Welcome to HASCA2013

Welcome to HASCA2013 Web site!

Checkout Workshop Pictures!

All papers of HASCA2013 is now Downloadable from Here!
All presentation slides are available from Here! (after the questionnaires.)

Invited speaker is Afra Mashhadi (Alcatel-Lucent Bell Labs).

HASCA2013 is a first workshop for Human Activity Sensing Corpus and its Application. The workshop is held in conjunction with UbiComp2013 at Zurich.

Workshop day is Sep 8th(Sun),the first day of the conference(Sep8-12).
Workshop venue is ETH Zurich Downtown Campus, room CHN E 46.


To understand human activities using various sensors such as accelerometers and gyroscopes in recent smartphones/wearable devices, a large scale human activity sensing corpus might play an important role. Also, it is a great challenge to utilize such enormous number of wearable sensors to collect large-scale activity corpus. In this workshop, we will share the experiences of current researches on human activity corpus and its applications among the researchers and the practitioners and to have a deep discussion for the future of human activity understanding.

We solicit the following topics (but not limited to).

Data collection / Corpus construction

Experiences or reports from the data collection and/or corpus construction projects. Also includes the papers which 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 technology, 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.

Invited Talk by Afra Mashhad

     Afra Mashhadi, Alcatel-Lucent Bell Labs

Afra Mashhadi is a research scientist in the domain of ubiquitous computing in Bell Laboratories. In her previous appointment, she was a post-doctoral researcher first at UCL and then in Bell Labs. Her research interest spans from opportunistic and sensor networks to HCI. Her current research focuses on understanding users participation, accuracy and coverage of ubiquitous crowd-sourcing systems where users voluntarily contribute real-time information about spatial objects/events.

Title :  
    A perfect partnership: Activity Recognition and the Crowd

  Recognising and modelling human activity can be applied to a diverse range of real-world applications, from intelligent transportation where one can infer delays occurring in metro line by monitoring the accelerometer of travellers smartphones, to surveillance systems that can detect anomalies and/or problematic behaviours in different neighbourhood. However, taking these models out of the lab context and deploying them into real-world scenarios is not an easy task, as they often require a priori training that is highly dependent on the particular context of the activity. A possible solution is to leverage crowd-sourcing systems so to train the activity recognition on the fly based on the input of the crowd. In this talk we discuss the pros and cons of leveraging the crowd-sourcing techniques in partnership with AR models and present the challenges that need to be faced in this domain.


Sep 8th (Sunday)




Opening remarks from workshop organizers

Invited talk:
  Afra Mashhadi, Altcatel-Lucent Bell Labs.
      "A perfect partnership: Activity Recognition and the Crowd"


Session 1, Chair: Kaori Fujinami

2 short papers (15min talk + 5min each)

Automatic Correction of Annotation Boundaries in Activity Datasets by Class Separation Maximization

Reuben Kirkham, Aftab Khan(Newcastle Univ.), Sourav Bhattacharya(Univ. of Helsinki), Nils Hammerla, Sebastian Mellor, Daniel Roggen, Thomas Plotz(Newcastle Univ.)

HASC-IPSC: Indoor Pedestrian Sensing Corpus with a Balance of Gender and Age for Indoor Positioning and Floor-plan Generation Researches

Katsuhiko Kaji, Hodaka Watanabe, Ryoji Ban, Nobuo Kawaguchi(Nagoya Univ.)


Coffee Break


Session 2, Chair: Sozo Inoue

3 full papers (25min talk + 5-10min dis)

Evaluation Function of Sensor Position for Activity Recognition considering Wearability

Kazuya Murao, Haruka Mograi, Tsutomo Terada, Masahiko Tsukamoto(Kobe Univ.)

Improving Fault Tolerance of Wearable Sensor-based Activity Recognition Techniques

Ryoma Uchida, Hiroto Horino, Ren Ohmura(Toyohashi Univ. of Tech.)

Detecting Wi-Fi Base Station Behavior Inappropriate for Positioning Method in Participatory Sensing Logs

Nobuhiko Nishio, Yuuki Fukuzaki, Takuya Azami(Ritsumeikan Univ.)




Session 3, Chair: Nobuhiko Nishio

3 full papers (25min talk + 5-10min dis)

Labeling Method for Acceleration Data using an Execution Sequence of Activities

Kazuya Murao, Tsutomu Terada(Kobe Univ.)

Parallel, Distributed, and Differential Processing System for Human Activity Sensing Flows

Takamichi Toda, Sozo Inoue, Lin Li(Kyushu Institute of Tech.)

Sharing Training Data among Diffferent Activity Classes

Quan Kong, Takuya Maekawa(Osaka Univ.)


Coffee Break


Session 4, Chair: Daniel Roggen

1 short + 3 full papers

PDR-Based Adaptation for User-Progress in Interactive Navigation System

Shun Yoshimi, Takuya Azami, Nobuhiko Nishio(Ritsumeikan Univ.)

Pointing Gesture Recognition using Compressed Sensing for Training Data Reduction

Masahiro Iwasaki, Kaori Fujinami(Tokyo Univ. of Agriculture and Tech.)

Parameter Exploration for Response Time Reduction in Accelerometer-based Activity Recognition

Minoru Yoshizawa, Wataru Takasaki, Ren Ohmura(Toyohashi Univ. of Tech.)

Monitor and Understand Pilgrims: Data Collection using Smartphones and Wearable Devices

Amir Muaremi, Julia Seiter, Gerhard Troster(ETH Zurich), Agon Bexheti(EPFL)



Discussion and Closing Remarks


Call for papers

Scope and Aims

Recent advancement of technology enables installations of small sized accelerometers or gyroscopes on various kinds of wearable/portable information devices. By using such wearable sensors, these devices can estimate its posture or status. However, most of current devices only utilize these sensors for simple orientation and gesture recognition. More deep understandings and recognition of human activity through these sensors will enable the next-generation human-oriented computing. To enable the real-world application by these kinds of wearable sensors, a large scale human activity corpus might play an important role. Additionally, we have now a lot of high-performance mobile devices in real-world such as smart-phones. It is a great challenge to utilize such enormous number of wearable sensors to collect large-scale activity corpus. In recent years, there are several on-going projects which are collecting human activities. In this workshop, we are planning to share these experiences of current research on human activity corpus and its applications among the researchers and the practitioners and to have a deep discussion for future of activity sensing.

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

PDF version of CFP