Accepted papers

We will announce the timetable soon.
All the accepted papers will be presented in oral or poster format during the HASCA workshop on September 10th.

[HASCA Oral presentation]

-Let there be IMU data: generating training data for wearable, motion sensor based activity recognition from monocular RGB videos
Vitor Fortes, Peter Hevesi, Onorina Kovalenko, Paul Lukowicz

-Benchmarking Deep Classifiers on Mobile Devices for Vision-based Transportation Recognition
Sebastien Richoz, Daniel Roggen, Andres Perez-Uribe, Philip Birch

-Optimizing Activity Data Collection with Gamification Points Using Uncertainty Based Active Learning
Nattaya Mairittha, Tittaya Mairittha, Sozo Inoue

-Ballroom Dance Step Type Recognition by Random Forest using Video and Wearable Sensor
Hitoshi Matsuyama, Kei Hiroi, Katsuhiko Kaji, Takuro Yonezawa, Nobuo Kawaguchi

-Cross-dataset Deep Transfer Learning for Activity Recognition
Martin Gjoreski, Stefan Kalabakov, Matjaž Gams, Hristijan Gjoreski

-A Dialogue-Based Annotation for Activity Recognition
Tittya Mairittha, Nattaya Mairittha, Sozo Inoue

-Reduction of Marker-Body Matching Work in Activity Recognition Using Motion Capture
Shingo Takeda, Paula Lago, Tsuyoshi Okita, Sozo Inoue

-PDR with Head Swing Detection Only using Hearable Device
Koki Tamura, Hiroto Asai, Nobuhiko Nishio

-Gesture recognition method with acceleration data weighted by EMG
Daiki Kajiwara, Kazuya Murao

-Appraisal theory-based mobile app for physiological data collection and labelling in the wild
Fanny Larradet, Radoslaw Niewiadomski, Giacinto Barresi, leonardo de mattos

[HASCA Poster presentation]

-The Practicability of Predicting the Number of Bus Passengers by Monitoring Wi-Fi Signal from Mobile Devices with the Polynomial Regression
Thongtat Oransirikul, Hideyuki Takada

-M3B Corpus: Multi-Modal Meeting Behavior Corpus for Group Meeting Assessment
Yusuke Soneda, Yuki Matsuda, Yutaka Arakawa, Keiichi Yasumoto

-Sampling Rate Dependency in Pedestrian Walking Speed Estimation using DualCNN-LSTM
Takuto Yoshida

-Crowdsensing Under Recent Mobile Platform Background Service Restrictions - A Practical Approach
Oliver Petter, Marco Hirsch, Eshan Mushtaq, Peter Hevesi, Paul Lukowicz

-Automatic Annotation for Human Activity and Device State Recognition Using Smartphone Notification
Ryota Sawano, Kazuya Murao

-CoAT: A Web-based, Collaborative Annotation Tool
Aziret Satybaldiev, Peter Hevesi, Marco Hirsch, Vitor Fortes, Paul Lukowicz

-VegeTongs: Vegetable Recognition Tongs Using Active Acoustic Sensing
Haruna Nishii, Kyosuke Futami, Kazuya Murao

[SHL Challenge Oral presentation]

To be updated.

[SHL Challenge Poser presentation]

To be updated.

[Nurse Challenge Oral presentation]

-Can a Simple Approach Identify Complex Nurse Care Activity?
Pritom Saha Akash, et al.

-Nurse Care Activity Recognition Challenge: Summary and Results
Paula Lago, et al.

[Nurse Challenge Poster presentation]

-Nurse Care Activity Recognition: A GRU-based Approach with Attention Mechanism
Md. Nazmul Haque, et al.

-Nurse Care Activity Recognition Challenge Using A Supervised Methodology
Protap Saha, et al.

-Activity Recognition Using ST-GCN with 3D Motion Data
Masaki Shuzo, et al.

Welcome to HASCA2019

Submission deadline was extended to June 21, 2019
Submission deadline was extended to June 26, 2019 (Hard)

Welcome to HASCA2019 Web site!

HASCA2019 is a seventh International Workshop on Human Activity Sensing Corpus and Applications. The workshop will be held in conjunction with UbiComp2019.


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.

This year, HASCA will welcome papers from participants to

the Sussex-Huawei Locomotion and Transportation Recognition Competition and to

the Nurse Care Activity Recognition Challenge

in two special sessions.

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.