VibeFones: Socially Aware Mobile Phones

Madan, Anmol; Pentland, Alex

doi:10.1109/iswc.2006.286352

Cited by 27 publications

(34 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Their technique, however, would not be directly applicable to mobile sensor data since it uses body part sensitive human motion features of the wrist and hip as opposed to features directly obtainable by mobile phones that can be worn in pockets, bags, or backpacks. Recently, mobile phones have been modified to capture non-linguistic speech attributes [19], [20]. These non-verbal speech features have been used for sound classification (for example music versus voice) and for the discovery of sound events [19].…”

Section: Related Workmentioning

confidence: 99%

“…These non-verbal speech features have been used for sound classification (for example music versus voice) and for the discovery of sound events [19]. In [20], these features, in addition to others obtained by mobile phone sensors, are used to characterize social interactions such as personal relationships at the workplace or in private. 4 Mobile phone call data has been analyzed at large-scales in [3], [11] to understand human dynamics.…”

Section: Related Workmentioning

confidence: 99%

“…There are some works by Pentland's group [20], [21], using multimodal data, though the critical features for these works are non verbal audio features which would not be readily available from most off-the-shelf mobile devices. In [6], a dynamic proximity network is modeled and analyzed to find the properties of human interaction dynamics.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Probabilistic Mining of Socio-Geographic Routines From Mobile Phone Data

Farrahi

Gática-Pérez

2010

IEEE J. Sel. Top. Signal Process.

View full text Add to dashboard Cite

There is relatively little work on the investigation of large-scale human data in terms of multimodality for human activity discovery. In this paper we suggest that human interaction data, or human proximity, obtained by mobile phone Bluetooth sensor data, can be integrated with human location data, obtained by mobile cell tower connections, to mine meaningful details about human activities from large and noisy datasets. We propose a model, called bag of multimodal behavior, that integrates the modeling of variations of location over multiple time-scales, and the modeling of interaction types from proximity. Our representation is simple yet robust to characterize real-life human behavior sensed from mobile phones, which are devices capable of capturing large-scale data known to be noisy and incomplete. We use an unsupervised approach, based on probabilistic topic models, to discover latent human activities in terms of the joint interaction and location behaviors of 97 individuals over the course of approximately a 10 month period using data from MIT's Reality Mining project. Some of the human activities discovered with our multimodal data representation include "going out from 7pm-midnight alone" and "working from 11am-5pm with 3-5 other people", further finding that this activity dominantly occurs on specific days of the week. Our methodology also finds dominant work patterns occurring on other days of the week. We further demonstrate the feasibility of the topic modeling framework to discover human routines to predict missing multimodal phone data on specific times of the day.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Probabilistic Mining of Socio-Geographic Routines From Mobile Phone Data

Farrahi

Gática-Pérez

2010

IEEE J. Sel. Top. Signal Process.

View full text Add to dashboard Cite

show abstract

“…Eagle and Pentland [11] inferred location and activity with both short-range (Bluetooth) and longrange radio network data (GSM) on mobile phones. Madan et al [29] developed VibeFone, a mobile software application that used location, proximity, and tone of voice to gain understanding of people's social lives by mining their face-to-face and phone interactions. This application augments traditional means of gathering social interaction data (surveys or ethnographic studies).…”

Section: Human Behavior Analysis Using Mobile Sensorsmentioning

confidence: 99%

Privacy-sensitive recognition of group conversational context with sociometers

et al. 2011

Self Cite

View full text Add to dashboard Cite

Recognizing the conversational context in which group interactions unfold has applications in machines that support collaborative work and perform automatic social inference using contextual knowledge. This paper addresses the task of discriminating one conversational context from another, specifically brainstorming from decision-making interactions, using easily computable nonverbal behavioral cues. Privacy-sensitive mobile sociometers are used to record the interaction data. We hypothesize that the difference in the conversational dynamics between brainstorming and decision-making discussions is significant and measurable using speaking activity-based nonverbal cues. We characterize the communication patterns of the entire group by the aggregation (both temporal and person-wise) of their nonverbal behavior. The results on our interaction data set show that the floor-occupation patterns in a brainstorming interaction are different from a decision-making interaction, and our method can obtain a classification accuracy as high as 87.5%.

show abstract

“…Social software describes any type of software that connects users and supports social interaction and sharing data [21,30]. A wide variety of social applications and mobile social computing applications exist; email and instant messaging are simple ways of on-line communications.…”

Section: Social Computing and Privacy Risksmentioning

confidence: 99%