2,606 research outputs found
Making Sensors, Making Sense, Making Stimuli: The State of the Art in Wearables Research from ISWC 2019
The International Symposium on Wearable Computers (ISWC) has been the leading research venue for wearable technology research since 1997. This year, the 23rd ISWC was held in London, UK from Sept 9-13th. Following on the last 8 years of successful collaboration, ISWC was co-located with the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp)
Unsupervised Motion Artifact Detection in Wrist-Measured Electrodermal Activity Data
One of the main benefits of a wrist-worn computer is its ability to collect a
variety of physiological data in a minimally intrusive manner. Among these
data, electrodermal activity (EDA) is readily collected and provides a window
into a person's emotional and sympathetic responses. EDA data collected using a
wearable wristband are easily influenced by motion artifacts (MAs) that may
significantly distort the data and degrade the quality of analyses performed on
the data if not identified and removed. Prior work has demonstrated that MAs
can be successfully detected using supervised machine learning algorithms on a
small data set collected in a lab setting. In this paper, we demonstrate that
unsupervised learning algorithms perform competitively with supervised
algorithms for detecting MAs on EDA data collected in both a lab-based setting
and a real-world setting comprising about 23 hours of data. We also find,
somewhat surprisingly, that incorporating accelerometer data as well as EDA
improves detection accuracy only slightly for supervised algorithms and
significantly degrades the accuracy of unsupervised algorithms.Comment: To appear at International Symposium on Wearable Computers (ISWC)
201
Understanding and Improving Recurrent Networks for Human Activity Recognition by Continuous Attention
Deep neural networks, including recurrent networks, have been successfully
applied to human activity recognition. Unfortunately, the final representation
learned by recurrent networks might encode some noise (irrelevant signal
components, unimportant sensor modalities, etc.). Besides, it is difficult to
interpret the recurrent networks to gain insight into the models' behavior. To
address these issues, we propose two attention models for human activity
recognition: temporal attention and sensor attention. These two mechanisms
adaptively focus on important signals and sensor modalities. To further improve
the understandability and mean F1 score, we add continuity constraints,
considering that continuous sensor signals are more robust than discrete ones.
We evaluate the approaches on three datasets and obtain state-of-the-art
results. Furthermore, qualitative analysis shows that the attention learned by
the models agree well with human intuition.Comment: 8 pages. published in The International Symposium on Wearable
Computers (ISWC) 201
Learning Bodily and Temporal Attention in Protective Movement Behavior Detection
For people with chronic pain, the assessment of protective behavior during
physical functioning is essential to understand their subjective pain-related
experiences (e.g., fear and anxiety toward pain and injury) and how they deal
with such experiences (avoidance or reliance on specific body joints), with the
ultimate goal of guiding intervention. Advances in deep learning (DL) can
enable the development of such intervention. Using the EmoPain MoCap dataset,
we investigate how attention-based DL architectures can be used to improve the
detection of protective behavior by capturing the most informative temporal and
body configurational cues characterizing specific movements and the strategies
used to perform them. We propose an end-to-end deep learning architecture named
BodyAttentionNet (BANet). BANet is designed to learn temporal and bodily parts
that are more informative to the detection of protective behavior. The approach
addresses the variety of ways people execute a movement (including healthy
people) independently of the type of movement analyzed. Through extensive
comparison experiments with other state-of-the-art machine learning techniques
used with motion capture data, we show statistically significant improvements
achieved by using these attention mechanisms. In addition, the BANet
architecture requires a much lower number of parameters than the state of the
art for comparable if not higher performances.Comment: 7 pages, 3 figures, 2 tables, code available, accepted in ACII 201
Multi-Sensor Context-Awareness in Mobile Devices and Smart Artefacts
The use of context in mobile devices is receiving increasing attention in mobile and ubiquitous computing research. In this article we consider how to augment mobile devices with awareness of their environment and situation as context. Most work to date has been based on integration of generic context sensors, in particular for location and visual context. We propose a different approach based on integration of multiple diverse sensors for awareness of situational context that can not be inferred from location, and targeted at mobile device platforms that typically do not permit processing of visual context. We have investigated multi-sensor context-awareness in a series of projects, and report experience from development of a number of device prototypes. These include development of an awareness module for augmentation of a mobile phone, of the Mediacup exemplifying context-enabled everyday artifacts, and of the Smart-Its platform for aware mobile devices. The prototypes have been explored in various applications to validate the multi-sensor approach to awareness, and to develop new perspectives of how embedded context-awareness can be applied in mobile and ubiquitous computing
Augmented Memory for Conference Attendees
Human memory at its best can perform astonishing feats - the tiniest snippet of information can trigger whole chains of associations, ending at an item long-believed forgotten. While modern information systems excel at systematic manipulation of structured or semi-structured information or even vast repositories of unstructured textual information, they are still far from these capabilities
Sensors in your clothes: Design and development of a prototype
Wearable computing is fast advancing as a preferred approach for integrating software solutions not only in our environment, but also in our everyday garments to exploit the numerous information sources we constantly interact with. This paper explores this context further by showing the possible use of wearable sensor technology for information critical information systems, through the design and development of a proof-of-concept prototyp
Adding generic contextual capabilities to wearable computers
Context-awareness has an increasingly important role to play in the development of wearable computing systems. In order to better define this role we have identified four generic contextual capabilities: sensing, adaptation, resource discovery, and augmentation. A prototype application has been constructed to explore how some of these capabilities could be deployed in a wearable system designed to aid an ecologist's observations of giraffe in a Kenyan game reserve. However, despite the benefits of context-awareness demonstrated in this prototype, widespread innovation of these capabilities is currently stifled by the difficulty in obtaining the contextual data. To remedy this situation the Contextual Information Service (CIS) is introduced. Installed on the user's wearable computer, the CIS provides a common point of access for clients to obtain, manipulate and model contextual information independently of the underlying plethora of data formats and sensor interface mechanisms
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