Tahera Hossain

Research Assistant Professor, Nagoya University, Japan

Email: taheramoni@gmail.com

My research focuses on modeling and understanding human behaviors in the healthcare domain. My work involves computational modeling in Human-Computer Interaction (HCI) by modeling behaviors of people based on the data collected from people’s mobile, smart wearable devices, and their instrumented environments. My current research looks at ways to understand and measure the health status and outcomes of individuals. I analyze real-world data collected from everyday situations, such as behavioral and physiological data, as well as nursing care and patient records, to gain insights into the subject matter. I use machine learning to understand patients/elderly people’s real-life complex activities and modeling of human ambulatory multimodal time-series data, including physiological, biological, and behavioral data for measuring, predicting, improving, and understanding human physiology and behavior for health, wellbeing, and performance.

I work in the domains of Applied Machine Learning, Computational Modeling, and Human-Computer Interaction (HCI). My primary focus of work is to analyze how activities and behaviors of patients/elderly correlate with their disease symptoms and how technology can coach them to modify their complex routines to be productive, healthy, and safe. I also work on the design and development of robust human behavior recognition methods, which are able to detect people's behavior in real-world settings by combating the challenges faced in such settings. My future goal is to create technology that will automatically infer user goals, predicts future user behaviors, describes common user behaviors, and guides users to follow their healthy routine life.

Highlighted Research Projects

★ Human Behavior Modeling and Computational Interaction [UM]

I was invited as a research scholar to work at the University of Michigan, USA. I worked in researching all the potential ways there are to deliver behavior-based healthcare interventions to patients with Multiple Sclerosis (MS) in their homes. This research is important because recent evidence has shown that behavior-based healthcare interventions (e.g., physical rehabilitations) can reduce relapses in patients with MS. However, lack of clinician oversight limits the benefit of such interventions when patients are performing such behavior-based interventions at their homes without clinician guidance. Although, lack of precise rules for determining how much data we need to accurately estimate the parameters of such behavior models remains one of the challenges. We present a Bayesian sample size determination method based on Uncertainty Quantification (UQ) for a specific Inverse Reinforcement Learning (IRL)-based human behavior modeling approach, which we illustrated on a real problem of modeling behaviors of people with Multiple sclerosis (MS).

ACM Transactions on Computer-Human Interaction (TOCHI), A Bayesian Approach for Quantifying Data Scarcity when Modeling Human Behavior via Inverse Reinforcement Learning (Presented CHI2023) [PDF]

★ Office Workers Behaviors, Mental Health, and Productivity Prediction Using Multimodal Data [Kyutech, Rice, NTT]

This is the project collaborating with Rice University, Kyushu Institute of Technology, and NTT Data Institute of Management Consulting, Inc., Tokyo, Japan. Under this project, we have collected 100 office workers’ real-field multimodal data by collaborating with a company in Japan, where the dataset contains objective physiological and behavioral sensor data, and work activities and work environment information with different psychological state information. The dataset depicts the status of real office workers states performing their everyday work under real-life stressors. This project's work focuses on understanding office workers stressors, including physical and mental exhaustion, due to different behavioral, work environmental, workplaces, and work types issues, which is also a social problem. Ongoing work focuses on revealing work and behavioral patterns and routines between Office work and Telework using a real-world multimodal dataset including physical activity sensors data, work activity, work environment, work engagement, and psychological status data. We are working to build models for revealing daily work and behavioral patterns while people are working at home and office. We are building interpretable and personalized models to predict the different mental states of office workers and analyze their day-to-day behavior and psychological state to reveal the factors in the workplace that affect their psychological states.

Activity and Behavior Computing (ABC 2021),Toward the Analysis of Office Workers’ Mental Indicators Based on Wearable, Work Activity, and Weather Data -Best Paper Award [PDF]

★ Heatstroke Prevention from Physiological Factors [AGU, Fujitsu]

Heat-related deaths have been increasing recently worldwide. According to Office for National Statistics, more than 10,000 people died due to heat-related illness since 2017. In Japan, ambulance due to heatstroke was record high for June 2022, also 56,303 deaths in England and Wales in 2022. The mortality rate due to overheating is estimated to increase by 260% by the 2050s. So, it is important to understand heatstroke symptoms in advance. I have been working on this project to develop an intelligent system to predict and prevent heatstroke from human physiological factors. In this project, I have been working to understand work stress, mental stress, and environmental stress under different activities, where it will be possible to model and understand human behaviors under different stressing factors. I worked with Fujitsu General Ltd for data collection. I have been building a reliable system that can effectively identify the factors responsible for heatstroke.

Activity and Behavior Computing (ABC 2022), Toward Human Thermal Comfort Sensing: New Dataset and Analysis of Heart Rate Variability (HRV) Under Different Activities-Excellent Paper Award

Featured

Presenting at the CHI2023

Delighted to give my first talk at CHI2023, representing the CompHCI Lab, University of Michigan. I talked about how to conduct sample size determination when modeling human behavior using Inverse Reinforcement Learning (IRL). ACM CHI Conference on Human Factors in Computing Systems is the premier international conference of Human-Computer Interaction (HCI) (CHI2023, Hamburg, Germany).

Representing CompHCI Lab (University of Michigan) at CHI2023

CompHCI lab at CHI2023 in one place, taking a break after the paper presentation.

Activity and Behavior Computing Conference (ABC 2022)

Delighted to receive the Excellent Paper Award in the Activity and Behavior Computing Conference, 2022, London, UK.

Best Ph.D. Forum Presentation Award at IEEE PerCom 2019

17th IEEE International Conference on Pervasive Computing and Communications (PerCom), Kyoto, Japan. Best Ph.D. Forum Presentation for 'Sensor-based Daily Activity Understanding in Caregiving Center'. PerCom is the premier annual scholarly venue in pervasive computing and communications.

Best Poster Paper Award at UbiComp

ACM International Conference on Pervasive and Ubiquitous Computing (Ubicomp’18), Singapore. UbiComp / ISWC is a premier interdisciplinary venue in which leading international researchers, designers, developers, and practitioners in the field present and discuss novel results in all aspects of ubiquitous, pervasive, and wearable computing.