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Shimosaka Research Group

pursuing MIUBIQ (machine intelligence in UbiComp Research)

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  • iv_slider
    How to drive safely & defensively?
    Learning from experts.
    Check out our project
  • webslide_urbdyn-1
    Urban Dynamics
    Location data from smartphones
    help us understand demographics of cities.
    Check out our project
  • fullyConnectedCRFs-dataset
    You're not alone.
    Collective Activity Recognition
    with Fully Connected CRFs
    Check out our project
  • top_image_steered_crowdsensning
    Steered CrowdSensing
    You're more valuable than you think.
    Help people with your palm.
    Check out our project
  • wearable
    Pedometers see through the deep inside of your community.
    Working-Relationship Detection
    from Fitbit Sensor Data
    Check out our project

Introduction

Welcome to Shimosaka Research Group.

This laboratory pursues innovative machine intelligence in ubiquitous and pervasive computing.

Research topics include deployments of wearable or ambient sensors for pervasive sensing, intelligent data analytics for modeling human behavior, and lifelog (quantify-self-technology).

The lab. is led by Masamichi Shimosaka who collaborate with graduate students in Dept of Computer Science in ISE/TITECH.

Towards succeeds on next level applications in ubiquitous computing, we collaborate with companies and governmental organizations as well as national scientific foundation in Japan.

Masamichi Shimosaka has moved to CS/ISE/TITECH from IMI/IST/UTokyo since July 2015. The group has restarted as lab. at TITECH!

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Our Projects

GroupWiLo: Accurate and Low-cost Wi-Fi-based Localization with Minimization of Parameter Total Variation

GroupWiLo: Accurate and Low-cost Wi-Fi-based Localization with Minimization of Parameter Total Variation

2020/11/10 |
Adaptive driving behavior modeling towards diverse driving situations using inverse reinforcement learning

Adaptive driving behavior modeling towards diverse driving situations using inverse reinforcement learning

2020/11/02 |
Robust indoor localization across smartphone models with ellipsoid features from multiple RSSIs

Robust indoor localization across smartphone models with ellipsoid features from multiple RSSIs

2017/10/23 |
Early destination prediction by mobile phone sensing

Early destination prediction by mobile phone sensing

2017/02/04 |
Irregularity prediction based on transit app logs

Irregularity prediction based on transit app logs

2017/02/04 |
Working-Relationship Detection from Fitbit Sensor Data

Working-Relationship Detection from Fitbit Sensor Data

2015/10/05 |
Urban dynamics analysis with large-scale Location data from smartphones

Urban dynamics analysis with large-scale Location data from smartphones

2015/10/05 |
Modeling risk anticipation and defensive driving on residential roads using inverse reinforcement learning

Modeling risk anticipation and defensive driving on residential roads using inverse reinforcement learning

2015/08/17 |
Consistent collective activity recognition with fully connected CRFs

Consistent collective activity recognition with fully connected CRFs

2014/09/01 |
Steered Crowdsensing

Steered Crowdsensing

2014/08/06 |
An easy-to-install system for daily walking ability assessment with a distance sensor array

An easy-to-install system for daily walking ability assessment with a distance sensor array

2013/04/01 |
Hand Shape Classification with a Wearable Wrist Contour Sensor

Hand Shape Classification with a Wearable Wrist Contour Sensor

2013/04/01 |
Health score prediction with high-level activity features

Health score prediction with high-level activity features

2013/04/01 |
Frustratingly Simplified Deployment in Wi-Fi Localization via Route Annotation.

Frustratingly Simplified Deployment in Wi-Fi Localization via Route Annotation.

2012/04/01 |
Indoor activity recognition with discriminative deformable part models

Indoor activity recognition with discriminative deformable part models

2012/04/01 |
Modeling and Predicting Going-out Behavior via Dirichlet process mixture

Modeling and Predicting Going-out Behavior via Dirichlet process mixture

2012/04/01 |
Counting People in Crowded Environment

Counting People in Crowded Environment

2011/04/01 |
Driver behavior modeling from multiple drivers’ data

Driver behavior modeling from multiple drivers’ data

2011/04/01 |
Behavior Prediction from Trajectories in a House by Estimating Transition Model Using Stay Points

Behavior Prediction from Trajectories in a House by Estimating Transition Model Using Stay Points

2011/04/01 |
Driver behavior modeling based on Bayesian collaborative learning

Driver behavior modeling based on Bayesian collaborative learning

2010/04/01 |
The Optimization of Sensor Arrangement and Feature Selection in Activity Recognition

The Optimization of Sensor Arrangement and Feature Selection in Activity Recognition

2010/04/01 |

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Recent News

  • Presenting our paper on fine-grained urban anomaly detection at nationwide scale using massive GPS logs on BigData2020 2020/12/11
  • GroupWiLo: Accurate and Low-cost Wi-Fi-based Localization with Minimization of Parameter Total Variation 2020/11/10
  • Adaptive driving behavior modeling towards diverse driving situations using inverse reinforcement learning 2020/11/02
  • Presenting our paper on accurate anomaly crowd prediction at ACM SIGSPATIAL 2020 2020/10/27
  • Presenting our paper on neural-based time-series multi-label classification problem towards reliable activity recognition on construction vehicles at IROS2020 2020/10/26
  • Presenting our paper on Wi-Fi-based indoor localization maintenance method via group-wise total variation regularization at IPIN2019 2019/10/01
  • Presenting our project on VR art “CityAtmosphere” in UbiComp 2019 Demos. 2019/09/04
  • Presenting our paper on large-scale fine-grained urban dynamics prediction at AAAI2019 2019/01/28
  • Presenting our paper on population pattern prediction at ACM SIGSPATIAL2018 2018/11/01
  • New members of September 2018 2018/10/19
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