Wenbin Gan

Wenbin Gan

Researcher, National Institute of Information and Communications Technology (NICT)

Multimodal Big Data Analysis for Human Wellness

With the widespread use of the IoT, it is expected that advanced services for a smart and sustainable society will be created by interconnecting a wide variety of sensing data to generate and utilize actionable data that is useful for understanding complex situations in the real world and supporting actions appropriate to the situation. I am conducting the research and development of machine learning and data mining technologies for cross-data analysis that appropriately collects sensing data of various types and fields, and discovers, learns, and predicts their cross-sectional associations. Based on these technologies, I am building a platform necessary for the development of smart services that support safe and comfortable transportation and healthy lifestyles taking into account various social issues, and improve the human wellness.

In this direction, I have conducted researches from two perspectives toward improving human wellness: the smart driving support and the personalized sleep support.

Personalized Sleep support


Sleep plays a vital role in our physical, cognitive, and psychological well-being. Despite its importance, long-term monitoring of personalized sleep quality (SQ) in real-world contexts is still challenging. Many sleep researches are still developing clinically and far from accessible to the general public. Fortunately, wearables and IoT devices provide the potential to explore the sleep insights from multimodal data, and have been used in some SQ researches. However, most of these studies analyze the sleep related data and present the results in a delayed manner (i.e., today’s SQ obtained from last night’s data), it is sill difficult for individuals to know how their sleep will be before they go to bed and how they can proactively improve it. To this end, this paper proposes a computational framework to monitor the individual SQ based on both the objective and subjective data from multiple sources, and moves a step further to provide the personalized feedback to improve the SQ in a data-driven manner. The feedback is implemented by referring the insights from the PMData dataset based on the discovered patterns between life events and different levels of SQ. The deep learning based personal SQ model (PerSQ), using the long-term heterogeneous data and considering the carry-over effect, achieves higher prediction performance compared with baseline models. A case study also shows reasonable results for an individual to monitor and improve the SQ in the future.


[keywords]: Sleep Quality Monitoring, Life Event Patterns, Personalized Feedback, Lifelogs

  • Wenbin Gan, Minh-Son Dao, Koji Zettsu, “Monitoring and Improving Personalized Sleep Quality from Long-Term Lifelogs,” 2022 IEEE International Conference on Big Data (Big Data), Osaka, Japan, 2022, pp. 4356-4364, doi: 10.1109/BigData55660.2022.10020829.

Personalized On-board Driving Assistance


Driver reaction is of vital importance in risk sce- narios. Drivers can take correct evasive maneuver at proper cushion time to avoid the potential traffic crashes, but this reaction process is highly experience-dependent and requires various levels of driving skills. To improve driving safety and avoid the traffic accidents, it is necessary to provide all road drivers with on-board driving assistance. This study explores the plausibility of case-based reasoning (CBR) as the inference paradigm underlying the choice of personalized crash evasive maneuvers and the cushion time, by leveraging the wealthy of human driving experience from the steady stream of traffic cases, which have been rarely explored in previous studies. To this end, in this paper, we propose an open evolving framework for gen- erating personalized on-board driving assistance. In particular, we present the FFMTE model with high performance to model the traffic events and build the case database; A tailored CBR- based method is then proposed to retrieve, reuse and revise the existing cases to generate the assistance. We take the 100-Car Naturalistic Driving Study dataset as an example to build and test our framework; the experiments show reasonable results, providing the drivers with valuable evasive information to avoid the potential crashes in different scenarios.


[keywords]: Driving Assistance, Driving Maneuver Recommendation, Naturalistic Driving Study, Traffic Event Modeling, Case-based Reasoning, Intelligent Vehicles

  • Wenbin Gan, Minh-Son Dao, Koji Zettsu, “An Open Case-based Reasoning Framework for Personalized On-board Driving Assistance in Risk Scenarios,” 2022 IEEE International Conference on Big Data (Big Data), Osaka, Japan, 2022, pp. 1822-1829, doi: 10.1109/BigData55660.2022.10020284.