Proceedings
The accepted papers in HASCA workshop are included in the proceedings on ACM DL.
(Proceedings link will be here)

Please do not show the zoom link as only the paid participants can join the workshop.
Presentation time:
HASCA oral presentation, 20 min (15-min talk + 5-min Q&A)

The program's timezone is UK.

13:00-13:10	Opening
13:10-14:30	Session 1 [20min x4] Smart-Badge: A wearable badge with multi-modal sensors for kitchen activity recognition Personal Identification Method using Active Acoustic Sensing applied to the Nose pad of Eyeglasses A Method for Identifying a Person Entering a Bathtub using a Water Pressure Sensor (virtual) FlexiBLE: A Toolkit for Free Living Wearable Development
14:30-14:50	Break (20min)
14:50-16:10	Session 2 [20min x4] (virtual) Feature relevance analysis to explain concept drift - a case study in human activity recognition Hierarchical Feature Recovery for Robust Human Activity Recognition in Body Sensor Networks Investigating domain-agnostic performance in activity recognition using accelerometer data A Method for Estimating Distance Actually Swam using Wearable Accelerometer and Gyroscope
16:10-16:30	Break (20min)
16:30-17:50	Session 3 [20min x4] Preliminary Investigation on Wrist Temperature Control by Wearable Peltier Armband A Method for Estimating Temperature of Grasped Object using PPG Sensor Design and Implementation of a Non-contact Thermometer that Records Body Temperature Individually using Forehead Wrinkle Image (virtual) ActiviSee: Activity Transition Detection for Human Users through Wearable Sensor-augmented Glasses
17:50-18:00	Closing

Welcome to HASCA2022 Web site!

HASCA2022 is a tenth International Workshop on Human Activity Sensing Corpus and Applications. The workshop will be held in conjunction with UbiComp/ISWC2022.

Important Dates
Submission Deadline: Aug. 14th Aug. 17th
Acceptance Notification: Aug. 21st Aug. 24th
Camera-ready: Aug. 26th Sep. 2nd
Workshop: Sept. 15th

Notice This year, the venue of HASCA 2022 workshop will be UK (Cambridge) and we will allow online presentations.

Abstract

The recognition of complex and subtle human behaviors from wearable sensors will enable next-generation human-oriented computing in scenarios of high societal value (e.g., dementia care). This will require large-scale human activity corpora and improved methods to recognize activities and the context in which they occur. 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. We wish to reflect on future methods, such as lifelong learning approaches that allow open-ended activity recognition. 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.

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. 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-scape 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 Envi- ronment" 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.

Mobile experience sampling, experience sampling strategies:

Advances in experience sampling ap- proaches, 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 mean- ingful repeating patterns in sensor data can be fundamental in informing other elements of a system generating an activity corpus, such as inquiring user or triggering annotation crowd sourcing.

Dataset acquisition and annotation through crowd-sourcing, web-mining

A wide abundance of sensor data is potentially in reach with 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. Online datasets could also be used to bootstrap recognition models.

Transfer learning, semi-supervised learning, lifelong learning

The ability to translate recognition mod- els across modalities or to use minimal supervision would allow to reuse datasets across domains and reduce the costs of acquiring annotations.