The identification of peaks in physiological signals
Computers and Biomedical Research
Sensing and modeling human networks
Sensing and modeling human networks
Prototyping and sampling experience to evaluate ubiquitous computing privacy in the real world
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Detection of apneic events from single channel nasal airflow using 2nd derivative method
Computer Methods and Programs in Biomedicine
Social signal processing: state-of-the-art and future perspectives of an emerging domain
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Predicting shoppers' interest from social interactions using sociometric sensors
CHI '09 Extended Abstracts on Human Factors in Computing Systems
Proceedings of the 7th international conference on Mobile systems, applications, and services
SoundSense: scalable sound sensing for people-centric applications on mobile phones
Proceedings of the 7th international conference on Mobile systems, applications, and services
Exploring Privacy Concerns about Personal Sensing
Pervasive '09 Proceedings of the 7th International Conference on Pervasive Computing
Social sensing for epidemiological behavior change
Proceedings of the 12th ACM international conference on Ubiquitous computing
The Jigsaw continuous sensing engine for mobile phone applications
Proceedings of the 8th ACM Conference on Embedded Networked Sensor Systems
ACM Transactions on Intelligent Systems and Technology (TIST)
Social sensing: obesity, unhealthy eating and exercise in face-to-face networks
WH '10 Wireless Health 2010
Automated detection of sensor detachments for physiological sensing in the wild
WH '10 Wireless Health 2010
SpeakerSense: energy efficient unobtrusive speaker identification on mobile phones
Pervasive'11 Proceedings of the 9th international conference on Pervasive computing
Proceedings of the 9th ACM Conference on Embedded Networked Sensor Systems
Exploring micro-incentive strategies for participant compensation in high-burden studies
Proceedings of the 13th international conference on Ubiquitous computing
Proceedings of the 9th ACM Conference on Embedded Networked Sensor Systems
mPuff: automated detection of cigarette smoking puffs from respiration measurements
Proceedings of the 11th international conference on Information Processing in Sensor Networks
Proceedings of the Third International Workshop on Sensing Applications on Mobile Phones
ipShield: a framework for enforcing context-aware privacy
NSDI'14 Proceedings of the 11th USENIX Conference on Networked Systems Design and Implementation
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Automated detection of social interactions in the natural environment has resulted in promising advances in organizational behavior, consumer behavior, and behavioral health. Progress, however, has been limited since the primary means of assessing social interactions today (i.e., audio recording) has several issues in field usage such as microphone occlusion, lack of speaker specificity, and high energy drain, in addition to significant privacy concerns. In this paper, we present mConverse, a new mobile-based system to infer conversation episodes from respiration measurements collected in the field from an unobtrusively wearable respiratory inductive plethysmograph (RIP) band worn around the user's chest. The measurements are wire-lessly transmitted to a mobile phone, where they are used in a novel machine learning model to determine whether the wearer is speaking, listening, or quiet. Our model incorporates several innovations to address issues that naturally arise in the noisy field environment such as confounding events, poor data quality due to sensor loosening and detachment, losses in the wireless channel, etc. Our basic model obtains 83% accuracy for the three class classification. We formulate a Hidden Markov Model to further improve the accuracy to 87%. Finally, we apply our model to data collected from 22 subjects who wore the sensor for 2 full days in the field to observe conversation behavior in daily life and find that people spend 25% of their day in conversations.