Research

Research areas for students

Human-centered modeling driven by real-time sensory and feedback devices (trackers, accelerometers, haptics, lidars, EEG, EMG, RGB(-D) cameras, etc. )

Motion capture, transfer, and biomechanical analysis for sports, healthcare, and ergonomics applications; human posture analysis; fall detection

Machine learning for human behavior and state detection, monitoring and analysis; hand gesture recognition; hand tracking

Autonomous driving in urban environment

Pedestrian and cyclist detection in different lighting and traffic conditions

Smartphones, wearable devices and sensors for human daily activity monitoring

Some recent thesis titles of Graduation Research of our students

Posture Corrector: Incorrect Posture and Repetition Identifier using OpenPose and Deep Neural Network

Detection of Cyclist’s Crossing Intention based on Posture Estimation for Autonomous Driving

E-Worker Mental Fatigue Detection through Mindwave EEG Data and Deep Neural Networks

Driver Hand Recognition System using Near-Infrared Camera

Comparison of Image-Based and Skeleton-Based Machine Learning Methods in the Task of Alphabetical Sign Language Recognition

Augmented Reality-based Indoor Navigation Using SLAM and Hybrid Map Information

Pedestrian Detection in Different Lighting Conditions using Deep Neural Networks and Multispectral Images

Air Writing in Japanese: A CNN-based character recognition system using hand tracking

Body shape modeling

As a basis for the visualization systems, we develop real-time algorithms and methods for gridding, volume rendering, 3D segmentation, mesh reconstruction, and morphing, which are specifics for human-oriented modeling. We conduct body shape modeling using Digital Human manikin models and 3D full-body scan data. Modeling of body shapes can be done for the scenarios of weight gain/loss, muscularity gain, and effects of ageing, which are important for healthcare and beauty services.

Motion Analysis

Combining with motion capture (MoCap) data, 3D scans are used to estimate dynamic parameters, necessary to realistically model human movements. Digital Hand models are used for ergonomics factors estimation, as such as grasp quality and stability. We pay attention to visualization systems with haptic devices for VR surgery and VR nursing. Digital Human modeling is driven by real-time sensory and feedback devices (trackers, accelerometers, haptics), and utilizes experimentally collected data bases (3D scans, CT/MRI data). We also conduct experimental processing of data collected from haptic devices to model and predict human hand movement in constraint dynamic environments and to study human balancing and reaction skills.

Autonomous Driving in Urban City

Autonomous driving is one of the promising technologies that can enhance safety and mobility. In the near future, self-driving vehicles will inevitably coexist with human-driving vehicles. To harmoniously share traffic resources, self-driving vehicles have to learn behavioral customs from human drivers. Incorporating human-driver traits into how autonomous vehicles drive is a significant topic. This research will build a machine-learning based model to learn the human driver’s perception and decision-making ability in the complex and crowded urban city.

Wearable Smart Glasses based Navigation

The Augmented Reality (AR) navigation system in smart glasses could provide a new experience for pedestrians compared to the conventional navigation in the smartphone. An accurate positioning technology is fundamental to a satisfied AR based navigation service. However, Global Navigation Satellite System (GNSS), Pedestrian Dead Reckoning (PDR) and Wi-Fi, those positioning methods all cannot have a satisfied performance in either indoor or outdoor of the urban city environment. This research will leverage camera sensing ability and the rich information in open map sources to improve the positioning accuracy. Moreover, the navigation information will be visualized by integrating with the real scene based on the online and real-time object detection and matching technology.