3,646 research outputs found
GLOBEM Dataset: Multi-Year Datasets for Longitudinal Human Behavior Modeling Generalization
Recent research has demonstrated the capability of behavior signals captured
by smartphones and wearables for longitudinal behavior modeling. However, there
is a lack of a comprehensive public dataset that serves as an open testbed for
fair comparison among algorithms. Moreover, prior studies mainly evaluate
algorithms using data from a single population within a short period, without
measuring the cross-dataset generalizability of these algorithms. We present
the first multi-year passive sensing datasets, containing over 700 user-years
and 497 unique users' data collected from mobile and wearable sensors, together
with a wide range of well-being metrics. Our datasets can support multiple
cross-dataset evaluations of behavior modeling algorithms' generalizability
across different users and years. As a starting point, we provide the benchmark
results of 18 algorithms on the task of depression detection. Our results
indicate that both prior depression detection algorithms and domain
generalization techniques show potential but need further research to achieve
adequate cross-dataset generalizability. We envision our multi-year datasets
can support the ML community in developing generalizable longitudinal behavior
modeling algorithms.Comment: Thirty-sixth Conference on Neural Information Processing Systems
Datasets and Benchmarks Trac
Design with Perfect Sense: the Adoption of Smart Sensor Technologies (SST) in Architectural Practice
Recent development in the Internet of Things (IoT) has enabled real-time data-driven decision making in diverse industries. For example, over the last few years, the introduction of smart sensor technologies such as Watson IoT has led to various data-driven solutions in space planning, real-estate management, and energy conservation. Despite the recent development, these technologies are not widely used in architectural practice. In the wake of this trend, this research aims at understanding how architects and design professionals can be supported to further utilize smart sensor technologies in their practice. Based on the Technology-Organization-Environment framework and a series of interviews, the major influencing factors on user adoption were identified. This study contributes to both theory and practice by identifying six contributing factors, namely perceived risk and value, commitment to learn and collaborate, as well as knowledge and trust
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Emotional Biosensing: Exploring Critical Alternatives
Emotional biosensing is rising in daily life: Data and categories claim to know how people feel and suggest what they should do about it, while CSCW explores new biosensing possibilities. Prevalent approaches to emotional biosensing are too limited, focusing on the individual, optimization, and normative categorization. Conceptual shifts can help explore alternatives: toward materiality, from representation toward performativity, inter-action to intra-action, shifting biopolitics, and shifting affect/desire. We contribute (1) synthesizing wide-ranging conceptual lenses, providing analysis connecting them to emotional biosensing design, (2) analyzing selected design exemplars to apply these lenses to design research, and (3) offering our own recommendations for designers and design researchers. In particular we suggest humility in knowledge claims with emotional biosensing, prioritizing care and affirmation over self- improvement, and exploring alternative desires. We call for critically questioning and generatively re- imagining the role of data in configuring sensing, feeling, âthe good life,â and everyday experience
Leveraging Multi-Modal Sensing for Mobile Health: A Case Review in Chronic Pain
Active and passive mobile sensing has garnered much attention in recent years. In this paper, we focus on chronic pain measurement and management as a case application to exemplify the state of the art. We present a consolidated discussion on the leveraging of various sensing modalities along with modular server-side and on-device architectures required for this task. Modalities included are: activity monitoring from accelerometry and location sensing, audio analysis of speech, image processing for facial expressions as well as modern methods for effective patient self-reporting. We review examples that deliver actionable information to clinicians and patients while addressing privacy, usability, and computational constraints. We also discuss open challenges in the higher level inferencing of patient state and effective feedback with potential directions to address them. The methods and challenges presented here are also generalizable and relevant to a broad range of other applications in mobile sensing
Machine Learning for Stress Monitoring from Wearable Devices: A Systematic Literature Review
Introduction. The stress response has both subjective, psychological and
objectively measurable, biological components. Both of them can be expressed
differently from person to person, complicating the development of a generic
stress measurement model. This is further compounded by the lack of large,
labeled datasets that can be utilized to build machine learning models for
accurately detecting periods and levels of stress. The aim of this review is to
provide an overview of the current state of stress detection and monitoring
using wearable devices, and where applicable, machine learning techniques
utilized.
Methods. This study reviewed published works contributing and/or using
datasets designed for detecting stress and their associated machine learning
methods, with a systematic review and meta-analysis of those that utilized
wearable sensor data as stress biomarkers. The electronic databases of Google
Scholar, Crossref, DOAJ and PubMed were searched for relevant articles and a
total of 24 articles were identified and included in the final analysis. The
reviewed works were synthesized into three categories of publicly available
stress datasets, machine learning, and future research directions.
Results. A wide variety of study-specific test and measurement protocols were
noted in the literature. A number of public datasets were identified that are
labeled for stress detection. In addition, we discuss that previous works show
shortcomings in areas such as their labeling protocols, lack of statistical
power, validity of stress biomarkers, and generalization ability.
Conclusion. Generalization of existing machine learning models still require
further study, and research in this area will continue to provide improvements
as newer and more substantial datasets become available for study.Comment: 50 pages, 8 figure
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