|Dates:||2016 – 2020|
|Researchers:||Igor Kulev and Pearl Pu|
|Keywords:||activities of daily living, machine learning, timeseries analysis, clustering|
While an increase in physical activity levels has been shown to be the most effective and important intervention strategy to improve health in elderly populations, it is notoriously difficult to increase physical activity, mobilization, and rehabilitation among older adults. We propose a lifestyle recommender system to help senior adults with sedentary habits to become more active. In that context methods are developed to cluster existing users based on how a given intervention of behavior change affects their current habits modelled as time series data.
|Title:||Behavior Recommender Systems|
|Researchers:||Onur Yuruten and Pearl Pu|
|Keywords:||Activities of daily living, activity recognition, data mining, time series analysis, statistical analysis, behavior recommenders|
|Abstract:||Sedentary lifestyles influence the onset of many serious health problems. Healthy behavior change is an arduous task, but behavior recommenders can help their users achieve it. This novel type of recommender system can suggest its users to pair up with an exercise partner, or propose a day-by-day plan to increase their physical activeness. We quest for understanding the factors that influence well-beings of people, and develop a behavior recommender system that exploits these factors to provide suggestions for healthier lifestyles. Toward this end, we analyse various aspects of activities of daily living, such as physical exercises, work, sleep, social interactions, and daily routines. We utilize state-of-the-art timeseries analysis methods to explore trends, repetitive patterns, regularities and correlations from data such as bluetooth proximity, cell-tower locations, calorie expenditure, and other various information collected from pervasive, wearable sensors. We also show that traditional factor analysis methods can also be applied to sensor data.|
|Title:||Dystemo: Distant Supervision for Emotion Recognition in Tweets|
|Dates:||2013 – 2016|
|Researchers:||Valentina Sintsova and Pearl Pu|
|Keywords:||emotion recognition, social media analysis, distant supervision, twitter|
Emotion recognition in text has become an important research objective. It involves building classifiers capable of detecting human emotions for a specific application, e.g. analyzing reactions to product launches, monitoring emotions at sports events, or discerning opinions in political debates. Most successful approaches rely heavily on costly manual annotation. To alleviate this burden, we propose a distant supervision method—Dystemo—for automatically producing emotion classifiers from tweets labeled using existing or easy-to-produce emotion lexicons. The goal is to obtain emotion classifiers that work more accurately for specific applications than available emotion lexicons.
|Title:||Emotion Recognition of Influential Users|
|Dates:||2013 – present|
|Researchers:||Lionel Martin and Pearl Pu|
|Keywords:||emotion recognition, influence detection, prediction, social media analysis, natural language processing, Geneva Emotion Wheel|
People increasingly rely on other consumers’ opinion to make online purchase decisions. Amazon alone provides access to millions of reviews, risking to cause information overload to an average user. Recent research has thus aimed at understanding and identifying reviews that are considered influential. Most of such works analyzed the structure and connectivity of social networks to identify influential users. We believe that insight about influence can be gained from analyzing the affective content of the text as well as affect intensity. We employ text mining to extract the emotionality of thousands of reviews in different domains to investigate how those influential users behave. We analyze whether texts with words and phrases indicative of a writer’s emotions, moods, and attitudes are more likely to trigger a genuine interest compared to more neutral texts.
|Sponsor:||Swiss National Science Foundation|
|Title:||Visual Analytics for Pervasive Sensor Data|
|Researchers:||Onur Yuruten and Pearl Pu|
|Keywords:||Activities of daily living, visual analytics, visualisation|
Advances in sensor technology have enabled the deployment of pervasive healthcare systems in increasingly larger scales. There is a ‘quantified self’ movement, which involves using wearable sensors to quantify external activities of daily living (ADL) and internal metabolism. Many studies show that these systems can improve well-being of their users via presenting such information with self-reflection tools. However, there is a lack of visualization tools to aid analytical investigations over the data collected from these systems, which could improve our understanding on factors that influence well-being, and guide the design for better wellness management systems.
We develop a visual analytics tool to discover the interpersonal and intrapersonal patterns of ADLs. With this tool, we aim to observe possible influencing factors of well-being, which could be use to construct or validate models for statistical analysis.
This project is part of a larger framework of lifestyle recommenders research, and proceeds in parallel with “mining and analysing factors of well-being project.