Welcome to HASCA2021 Web site!

HASCA2021 is an ninth International Workshop on Human Activity Sensing Corpus and Applications. The workshop will be held in conjunction with UbiComp/ISWC2021.

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.

Proceedings
The accepted papers in HASCA workshop are included in the proceedings on ACM DL.
https://dl.acm.org/doi/proceedings/10.1145/3460418

Workshop starts at September 25th (PDT) / 26th (EDT, CEST, JST), 2021.
When you join, please search "HASCA" on Whova Agenda and get the links for zoom/gather.town.

Presentation time:
HASCA oral presentation, 15 min (12-min talk + 3-min Q&A)
(For short paper, 8min talk + 2min Q&A)
SHL oral presentation, 12 min (10-min talk + 2-min Q&A)
SHL video, 1 min
Nurse oral presentation, 12 min
Nurse video, 1 min

2200-0027(PDT) 0100-0327(EDT) 0700-0927(CEST) 14:00-1627(JST)	-Opening remarks [SHL Session (chair: Paula Lago)] -SHL introduction [4 min] -SHL summary [15 min] Locomotion and Transportation Mode Recognition from GPS and radio signals: Summary of SHL Challenge 2021. Lin Wang,Mathias Ciliberto,Hristijan Gjoreski,Paula Lago,Kazuya Murao,Tsuyoshi Okita,Daniel Roggen -SHL team 1 presentation [12 min] -SHL team 2 presentation [12 min] -SHL team 3 presentation [12 min] -SHL Challenge videos broadcast [11 min] Dense CNN and IndRNN for the Sussex-Huawei Locomotion-Transportation Recognition Challenge. Chuankun Li, Shuai Li, Yanbo Gao, Jinming Guo, Ping Chen, Wanqing Li. [Poster][Video] Transition-points-based Segmentation and Hierarchical Classification for Locomotion and Transportation recognition Radio-data. Nhat Tan Le, Nazmun Nahid, Sozo Inoue. [Poster][Video] Triple-O for SHL Recognition Challenge: An Ensemble Framework for Multi-class Imbalance and Training-testing Distribution Inconsistency by OvO Binarization with Confidence Weight of One-class Classification. Jinhua Su, Yuanyuan Zhang. [Poster][Video] A Windowless Approach to Recognize Various Modes of Locomotion and Transportation. Promit Basak, Shahamat Mustavi Tasin, A.H.M. Nazmus Sakib, Syed Doha Uddin, Md Atiqur Rahman Ahad. [Poster][Video] An Ensemble of ConvTransformer Networks for the Sussex-Huawei Locomotion-Transportation (SHL) Recognition Challenge. Aosheng Tian, Ye Zhang, Huiling Chen, Chao Ma, Shilin Zhou. [Poster][Video] Locomotion-Transportation Recognition via LSTM and GPS Derived Feature Engineering from Cell Phone Data. Gulustan Dogan, Jonathan Daniel Sturdivant, Seyda Ari, Evan Kurpiewski. [Poster][Video] Location-based Human Activity Recognition Using Long-term Deep Learning Invariant Mapping. Livii Iabanzhi, Maria Astrakhan, Pavlo Tyshevskyi. Classical Machine Learning Approach for Human Activity Recognition Using Location Data. Safaeid Hossain Arib, Rabeya Akter, Omar Shahid, Md Atiqur Rahman Ahad. [Poster][Video] Multiple Tree Model Integration for Transportation Mode Recognition. Yan Ren. [Poster][Video] Classical machine learning and deep neural network ensemble model for GPS-based activity recognition. Ryoichi Kojima, Roberto Legaspi, Yutaro Mishima, Shinya Wada. [Poster][Video] Phased Human Activity Recognition based on GPS. Ryoichi Sekiguchi, Kenji Abe, Shogo Suzuki, Masayasu Kumano, Daisuke Asakura, Ryo Okabe, Takeru Kariya, Masaki Kawakatsu. [Poster][Video] -SHL ceremony (5 min) [HASCA Session 1 (chair: Yu Enokibori)] -[HASCA] Collecting a dataset of gestures for skill assessment in the field: a beach volleyball serves case study Mathias Ciliberto, Luis Alejandro Ponce Cuspinera, Daniel Roggen -[HASCA] Reducing Label Fragmentation during Time-series Data Annotation to Reduce Annotation Costs Joseph Korpela, Takayuki Akiyama, Takehiro Niikura, Katsuyuki Nakamura -[HASCA] Prediction of Eating Activity using Smartwatch Haruka Kamachi, Tahera Hossain, Fuyuka Tokuyama, Anna Yokokubo, Guillaume Lopez -[HASCA] [Short Paper] Inferring complex textile shape from an integrated carbon black-infused ecoflex-based bend and stretch sensor array Leonardo Azael Garcia-Garcia, George Valsamakis, Paul Kreitmair, Niko Munzenrieder, Daniel Roggen -[HASCA] Automatic Segmentation Method of Bone Conduction Sound for Eating Activity Detailed Detection Haruka Kamachi, Takumi Kondo, Tahera Hossain, Anna Yokokubo, Guillaume Lopez
0027-0050(PDT) 0327-0350(EDT) 0927-0950(CEST) 1627-1650(JST)	Break/ SHL poster session [about 30 min]
0050-0233(PDT) 0350-0533(EDT) 0950-1133(CEST) 1650-1833(JST)	[Nurse Session (chair: Sozo Inoue)] -Nurse summary [12 min] Summary of the Third Nurse Care Activity Recognition Challenge - Can We Do from the Field Data? Sayeda Shamma Alia, Kohei Adachi, Tahera Hossain, Nhat Tan Le, Haru Kaneko, Paula Lago, Tsuyoshi Okita, Sozo Inoue -Nurse winner presentation [12 min] -Nurse videos broadcast [3min] -Nurse ceremony Nurse papers: - Nurse Care Activity Recognition from Accelerometer Sensor Data Using Fourier- and Wavelet-based Features M. Ashikuzzaman Kowshik,Yeasin Arafat Pritom,Md.Sohanur Rahman,Ali Akbar,Md Atiqur Rahman Ahad [PDF][Video] - Nurse Care Activity Recognition: A Cost-Sensitive Ensemble Approach to Handle Imbalanced Class Problem in the Wild Arafat Rahman,Iqbal Hassan,Md Atiqur Rahman Ahad [PDF][Video] - Feature-based Method for Nurse Care Complex Activity Recognition from Accelerometer Sensor Faizul Rakib Sayem,MD Mamun Sheikh,Md Atiqur Rahman Ahad [PDF][Video] - Accelerometer based Complex Nurse Care Activity Recognition using Machine Learning Approach Zubair Rahman Tusar,Maksuda Islam,Sadia Sharmin [PDF][Video] [HASCA Session 2 (chair Kazuya Murao)] -[HASCA] Activity Knowledge Graph Recognition by Eye Gaze: Identification of Distant Object in Eye Sight for Watch Activity Yuki Toyosaka, Tsuyoshi Okita -[HASCA] Toward Fine-Grained Sleeping Activity Recognition: 3d Extension and an Estimation Try on Joint Position of SLP Dataset Hiroki Kato, Yu Enokibori, Naoto Yoshida, Kenji Mase -[HASCA] Analysis of Feature Importances for Automatic Generation of Care Records Haru Kaneko, Tahera Hossain, Sozo Inoue -[HASCA] PerMML: A Performance Metric for Multi-layer Dataset Sayeda Shamma Alia, Tahera Hossain, Sozo Inoue -Closing remarks