The objective of this workshop is to share the experiences among researchers about current challenges of real-world activity recognition with newly developed datasets and tools, breaking through towards open-ended contextual intelligence.
This workshop discusses the challenges of designing reproducible experimental setups, the large-scale dataset collection campaigns, the activity and context recognition methods that are robust and adaptive, and evaluation systems in the real world.
As a special topic of this year we will reflect on the challenges to recognize situations, events and/or activities among the statically predefined pools and beyond - which is the current state of the art - and instead we will adopt an "open-ended view" on activity and context awareness. This may result in combinations of the automatic discovery
of relevant patterns in sensor data, the experience sampling and wearable technologies to unobtrusively discover the semantic meaning of such patterns, the crowd-sourcing of dataset acquisition and annotation, and new "open-ended" human activity modeling techniques.
CALL FOR CONTRIBUTIONS
We expect the following domains to be relevant contributions to this workshop (but not limited to):
- *Data collection*, *Corpus construction*.
Experiences or reports from data collection and/or corpus construction projects, including papers which describes the formats, styles and/or methodologies for data collection. Cloud-sourcing data collection and participatory sensing also could be included in this topic.
- *Effectiveness of Data*, *Data Centric Research*.
There is a field of research based on the collected corpora, which is so called "data centric research". Also, we call for the experience of using large-scale human activity sensing corpora. Using large-scale corpora with an analysis by machine learning, there will be a large space for improving the performance of recognition results.
- *Tools and Algorithms for Activity Recognition*.
If we have appropriate tools for the management of sensor data, activity recognition researchers could have more focused on their actual research theme. This is because the developed tools and algorithms are often not shared among the research community. In this workshop, we solicit reports on developed tools and algorithms for forwarding to the community.
- *Real World Application and Experiences*.
Activity recognition *in the Lab* usually works well. However, it does not scale well with real world data. In this workshop, we also solicit the experiences from real world applications. There is a huge gap between "lab" and "real world" environments. Large-scale human activity sensing corpora will help to overcome this gap.
- *Sensing Devices and Systems*.
Data collection is not only performed by the "off-the-shelf" sensors but also the newly developed sensors which supply information which has not been investigated. There is also a research area about the development of new platform for data collection or the evaluation tools for collected data.
In light of this year's special emphasis on open-ended contextual awareness, we wish cover these topics as well:
- *Mobile Experience Sampling*, *Experience Sampling Strategies*.
Advances in experience sampling approaches, for instance intelligent user query or those using novel devices (e.g. smartwatches), are likely to play an important role to provide user-contributed annotations of their own activities.
- *Unsupervised Pattern Discovery*.
Discovering meaningful patterns in sensor data in an unsupervised manner can be needed in the context of informing other elements of the system by inquiring the user and by triggering the annotation with crowd-sourcing.
- *Dataset Acquisition and Annotation*, *Crowd-Sourcing*, *Web-Mining*.
A wide abundance of sensor data is potentially within the reach of users instrumented with their mobile phones and other wearables. Capitalizing on crowd-sourcing to create larger datasets in a cost effective manner may be critical to open-ended activity recognition. Many online datasets are also available and could be used to bootstrap recognition models.
- *Transfer Learning*, *Semi-Supervised Learning*, *lifelong Learning*.
The ability to translate recognition models across modalities or to use minimal forms of supervision would allow to reuse datasets in a wider range of domains and reduce the costs of acquiring annotations.
AREAS OF INTEREST
- Human Activity Sensing Corpus
- Large Scale Data Collection
- Data Validation
- Data Tagging / Labeling
- Efficient Data Collection
- Data Mining from Corpus
- Automatic Segmentation
- Performance Evaluation
- Man-machine Interaction
- Noise Robustness
- Non Supervised Machine Learning
- Sensor Data Fusion
- Tools for Human Activity Corpus/Sensing
- Participatory Sensing
- Feature Extraction and Selection
- Context Awareness
- Pedestrian Navigation
- Social Activities Analysis/Detection
- Compressive Sensing
- Sensing Devices
- Lifelog Systems
- Route Recognition/Detection
- Wearable Application
- Gait Analysis
- Health-care Monitoring/Recommendation
- Daily-life Worker Support
The Sussex-Huawei Locomotion Dataset will be used in an activity recognition challenge with results to be presented at HASCA 2018. To be part of the final ranking, participants will be required to submit a detailed paper to the
HASCA workshop. The paper should contain technical description of the processing pipeline, the algorithms and the results achieved during the development/training phase.
The paper submission date will be set during the competition. The submissions must follow the HASCA format (up to 10 pages).
We invite three kinds of submissions:
- Full research papers up to 10 pages
- Short technical papers up to 5 pages
- SHL challenge papers up to 10 pages
All publications will be peer reviewed together with their contribution to the topic of the workshop.
Submission is electronic,
using precision submission system. The submission site is open at https://new.precisionconference.com/user/login (select SIGCHI/UbiComp 2018/UbiComp 2018 Workshop - HASCA and push Go button).
All the papers must be formatted as "ACM SIGCHI Extended Abstracts format" (landscape). Submissions do not need to be anonymous.
The accepted papers will be published in the UbiComp 2018 adjunct proceedings, which will be included in the ACM Digital Library.
Full research/short technical papers:
- Submission deadline : August 2, 2018 (extended)
- Notification of acceptance: Aug 10, 2018
- Camera ready deadline : Aug 18, 2018
- Workshop date: October 12, 2018
SHL challenge papers (only the challenge participants):
- Submission deadline : July 31, 2018
(Check how to register at http://www.shl-dataset.org/activity-recognition-challenge/ no later than June
If you have any questions please contact