All the accepted papers will be presented in oral or poster format during the HASCA workshop on September 10th.
Please check your presentation format (updated 31st Aug.)
Oral presentation: 15 min (10 min talk + 5 min Q&A)
Poster presentation: Poster size is A1 portrait.
| 0900-0910 | Opening remarks |
| 0910-1010 | HASCA Oral Presentations (1)
Chair: Martin Gjoreski (Jožef Stefan Postgraduate School, Slovenia)
-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
-A Dialogue-Based Annotation for Activity Recognition
Tittaya Mairittha, Nattaya Mairittha, Sozo Inoue
-Appraisal theory-based mobile app for physiological data collection and labelling in the wild
Fanny Larradet, Radoslaw Niewiadomski, Giacinto Barresi, leonardo de mattos
-Optimizing Activity Data Collection with Gamification Points Using Uncertainty Based Active Learning
Nattaya Mairittha, Tittaya Mairittha, Sozo Inoue
|
| 1010-1100 | Coffee break / Poster session
-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
-Nurse Care Activity Recognition: A GRU-based Approach with Attention Mechanism
Md. Nazmul Haque, Mahir Mahbub, Md. Hasan Tarek, Lutfun Nahar Lota, Amin Ahsan Ali
-Nurse Care Activity Recognition Challenge Using A Supervised Methodology
Nazmus Sakib Patwary, Protap Kumar Saha, Ifthakhar Ahmed
-Activity Recognition Using ST-GCN with 3D Motion Data
Xin Cao, Wataru Kudo, Chihiro Ito, Masaki Shuzo, Eisaku Maeda
-Orange labs contribution to the Sussex-Huawei locomotion-transportation recognition challenge
A. Alwan, V. Frey, G. L. La.
-Transportation mode classification from smartphone sensors via a long-short-term memory network
B. Friedich, B. Cauchi, A. Hein, S. Fudickar
-CNN for human activity recognition on small datasets of acceleration and gyro sensors using transfer learning
C. Ito, M. Shuzo, E. Maeda
-Semi-supervised learning for human activity recognition using adversarial autoencoders
D. Balabka
-Locomotion recognition using XGBoost and neural network ensemble
H. Lu, M. Pinaroc, M. Lv, S. Sun, H. Han, R. C. Shah
-EmbraceNet for activity: A deep multimodal fusion architecture for activity recognition
J. Choi and J. Lee
-Application of IndRNN for human acivity recognition - the Sussex-Huawei locomotion
L. Zheng, S. Li, C. Zhu, Y. Gao
-POIDEN: position and orientation independent deep ensemble network for the classification of locomotion and transportation modes
Masud Ahmed, Anindya Das Antar, Tahera Hossain, Sozo Inoue, Md Atiqur Rahman Ahad
-Ensemble-based domain adaptation for transport mode recognition with mobile sensors
P. Widhalm, M. Leodolter, N. Brandle
-Position independent activity recognition using shallow neural architecture and empirical modeling
Swapnil Sayan Saha, Shafizur Rahman, Zarin Rezwana Ridita Haque, Tahera Hossain, Sozo Inoue, Md Atiqur Rahman Ahad
-Cross-location transfer learning for the Sussex-Huawei locomotion recognition challenge
Vito Janko, Martin Gjoreski, Carlo Maria De Masi, Nina Reščič, Mitja Luštrek, Matjaž Gams
-Transportation recognition with the Sussex-Huawei locomotion challenge
Y. Qin, C. Wang, H. Luo
-Applying 1D sensor denseNet to Sussex-Huawei locomotion-transportation recognition challenge
Y. Zhu, F. Zhao, R. Chen
|
| 1100-1230 | HASCA Oral Presentations (2)
Chair: Mathias Ciliberto (University of Sussex, UK)
-Ballroom Dance Step Type Recognition by Random Forest using Video and Wearable Sensor
Hitoshi Matsuyama, Kei Hiroi, Katsuhiko Kaji, Takuro Yonezawa, Nobuo Kawaguchi
-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
-Benchmarking Deep Classifiers on Mobile Devices for Vision-based Transportation Recognition
Sebastien Richoz, Daniel Roggen, Andres Perez-Uribe, Philip Birch
-Cross-dataset Deep Transfer Learning for Activity Recognition
Martin Gjoreski, Stefan Kalabakov, Matjaž Gams, Hristijan Gjoreski |
| 1230-1400 | Lunch |
| 1400-1450 | Nurse session
-Nurse Care Activity Recognition Challenge: Summary and Results
Paula Lago, Sayeda Shamma Alia, Shingo Takeda, Tittaya Mairittha, Nattaya Mairittha, Farina Faiz, Yusuke Nishimura, Kohei Adachi, Tsuyoshi Okita, François Charpillet, Sozo Inoue
-Can a Simple Approach Identify Complex Nurse Care Activity?
Md. Eusha Kadir, Pritom Saha Akash, Sadia Sharmin, Amin Ahsan Ali, Mohammad Shoyaib
-Ceremony |
| 1450-1600 | Coffee break / Poster session (Continued) |
| 1600-1730 | SHL session
-Summary of the Sussex-Huawei locomotion-transportation recognition challenge 2019
L. Wang, H. Gjoreski, M. Ciliberto, P. Lago, K. Murao, T. Okita, D. Roggen
-Cross-location transfer learning for the sussex-huawei locomotion recognition challenge
Vito Janko, Martin Gjoreski, Carlo Maria De Masi, Nina Reščič, Mitja Luštrek, Matjaž Gams
-EmbraceNet for activity: a deep multimodal fusion architecture for activity recognition
Jun-Ho Choi, Jong-Seok Lee
-Applying 1D sensor DenseNet to Sussex-Huawei locomotion-transportation recognition challengee
Yida Zhu, Fang Zhao, Runze Chen
-Ceremony |
| 1730- | Closing remarks |