Socialoscope: Sensing User Loneliness and Its Interactions with Personality Traits

Loneliness and social isolation can have a serious impact on one’s mental health, leading to increased stress, lower self-esteem, panic attacks, and drug or alcohol addictions. Older adults and international students are disproportionately affected by loneliness. This thesis investigates Socialoscope, a smartphone app that passively detects loneliness in smartphone users based on the user’s day-to-day social interactions, communication and smartphone activity sensed by the smartphone’s built-in sensors. Statistical analysis is used to determine smartphone features most correlated with loneliness. A previously established relationship between loneliness and personality type is explored. The most correlated features are used to synthesize machine learning classifiers that infer loneliness levels from smartphone sensor features with an accuracy of 90%. These classifiers can be used to make the Socialoscope an intelligent loneliness sensing Android app. The results show that, of the five Big-Five Personality Traits, emotional stability and extraversion personality traits are strongly correlated with the sensor features such as number of messages, number of outgoing calls, number of late night browser searches, number of long incoming or outgoing calls and number of auto-joined trusted Wi-Fi SSIDs. Moreover, the classifier accuracy while classifying loneliness levels is significantly improved to 98% by taking these personality traits into consideration. Socialoscope can be integrated into the healthcare system as an early warning indicator of patients requiring intervention or utilized for personal self-reflection

Sensing and modeling human networks

Thesis (Ph. D.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2004.Includes bibliographical references (p. 101-105).This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Knowledge of how groups of people interact is important in many disciplines, e.g. organizational behavior, social network analysis, knowledge management and ubiquitous computing. Existing studies of social network interactions have either been restricted to online communities, where unambiguous measurements about how people interact can be obtained (available from chat and email logs), or have been forced to rely on questionnaires, surveys or diaries to get data on face-to-face interactions between people. The aim of this thesis is to automatically model face-to-face interactions within a community. The first challenge was to collect rich and unbiased sensor data of natural interactions. The "sociometer", a specially designed wearable sensor package, was built to address this problem by unobtrusively measuring face-to-face interactions between people. Using the sociometers, 1518 hours of wearable sensor data from 23 individuals was collected over a two-week period (66 hours per person). This thesis develops a computational framework for learning the interaction structure and dynamics automatically from the sociometer data. Low-level sensor data are transformed into measures that can be used to learn socially relevant aspects of people's interactions - e.g. identifying when people are talking and whom they are talking to. The network structure is learned from the patterns of communication among people. The dynamics of a person's interactions, and how one person's dynamics affects the other's style of interaction are also modeled. Finally, a person's style of interaction is related to the person's role within the network. The algorithms are evaluated by comparing the output against hand-labeled and survey data.by Tanzeem Khalid Choudhury.Ph.D

Identifying Close Friendships in a Sensed Social Network

Studies have suggested that propinquity; social, cultural, physical and psychological similarities are major factors in close friendship ties. These studies were subject to human recall of interactions with no details of length or time of interactions. Recently, advancements in mobile technology have enabled the measurement of complex systems of interactions. This study uses social network analysis of data comprising of time-resolved sensed interactions to predict and explain close friendship ties via interactions at different periods, residence (floor) similarity and gender similarity. Results indicate residence (floor) proximity and duration of weekend night interactions have the potential of explaining close friendship ties.MIT Masdar Progra

Applications of sensors for in-home elder support

The number of retirees in the US is continuously increasing in proportion to the total population as the baby-boomers in the United States age. As these retirees age they are in need on continuous medical treatment and care which impacts the percent of the national budget placed on healthcare. As healthcare issues with the retirees they are often moved by their family members to assisted living facilities or to nursing homes. This movement is costly to both the family members and to the government agencies paying for or subsidizing their care. The proposal brought forth in this thesis is to design a sensor based system that should reduce the need for personnel and enhance elder\u27s quality of life by affording them more independence allowing them to live at home longer; The purpose of this thesis is the evaluation of different sensor types with regard to benefits, specificity of sensor signal to the function being monitored, drawbacks, reliability, acceptance levels by elders, privacy concerns. The design concepts for sensor assembly\u27s configurations under the special set of criteria that must be applied in the homes of elders, information of reliability studies: signal threshold levels, resolution of potential conflicts or false positives. Finally an inference engine R&D: Drawing inferences and conclusions from signals and temporal sequences, correlation with other signals, signal validation and plausibility analysis. (Abstract shortened by UMI.)

Automatic Sensing of Speech Activity and Correlation with Mood Changes

he association between social relationships and psychological health has been established fairly recently, in the last 30-40 years, relying on survey-based methods to record past activities and the psychological responses in individuals. However, using the self-reporting methods for capturing social behavior exhibits a number of shortcomings including recall bias, memory dependence, and a high end user effort for a continuous long-term monitoring. In contrast, automated sensing techniques for monitoring social activity, and in general, human behavior, has a potential to provide more objective measurements thus to overcome the shortcomings of self-reporting methods. In this paper, we present a privacy preserving approach to detect one component of social interactions - the speech activity, through the use of off-the-shelf accelerometers. Furthermore, we used the accelerometer based speech detection method to investigate the correlation between the amount of speech (which is an aspect that reflects the participation in verbal social interactions) and mood changes. Our pilot study suggested that verbal interactions are an important factor that has an impact on individuals’ mood, while the study also demonstrated the potential of automated capturing social activity comparable to the use of gold standard surveys

Collaborative personal speaker identification: A generalized approach

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SALSA: A Novel Dataset for Multimodal Group Behavior Analysis

Studying free-standing conversational groups (FCGs) in unstructured social settings (e.g., cocktail party ) is gratifying due to the wealth of information available at the group (mining social networks) and individual (recognizing native behavioral and personality traits) levels. However, analyzing social scenes involving FCGs is also highly challenging due to the difficulty in extracting behavioral cues such as target locations, their speaking activity and head/body pose due to crowdedness and presence of extreme occlusions. To this end, we propose SALSA, a novel dataset facilitating multimodal and Synergetic sociAL Scene Analysis, and make two main contributions to research on automated social interaction analysis: (1) SALSA records social interactions among 18 participants in a natural, indoor environment for over 60 minutes, under the poster presentation and cocktail party contexts presenting difficulties in the form of low-resolution images, lighting variations, numerous occlusions, reverberations and interfering sound sources; (2) To alleviate these problems we facilitate multimodal analysis by recording the social interplay using four static surveillance cameras and sociometric badges worn by each participant, comprising the microphone, accelerometer, bluetooth and infrared sensors. In addition to raw data, we also provide annotations concerning individuals' personality as well as their position, head, body orientation and F-formation information over the entire event duration. Through extensive experiments with state-of-the-art approaches, we show (a) the limitations of current methods and (b) how the recorded multiple cues synergetically aid automatic analysis of social interactions. SALSA is available at http://tev.fbk.eu/salsa.Comment: 14 pages, 11 figure