Analyzing Sensor-Based Individual and Population Behavior Patterns via Inverse Reinforcement Learning

Digital markers of behavior can be continuously created, in everyday settings, using time series data collected by ambient sensors. The goal of this work was to perform individual- and population-level behavior analysis from such time series sensor data. In this paper, we introduce a novel algorithm-Resident Relative Entropy-Inverse Reinforcement Learning (RRE-IRL)-to perform an analysis of a single smart home resident or a group of residents, using inverse reinforcement learning. By employing this method, we learnt an individual\u27s behavioral routine preferences. We then analyzed daily routines for an individual and for eight smart home residents grouped by health diagnoses. We observed that the behavioral routine preferences changed over time. Specifically, the probability that the observed behavior was the same at the beginning of data collection as it was at the end (months later) was lower for residents experiencing cognitive decline than for cognitively healthy residents. When comparing aggregated behavior between groups of residents from the two diagnosis groups, the behavioral difference was even greater. Furthermore, the behavior preferences were used by a random forest classifier to predict a resident\u27s cognitive health diagnosis, with an accuracy of 0.84

Studying Spread Patterns of COVID-19 based on Spatiotemporal Data

The current COVID-19 epidemic have transformed every aspect of our lives, especially our behavior and routines. These changes have been drastically impacting the economy in each region, such as local restaurants and transportation systems. With massive amounts of ambient data being collected everywhere, we now can develop innovative algorithms to have a much greater understanding of epidemic spread patterns of COVID-19 based on spatiotemporal data. The findings will open up the possibility to design adaptive planning or scheduling systems that will help preventing the spread of COVID-19 and other infectious diseases. In this tutorial, we will review the trending state-of-theart machine learning techniques to model epidemic spread patterns with spatiotemporal data. These techniques are organized from two aspects: (1) providing a comprehensive review of recent studies about human routine behavior modeling, such as inverse reinforcement learning and graph neural network, and the impacts of behaviors on the spread patterns of infectious diseases based on GPS data; (2) introducing the existing literature on using remote sensing data to monitor the spatiotemporal pattern of the epidemic spread. Under current epidemic with unknown lasting time, we believe that modeling the spread patterns of COVID-19 epidemic is an important topic that will benefit to researchers and practitioners from both academia and industry

Using continuous sensor data to formalize a model of in-home activity patterns

Formal modeling and analysis of human behavior can properly advance disciplines ranging from psychology to economics. The ability to perform such modeling has been limited by a lack of ecologically-valid data collected regarding human daily activity. We propose a formal model of indoor routine behavior based on data from automatically-sensed and recognized activities. A mechanistic description of behavior patterns for identical activity is offered to both investigate behavioral norms with 99 smart homes and compare these norms between subgroups. We identify and model the patterns of human behaviors based on inter-arrival times, the time interval between two successive activities, for selected activity classes in the smart home dataset with diverse participants. We also explore the inter-arrival times of sequence of activities in one smart home. To demonstrate the impact such analysis can have on other disciplines, we use this same smart home data to examine the relationship between the formal model and resident health status. Our study reveals that human indoor activities can be described by non-Poisson processes and that the corresponding distribution of activity inter-arrival times follows a Pareto distribution. We further discover that the combination of activities in certain subgroups can be described by multivariate Pareto distributions. These findings will help researchers understand indoor activity routine patterns and develop more sophisticated models of predicting routine behaviors and their timings. Eventually, the findings may also be used to automate diagnoses and design customized behavioral interventions by providing activity-anticipatory services that will benefit both caregivers and patients

Sentiment Analysis of Long-term Social Data during the COVID-19 Pandemic

The COVID-19 pandemic has bringing the “infodemic” in the social media worlds. Various social platforms play a significant role in instantly acquiring the latest updates of the pandemic. Social media such as Twitter and Facebook produce vast amounts of posts related to the virus, vaccines, economics, and politics. In order to figure out how public opinion and sentiments are expressed during the pandemic, this work analyzes the long-term social posts from social media and conducts sentiment analysis on tweets within 12 months. Our findings show the trend topics of long-term social communities during the pandemic and express people’s attitudes towards progress of major actions during the pandemic. We explore the main topics during the prolonged pandemic, including information surrounding economics, vaccines, and politics. Besides, we show the differences in gender-based attitudes and propose future research questions refer to the “infodemic”. We believe that our work contributes to attracting public attention to the “infodemic” of the social crisis

