Machine Understanding of Human Behavior

A widely accepted prediction is that computing will move to the background, weaving itself into the fabric of our everyday living spaces and projecting the human user into the foreground. If this prediction is to come true, then next generation computing, which we will call human computing, should be about anticipatory user interfaces that should be human-centered, built for humans based on human models. They should transcend the traditional keyboard and mouse to include natural, human-like interactive functions including understanding and emulating certain human behaviors such as affective and social signaling. This article discusses a number of components of human behavior, how they might be integrated into computers, and how far we are from realizing the front end of human computing, that is, how far are we from enabling computers to understand human behavior

Detection of visitors in elderly care using a low-resolution visual sensor network

Loneliness is a common condition associated with aging and comes with extreme health consequences including decline in physical and mental health, increased mortality and poor living conditions. Detecting and assisting lonely persons is therefore important-especially in the home environment. The current studies analyse the Activities of Daily Living (ADL) usually with the focus on persons living alone, e.g., to detect health deterioration. However, this type of data analysis relies on the assumption of a single person being analysed, and the ADL data analysis becomes less reliable without assessing socialization in seniors for health state assessment and intervention. In this paper, we propose a network of cheap low-resolution visual sensors for the detection of visitors. The visitor analysis starts by visual feature extraction based on foreground/background detection and morphological operations to track the motion patterns in each visual sensor. Then, we utilize the features of the visual sensors to build a Hidden Markov Model (HMM) for the actual detection. Finally, a rule-based classifier is used to compute the number and the duration of visits. We evaluate our framework on a real-life dataset of ten months. The results show a promising visit detection performance when compared to ground truth

Human desire inference process and analysis

Ubiquitous computing becomes a more fascinating research area since it may offer us an unobtrusive way to help users in their environments that integrate surrounding objects and activities. To date, there have been numerous studies focusing on how user\u27s activity can be identified and predicted, without considering motivation driving an action. However, understanding the underlying motivation is a key to activity analysis. On the other hand, user\u27s desires often generate motivations to engage activities in order to fulfill such desires. Thus, we must study user\u27s desires in order to provide proper services to make the life of users more comfortable. In this study, we present how to design and implement a computational model for inference of user\u27s desire. First, we devised a hierarchical desire inference process based on the Bayesian Belief Networks (BBNs), that considers the affective states, behavior contexts and environmental contexts of a user at given points in time to infer the user\u27s desire. The inferred desire of the highest probability from the BBNs is then used in the subsequent decision making. Second, we extended a probabilistic framework based on the Dynamic Bayesian Belief Networks (DBBNs) which model the observation sequences and information theory. A generic hierarchical probabilistic framework for desire inference is introduced to model the context information and the visual sensory observations. Also, this framework dynamically evolves to account for temporal change in context information along with the change in user\u27s desire. Third, we described what possible factors are relevant to determine user\u27s desire. To achieve this, a full-scale experiment has been conducted. Raw data from sensors were interpreted as context information. We observed the user\u27s activities and get user\u27s emotions as a part of input parameters. Throughout the experiment, a complete analysis was conducted whereas 30 factors were considered and most relevant factors were selectively chosen using correlation coefficient and delta value. Our results show that 11 factors (3 emotions, 7 behaviors and 1 location factor) are relevant to inferring user\u27s desire. Finally, we have established an evaluation environment within the Smart Home Lab to validate our approach. In order to train and verify the desire inference model, multiple stimuli are provided to induce user\u27s desires and pilot data are collected during the experiments. For evaluation, we used the recall and precision methodology, which are basic measures. As a result, average precision was calculated to be 85% for human desire inference and 81% for Think-Aloud