EmotionSense: a mobile phones based adaptive platform for experimental social psychology research

By Kiran K. Rachuri, Mirco Musolesi, Cecilia Mascolo, Peter J. Rentfrow, Chris Longworth, Andrius Aucinas. Ubiquitous Computing, 2010

536 citations recorded by the source corpus.

Subfields: Applied Psychology, Computer Vision and Pattern Recognition, Information Systems

View Paper: https://www.semanticscholar.org/paper/80f6139c1de8fbf819afa29764a6c39a208fcf41

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In Applied Psychology

In Computer Vision and Pattern Recognition

In Information Systems

Citations in the corpus

Mobile Phone Sensing Systems: A Survey (2013, background): “The authors wish to acknowledge the anonymous reviewers for their valuable comments and special thanks to Jun Zhang & Chao Chen (Deakin University) for helping to prepare this manuscripts.”
Personal Sensing: Understanding Mental Health Using Ubiquitous Sensors and Machine Learning (2017, background): “Another example, Emotion Sense (Rachuri et al. 2010) proposed that audio-based emotion recognition could identify up to 14 different emotions clustered into ﬁve broader emotion groups a t e n i v the self-reports of the participants.”
Trajectories of depression: unobtrusive monitoring of depressive states by means of smartphone mobility traces analysis (2015, methodology): “We exploit the asynchronous delay-tolerant data transfer strategy of the ES Data Manager Library [32, 21] to transmit the collected data in an energy-efﬁcient way and to avoid any cost on participating users.”
Participatory Air Pollution Monitoring Using Smartphones (2012, background): “Mobile phones are used in a wide range of application scenarios to facilitate data collection, such as visibility monitoring [22], traffic conditions surveillance [23], sensing individual emotions [24], and bicycle localization [19].”
A Survey on Mobile Crowdsensing Systems: Challenges, Solutions, and Opportunities (2019, methodology): “Some works require users to report their impact about environmental [119] or emotional situations [147].”
MoodScope: building a mood sensor from smartphone usage patterns (2013, unknown): “For example, EmotionSense [23] proposes a complete end-to-end smartphone platform by performing audio-based emotion recognition.”
Exploring Automatic Diagnosis of COVID-19 from Crowdsourced Respiratory Sound Data (2020, methodology): “In Emotionsense [26], the phone microphone is used as a sensor for users’ emotion detection in-thewild through Gaussian mixture models.”
Accelerating Mobile Audio Sensing Algorithms through On-Chip GPU Offloading (2017, methodology): “GMM emotion recognition [52], speaker identification [52, 45], ambient sound classification [47], stress detection [46]”
DEEProtect: Enabling Inference-based Access Control on Mobile Sensing Applications (2017, background): “While shared data has enabled context-aware apps to provide utility to the users, the same data can also be used by an adversary to make inferences that are possibly sensitive to the user such as speaker identity [52], [66], keystroke detection [91], [54], [13], [59], [63], location tracking [12], [”
StudentLife: assessing mental health, academic performance and behavioral trends of college students using smartphones (2014, background): “There is a growing interest in using smartphone sensing [31, 9, 12, 11, 40, 8, 15] to infer human dynamics and behavioral health [7, 34, 25, 21, 18, 23, 35, 28, 30].”
Opportunities and Challenges for Smartphone Applications in Supporting Health Behavior Change: Qualitative Study (2013, background): “Encouragingly, these features also map onto key behavior change techniques that behavioral science research has established as effective for supporting behavior change, namely self-monitoring, goal-setting, and receiving feedback on performance [10,11].”
Using Smartphones to Collect Behavioral Data in Psychological Science: Opportunities, Practical Considerations, and Challenges (2016, unknown): “Smartphone sensing methods are poised to address this gap in research by allowing researchers to collect records of naturalistic behavior relatively objectively and unobtrusively (Boase, 2013; Rachuri et al., 2010; Wrzus & Mehl, 2015).”
DeepEar: robust smartphone audio sensing in unconstrained acoustic environments using deep learning (2015, methodology): “Through the microphone it is possible to infer, for example: daily activities (e.g., eating [9], coughing [49], driving [58]), internal user states (e.g., stress [55], emotion [64]) and ambient conditions (e.g., number of nearby people [78]).”