Recognizing academic performance, sleep quality, stress level, and mental health using personality traits, wearable sensors and mobile phones

Abstract

What can wearable sensors and usage of smart phones tell us about academic performance, self-reported sleep quality, stress and mental health condition? To answer this question, we collected extensive subjective and objective data using mobile phones, surveys, and wearable sensors worn day and night from 66 participants, for 30 days each, totaling 1,980 days of data. We analyzed daily and monthly behavioral and physiological patterns and identified factors that affect academic performance (GPA), Pittsburg Sleep Quality Index (PSQI) score, perceived stress scale (PSS), and mental health composite score (MCS) from SF-12, using these month-long data. We also examined how accurately the collected data classified the participants into groups of high/low GPA, good/poor sleep quality, high/low self-reported stress, high/low MCS using feature selection and machine learning techniques. We found associations among PSQI, PSS, MCS, and GPA and personality types. Classification accuracies using the objective data from wearable sensors and mobile phones ranged from 67–92%.

The Neurobiology of Dispositional Negativity and Attentional Biases to Threat: Implications for Understanding Anxiety Disorders in Adults and Youth

“…J. Wilson, Smyth, & MacLean, 2014). The development of robust mobile eye trackers (e.g., Applied Science Laboratories’ Mobile Eye system), the emergence of commercial software for automated facial analytics (e.g., from Affectiva, Emotient, and Noldus; Olderbak, Hildebrandt, Pinkpank, Sommer, & Wilhelm, 2014), and the widespread dissemination of smart phone technology afford additional opportunities for objectively and unobtrusively quantifying social attention, context, and daily behavior (Gosling & Mason, 2015; Sano et al, 2015; Wrzus & Mehl, 2015). Combining these measures with laboratory assays of brain function would open the door to discovering the neural systems underlying maladaptive experiences and pathology-promoting behaviors (e.g., social withdrawal, avoidance, and hyper-vigilance) in the real world, close to clinical end-point (Price et al, 2016).…”

Predicting the Sixteen Personality Factors (16PF) of an individual by analyzing facial features

“…It could be useful in applications for career development and counseling in the human resources or academic areas [30,31], adaptive e-learning systems [32], diagnosis of mental health disorders (borderline personality disorder [33], depression [3], schizophrenia [34], eating disorder [35] or sleep disorders [36]), virtual psychologist applications [37], and personalized health assistance [38]. It was also shown that there are links between common physical diseases (such as heart attacks, diabetes, cancer, strokes, arthritis, hypertension, and respiratory disease) and Big Five personality traits [39] such that these diseases influence the age-related personality accelerating with 2.5 years decrease for extraversion, 5 years decrease for conscientiousness, 1.6 years decrease for openness, and 1.9 years increase for emotional stability.…”

Machine learning in mental health: A systematic scoping review of methods and applications

“…A novel approach identified for detection of conditions is the use of unstructured text, including detection of suicide ideation from counselling transcripts [10], detection of schizophrenia from written texts [11], and analysis of social media data to detect depressive symptoms [12]. ML has also been applied to wearable sensor data to assess general wellbeing [13], and to ambient, in-home sensors to detect psychiatric emergencies [14]. Finally, speech data has been used with ML to detect underlying mental states indicative of schizophrenia and depression [15], to assess the effects of drugs on mental state [16], and to classify at-risk patients of Alzheimer's disease based on speech patterns [17].…”