becoming the interface. Computers as distinguishable devices will disappear as the objects themselves become the means we use to interact with both the physical and the virtual worldsā (Page, 2003, p. 3). Ultimately, this will lead to a move away from desktop user interfaces and toward mobile interfaces and pervasive computing
Wearable Computing for Health and Fitness: Exploring the Relationship between Data and Human Behaviour
Health and fitness wearable technology has recently advanced, making it
easier for an individual to monitor their behaviours. Previously self generated
data interacts with the user to motivate positive behaviour change, but issues
arise when relating this to long term mention of wearable devices. Previous
studies within this area are discussed. We also consider a new approach where
data is used to support instead of motivate, through monitoring and logging to
encourage reflection. Based on issues highlighted, we then make recommendations
on the direction in which future work could be most beneficial
Challenges and opportunities of context-aware information access
Ubiquitous computing environments embedding a wide range of pervasive computing technologies provide a challenging and exciting new domain for information access. Individuals working in these environments are increasingly permanently connected to rich information resources. An appealing opportunity of these environments is the potential to deliver useful information to individuals either from their previous information experiences or external sources. This information should enrich their life experiences or make them more effective in their endeavours. Information access in ubiquitous computing environments can be made "context-aware" by exploiting the wide range context data available describing the environment, the searcher and the information itself. Realizing such a vision of reliable, timely and appropriate identification and delivery of information in this way poses numerous challenges. A central theme in achieving context-aware information access is the combination of information retrieval with multiple dimensions of available context data. Potential context data sources, include the user's current task, inputs from environmental and biometric sensors, associated with the user's current context, previous contexts, and document context, which can be exploited using a variety of technologies to create new and exciting possibilities for information access
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