POPULATION-LEVEL BEHAVIOR ANALYSIS BASED ON SMART ENVIRONMENT SENSOR DATA

Sensor data that are collected as people perform ordinary routines in their homes provide insights about human behavior. Comparing sensor data-derived norms among population subgroups offers the potential to transform how important services are delivered in millions of homes. With massive amounts of available sensor data, we have entered an era in which a much greater understanding of complex behavior can be gained through the development of innovative algorithms to analyze such sensor data.This dissertation focuses on population-level behavior analysis on smart environment sensor data. We leverage decades of behavioral sensor data from over 100 smart homes to identify routine behavior patterns and assess behavior changes with a view to applying our findings to personalized healthcare.We design three methods to formally model resident behavior based on smart home sensor data. We first introduce two stochastic methods with mechanistic descriptions of behavior patterns to model resident behavior at a population level and compare behavior differences among population groups. We also design a data-driven approach, inverse reinforcement learning, to model and quantify residents’ behavior as well as to distinguish cognitively impaired groups from healthy populations. The findings will offer the potential to automate diagnoses and design customized behavioral interventions as well as inform strategies for improving people’s health-promoting daily habits

The Study on Dynamic Characteristics of Twin-Screw Compressor Rotor

In the working process of twin-screw compressor, the rotors are subjected to multiple physical effects of the gas temperature, pressure and force, and presents a periodic change. In this paper, three-dimensional Computational Fluid Dynamics (CFD) simulation of screw compressor is carried out, and the characteristics of temperature distribution, pressure distribution and gas force distribution on the rotors’ surface are studied. Firstly, the rotor domain, suction and exhaust face grids are generated by TwinMesh, and then they are imported into the CFX calculation model. Through the fluid-structure interaction analysis and calculation, the strain shape of the screw rotor under the multiple alternating physical action of gas temperature, pressure and force is analyzed, and the deformation of the rotor structure caused by the pressure and temperature in the working process is obtained, which plays a good guiding role in the design of the screw rotor and the improvement of the performance of the compressor

Analyzing Sensor-Based Individual and Population Behavior Patterns via Inverse Reinforcement Learning

Digital markers of behavior can be continuously created, in everyday settings, using time series data collected by ambient sensors. The goal of this work was to perform individual- and population-level behavior analysis from such time series sensor data. In this paper, we introduce a novel algorithm—Resident Relative Entropy-Inverse Reinforcement Learning (RRE-IRL)—to perform an analysis of a single smart home resident or a group of residents, using inverse reinforcement learning. By employing this method, we learnt an individual’s behavioral routine preferences. We then analyzed daily routines for an individual and for eight smart home residents grouped by health diagnoses. We observed that the behavioral routine preferences changed over time. Specifically, the probability that the observed behavior was the same at the beginning of data collection as it was at the end (months later) was lower for residents experiencing cognitive decline than for cognitively healthy residents. When comparing aggregated behavior between groups of residents from the two diagnosis groups, the behavioral difference was even greater. Furthermore, the behavior preferences were used by a random forest classifier to predict a resident’s cognitive health diagnosis, with an accuracy of 0.84

FACES: A Deep-Learning-Based Parametric Model to Improve Rosacea Diagnoses

Rosacea is a chronic inflammatory skin disorder that causes visible blood vessels and redness on the nose, chin, cheeks, and forehead. However, visual assessment, the current standard method used to identify rosacea, is often subjective among clinicians and results in high variation. Recent advances in artificial intelligence have allowed for the effective detection of various skin diseases with high accuracy and consistency. In this study, we develop a new methodology, coined “five accurate CNNs-based evaluation system (FACES)”, to identify and classify rosacea more efficiently. First, 19 CNN-based models that have been widely used for image classification were trained and tested via training and validation data sets. Next, the five best performing models were selected based on accuracy, which served as a weight value for FACES. At the same time, we also applied a majority rule to five selected models to detect rosacea. The results exhibited that the performance of FACES was superior to that of the five individual CNN-based models and the majority rule in terms of accuracy, sensitivity, specificity, and precision. In particular, the accuracy and sensitivity of FACES were the highest, and the specificity and precision were higher than most of the individual models. To improve the performance of our system, future studies must consider patient details, such as age, gender, and race, and perform comparison tests between our model system and clinicians