Wearable sensors for pervasive healthcare management

Olguı́n, D.; Gloor, Peter A.; Pentland, Alex

doi:10.4108/icst.pervasivehealth2009.6033

Cited by 72 publications

(50 citation statements)

References 11 publications

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“…According to [46], Conscientiousness negatively correlates with closeness, betweenness and degree centrality; Extraversion and Neuroticism (the latter in a less evident manner) positively correlate with degree, closeness, betweenness and eigenvector centrality. Olguin et al [36] obtained evidence for the negative correlation of Conscientiousness and betweenness centrality. In a more recent study conducted by Gloor et al [17], the authors found significant positive correlations between Openness and Agreeableness and degree and betweenness centrality.…”

Section: Centrality Measuresmentioning

confidence: 99%

“…Recently, however, researchers in social and ubiquitous computing have started exploring the wealth of behavioral data made available by smartphones, wearable sensors (e.g., sociometric badges [36]), Facebook [39] and Twitter [18,38]. Exploiting sociometric badges, Olguin et al [36] found that Extraversion and Neuroticism were positively correlated with degree, closeness, betweenness, and eigenvector centrality measures. Moreover, they found a negative correlation between Conscientiousness and betweenness centrality.…”

Section: Previous Work In Social and Ubiquitous Computingmentioning

confidence: 99%

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Friends don't lie

Staiano

Lepri

Aharony

et al. 2012

Proceedings of the 2012 ACM Conference on Ubiquitous Computing

133

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In this work, we investigate the relationships between social network structure and personality; we assess the performances of different subsets of structural network features, and in particular those concerned with ego-networks, in predicting the Big-5 personality traits. In addition to traditional survey-based data, this work focuses on social networks derived from real-life data gathered through smartphones. Besides showing that the latter are superior to the former for the task at hand, our results provide a fine-grained analysis of the contribution the various feature sets are able to provide to personality classification, along with an assessment of the relative merits of the various networks exploited.

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Section: Centrality Measuresmentioning

confidence: 99%

Section: Previous Work In Social and Ubiquitous Computingmentioning

confidence: 99%

Friends don't lie

Staiano

Lepri

Aharony

et al. 2012

Proceedings of the 2012 ACM Conference on Ubiquitous Computing

133

View full text Add to dashboard Cite

show abstract

“…These studies use the abundance of personal data from different domains such as short essays, blog posts [21], social media profiles [18], surveillance with wearable sensors [22,23], computer game behavior [24] and videos containing interaction between people [10,17,20].…”

Section: Prediction Of Personality Traitsmentioning

confidence: 99%

Multi-domain and multi-task prediction of extraversion and leadership from meeting videos

Kındıroğlu

Akarun

Aran

2017

J Image Video Proc.

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Automatic prediction of personalities from meeting videos is a classical machine learning problem. Psychologists define personality traits as uncorrelated long-term characteristics of human beings. However, human annotations of personality traits introduce cultural and cognitive bias. In this study, we present methods to automatically predict emergent leadership and personality traits in the group meeting videos of the Emergent LEAdership corpus. Prediction of extraversion has attracted the attention of psychologists as it is able to explain a wide range of behaviors, predict performance, and assess risk. Prediction of emergent leadership, on the other hand, is of great importance for the business community. Therefore, we focus on the prediction of extraversion and leadership since these traits are also strongly manifested in a meeting scenario through the extracted features. We use feature analysis and multi-task learning methods in conjunction with the non-verbal features and crowd-sourced annotations from the Video bLOG (VLOG) corpus to perform a multi-domain and multi-task prediction of personality traits. Our results indicate that multi-task learning methods using 10 personality annotations as tasks and with a transfer from two different datasets from different domains improve the overall recognition performance. Preventing negative transfer by using a forward task selection scheme yields the best recognition results with 74.5% accuracy in leadership and 81.3% accuracy in extraversion traits. These results demonstrate the presence of annotation bias as well as the benefit of transferring information from weakly similar domains.

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“…The sociometric badge collects communication patterns of individuals by monitoring physical proximity, physical activity levels, and conversational times to quantify social interactions, group behavior, and organizational dynamics. It has also been used in the context of personal healthcare management [13]. The personal digital assistants designed by Wyatt et al [21], contain 8 different sensors to capture human conversations over time.…”

Section: Related Workmentioning

confidence: 99%

Socio-Technical Network Analysis from Wearable Interactions

Farrahi

Emonet

Ferscha

2012

2012 16th International Symposium on Wearable Computers

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Wearable sensing platforms like modern smartphones have proven to be effective means in the complexity and computational social sciences. This paper draws from explicit (phone calls, SMS messaging) and implicit (proximity sensing based on Bluetooth radio signals) interaction patterns collected via smartphones and reality mining techniques to explain the dynamics of personal interactions and relationships.We consider three real human to human interaction networks, namely physical proximity, phone communication and instant messaging. We analyze a real undergraduate community's social circles and consider various topologies, such as the interaction patterns of users with the entire community, and the interaction patterns of users within their own community. We fit distributions of various interactions, for example, showing that the distribution of users that have been in physical proximity but have never communicated by phone fits a gaussian. Finally, we consider five types of relationships, for example friendships, to see whether significant differences exist in their interaction patterns. We find statistically significant differences in the physical proximity patterns of people who are mutual friends and people who are non-mutual (or asymmetric) friends, though this difference does not exist between mutual friends and never friends, nor does it exist in their phone communication patterns.Our findings impact a wide range of data-driven applications in socio-technical systems by providing an overview of community interaction patterns which can be used for applications such as epidemiology, or in understanding the diffusion of opinions and relationships.

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Wearable sensors for pervasive healthcare management

Cited by 72 publications

References 11 publications

Friends don't lie

Friends don't lie

Multi-domain and multi-task prediction of extraversion and leadership from meeting videos

Socio-Technical Network Analysis from Wearable Interactions

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