Ubiquitous and Personal Computing Lab

Faculty of Engineering, Kyoto University of Advanced Science (KUAS), Japan

#Quantified-Self  #consumerWearables  #softwareCentered  #digitalHealth  #digitalEducation  #predictiveModelling  #signalProcessing  #dataMining  #humanComputerInteraction

Computing for Anyone, at Anytime, Anywhere

We research, develop and validate novel computing technologies to improve health, productivity, and well-being of individuals.  

Devoted to creating computing technologies that can be used anytime, anywhere, by anyone, we envision a world where everyone is able to design and conduct self-experiments to test their personal hypothesis, to gain a better understanding into the-self, and to maximize their potential.

Sensing

This research track focuses on developing novel software sensors with consumer wearble hardware for measuring psychological and physiological states in free-living environments.

Subtrack 1-1: Sleep Sensing

Subtrack 1-2: Wearable Brain Imaging

Subtrack 1-3: Mental States Sensing

Subtrack 1-4: Activity Recognition

Subtrack 1-5: Glucose Sensing

#sleepTracking   #eyeTracking   #glucoseSensing    #depressionDetection   #fNIRS  #brainImaging #signalProcessing #machineLearning 

Informatics

This research track focuses on developing novel analytical and predictive modelling methods for gaining insights into the quantified-self data collected using off-the-shelf consumer wearables.

Subtrack 2-1: Quantified-Self Data Mining

Subtrack 2-2: Large-Scale Longitudinal Collection of QS Data

#personalInformatics   #consumerInformatics  #dataVisualization   #dataMining  #machineLearning    #timeSeriesAnalysis   

Interaction

This research track focuses on designing and developing software applications (mobile apps, smartwatch apps, web apps) to engineer people's behavior towards better health, productivity and wellbeing.

Subtrack 3-1:  mHealth App Development

Subtrack 3-2:  mLearning App Development

Subtrack 3-3: Human-Computer Interaction in mHealth & mLearning

#appDevelopment    #gamification  #behaviorChangeTheory    #mHealth  #digitalHealth   #HCI   #soundTherapy  #mobileLearning #nudge

NEWS

    • Nhung H. H., Liang Z. (2023) Identifying Patterns in Continuous Glucose Monitoring Data using Contrast Set Mining.
    • Liang Z. (2023) Gamifying Digital Signal Processing Learning with Kahoot! Quiz.

Address

〒615-8577  京都市右京区山ノ内五反田町18番地 南館工学部5階研究室508

South Building F5-508, Faculty of Engineering

Kyoto University of Advanced Science (KUAS)

18 Yamanouchi Gotanda-cho, Ukyo-ku, Kyoto 615-8577, Japan

E-mail: ucpi.kuase[at]gmail.com

Please check lab availability before scheduling any experiment.