SUMMARY

The objective of this workshop is to share the experiences among current researchers around the challenges of real-world activity recognition, the role of datasets and tools, and breakthrough approaches towards open-ended contextual intelligence.

This workshop deals with the challenges of designing reproducible experimental setups, running large-scale dataset collection campaigns, designing activity and context recognition methods that are robust and adaptive, and evaluating systems in the real world.

As a special topic this year, we wish to reflect on the challenges and possible approaches to recognise situations, events or activities outside of a statically pre-defined pool - which is the current state of the art - and instead adopt an "open-ended view" on activity and context awareness. This may take combinations of advances in the automatic discovery of relevant patterns in sensor data, advances in experience sampling and wearable technologies to unobtrusively discover the semantic meaning of such patterns, advances in 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, such as papers describing the formats, styles or methodologies for data collection. Cloud-sourcing data collection or 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 corpus, which is called *Data Centric Research* Also, we solicit of the experience of using large-scale human activity sensing corpus. Using large-scale corpus with 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 and suitable tools for management of sensor data, activity recognition researchers could be more focused on their research theme. However, development of tools or algorithms for sharing among the research community is not much appreciated. In this workshop, we solicit development reports of tools and algorithms for forwarding the community.

- *Real World Application and Experiences*.
Activity recognition *in the Lab* usually works well. However, it is not true in the real world. In this workshop, we also solicit the experiences from real world applications. There is a huge gap/valley between Lab Environment and Real World Environment. Large scale human activity sensing corpus will help to overcome this gap/valley.

- *Sensing Devices and Systems*.
Data collection is not only performed by the Off the shelf sensors. There is a requirement to develop some special devices to obtain some sort of information. There is also a research area about the development or evaluate the system or technologies for data collection.

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 intelligently querying the user or 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 repeating patterns in sensor data can be fundamental in informing other elements of the system, such as inquiring user or triggering annotation crowd sourcing.

- *Dataset acquisition and annotation*, *crowd-sourcing*, *web-mining*.

A wide abundance of sensor data is potentially in reach with users instrumented with their mobile phones and other wearables. Capitalising 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 accross 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

FORMAT

We invite two kinds of submissions:

• Full research papers up to 10 pages
• Short technical papers up to 5 pages

All publications will be peer reviewed together with their contribution
to the topic of the workshop.

Send your paper to hasca2016-submission@hasc.jp with "HASCA2016 submission" as email subject.

Templates

All the papers must be formatted as “ACM SIGCHI Extended Abstracts format”(landscape). Templates: (LaTeX | WORD ) Submissions do not need to be anonymous.

KEY DATES

Submission: June 7, June 19,  2016

Notification of acceptance: June 21, June 27,  2016

Camera ready: June 28, July 4, 2016

 

 

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