Research has proven that stress reduces quality of life and causes many diseases. For this reason, several researchers devised stress detection systems based on physiological parameters. However, these systems require that obtrusive sensors are continuously carried by the user. In our paper, we propose an alternative approach providing evidence that daily stress can be reliably recognized based on behavioral metrics, derived from the user's mobile phone activity and from additional indicators, such as the weather conditions (data pertaining to transitory properties of the environment) and the personality traits (data concerning permanent dispositions of individuals). Our multifactorial statistical model, which is person-independent, obtains the accuracy score of 72.28 for a 2-class daily stress recognition problem. The model is efficient to implement for most of multimedia applications due to highly reduced low-dimensional feature space (32d). Moreover, we identify and discuss the indicators which have strong predictive power.
Among the different changes in physiological parameters that happen during stressful situations, variation in speech production has inspired a number of studies using acoustic sensing on smartphones. Research on stress detection based on voice analysis considered different speech characteristics such as pitch, glottal pulse, spectral slope and phonetic variations. For example, Lu and colleagues @cite_4 proposed StressSense, an Android application for stress detection from human voice in real-life conversation, and they achieved 81 .
@cite_4
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Stress can have long term adverse effects on individuals' physical and mental well-being. Changes in the speech production process is one of many physiological changes that happen during stress. Microphones, embedded in mobile phones and carried ubiquitously by people, provide the opportunity to continuously and non-invasively monitor stress in real-life situations. We propose StressSense for unobtrusively recognizing stress from human voice using smartphones. We investigate methods for adapting a one-size-fits-all stress model to individual speakers and scenarios. We demonstrate that the StressSense classifier can robustly identify stress across multiple individuals in diverse acoustic environments: using model adaptation StressSense achieves 81 and 76 accuracy for indoor and outdoor environments, respectively. We show that StressSense can be implemented on commodity Android phones and run in real-time. To the best of our knowledge, StressSense represents the first system to consider voice based stress detection and model adaptation in diverse real-life conversational situations using smartphones. It is argued that reliable acoustic profiles of speech under stress can only be found if different types of stress are clearly distinguished and experimentally induced. We report first results of a study with 100 speakers from three language groups, using a computer-based induction procedure that allows distinguishing cognitive load due to task engagement from psychological stress. Findings show significant effects of load, and partly of stress, for speech rate, energy contour, F0, and spectral parameters. It is further suggested that the mean results for the complete sample of speakers do not reflect the amplitude of stress effects on the voice. Future research should isolate and focus on speakers for whom the psychological stress induction has been successful. We describe initial results from an ongoing project to use mobile phone sensors to detect stress related situations1. The questions that we address in this stage of our work in progress is whether differences between stressful and non stressful periods can be detected in information readily available on a smartphone such as location traces, BlueTooth devices seen during the day and phone call patterns. We present an experiment with 7 students who were monitored during a two week exam session (stressful situation) and the two following weeks (non stressful period). The results show that a behaviour modification can clearly be seen, although the exact interpretation and generalization requires further work and larger scale experiments. Major depressive disorder (MDD) is known to result in neurophysiological and neurocognitive changes that affect control of motor, linguistic, and cognitive functions. MDD's impact on these processes is reflected in an individual's communication via coupled mechanisms: vocal articulation, facial gesturing and choice of content to convey in a dialogue. In particular, MDD-induced neurophysiological changes are associated with a decline in dynamics and coordination of speech and facial motor control, while neurocognitive changes influence dialogue semantics. In this paper, biomarkers are derived from all of these modalities, drawing first from previously developed neurophysiologically-motivated speech and facial coordination and timing features. In addition, a novel indicator of lower vocal tract constriction in articulation is incorporated that relates to vocal projection. Semantic features are analyzed for subject avatar dialogue content using a sparse coded lexical embedding space, and for contextual clues related to the subject's present or past depression status. The features and depression classification system were developed for the 6th International Audio Video Emotion Challenge (AVEC), which provides data consisting of audio, video-based facial action units, and transcribed text of individuals communicating with the human-controlled avatar. A clinical Patient Health Questionnaire (PHQ) score and binary depression decision are provided for each participant. PHQ predictions were obtained by fusing outputs from a Gaussian staircase regressor for each feature set, with results on the development set of mean F1=0.81, RMSE=5.31, and MAE=3.34. These compare favorably to the challenge baseline development results of mean F1=0.73, RMSE=6.62, and MAE=5.52. On test set evaluation, our system obtained a mean F1=0.70, which is similar to the challenge baseline test result. Future work calls for consideration of joint feature analyses across modalities in an effort to detect neurological disorders based on the interplay of motor, linguistic, affective, and cognitive components of communication.