StressSense: detecting stress in unconstrained acoustic environments using smartphones

By Hong Lu, Denise Frauendorfer, Mashfiqui Rabbi, M. Schmid Mast, Gokul Chittaranjan, Andrew T. Campbell, Daniel Gatica-Perez, Tanzeem Choudhury. Ubiquitous Computing, 2012

582 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Experimental and Cognitive Psychology

View Paper: https://www.semanticscholar.org/paper/fcf985676da8c176a38c548b193e2124eae87ade

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In Experimental and Cognitive Psychology

Citations in the corpus

Recognizing Detailed Human Context in the Wild from Smartphones and Smartwatches (2016, background): “In many studies the recognition setup involved cumbersome devices that had to be worn in a specific way [5], [12], [13], [14], [15], [17], [22], [23], [25], [28], [29].”
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].”
Lab And/Or Field? Measuring Personality Processes and Their Social Consequences (2015, methodology): “The StressSense app (Lu et al., 2012) monitors ambient sounds for the user’s voice, extracts stress-relevant parameters (e.g. speech rate, pitch variability and jitter) and integrates the parameters into stress estimates based on machine-learning algorithms (calibrated against users’ skin…”
Accelerating Mobile Audio Sensing Algorithms through On-Chip GPU Offloading (2017, methodology): “3081358 digital assistants such as Apple’s Siri [2] or Google Now [5], as well as a plethora of behavior monitoring apps that recognize emotions from voice [52, 46], or perform conversation analysis [45, 58, 48].”
Personal Sensing: Understanding Mental Health Using Ubiquitous Sensors and Machine Learning (2017, background): “StressSense (Lu et al. 2012) is a smartphone sensing system that uses the phone’s built-in microphone to capture human speech during social interactions to infer a user’s / 11 17 .”
Towards an automatic early stress recognition system for office environments based on multimodal measurements: A review (2016, methodology): “[108], Stress 2 14 EDA Job interview and marketing”
Review on Psychological Stress Detection Using Biosignals (2019, background): “Speaker’s pitch (f0) is one of the most studied indicators of emotional stress being typically increased during stress conditions [82-92].”
Monitoring stress with a wrist device using context (2017, background): “One step closer to real life are Ramos et al. [13], Mohino-Herranz et al. [22] and Lu et al. [23], who presented studies in which the subjects are allowed to be active based on a predeﬁned scenario.”
DeepX: A Software Accelerator for Low-Power Deep Learning Inference on Mobile Devices (2016, background): “Re-training is not practical during RLC because the training of deep learning models is extremely resource intensive 1 and therefore is not feasible to perform every time the model performs an inference (in order to optimize its execution relative to available resource).”
Using Smartphones to Collect Behavioral Data in Psychological Science: Opportunities, Practical Considerations, and Challenges (2016, unknown): “What types of smartphone data do I need for my study?—Some mobile-sensing apps vary in the breadth of sensors used, ranging from those that only collect data from one sensor (e.g., StressSense, in which microphone data were used to infer stress levels from features of a participant’s voice; Lu et al”