Analyzing the contribution of different passively collected data to predict Stress and Depression

• URL: http://arxiv.org/abs/2310.13607v1
• Date: Fri, 20 Oct 2023 15:57:22 GMT
• Title: Analyzing the contribution of different passively collected data to predict Stress and Depression
• Authors: Irene Bonafonte, Cristina Bustos, Abraham Larrazolo, Gilberto Lorenzo Martinez Luna, Adolfo Guzman Arenas, Xavier Baro, Isaac Tourgeman, Mercedes Balcells and Agata Lapedriza
• Abstract summary: We analyze the contribution of different passively collected sensor data types to predict daily selfreport stress and PHQ-9 depression score. Our results show that WiFi features (which encode mobility patterns) and Phone Log features (which encode information correlated with sleep patterns) provide significative information for stress and depression prediction.
• Score: 1.7638111859993633
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The possibility of recognizing diverse aspects of human behavior and environmental context from passively captured data motivates its use for mental health assessment. In this paper, we analyze the contribution of different passively collected sensor data types (WiFi, GPS, Social interaction, Phone Log, Physical Activity, Audio, and Academic features) to predict daily selfreport stress and PHQ-9 depression score. First, we compute 125 mid-level features from the original raw data. These 125 features include groups of features from the different sensor data types. Then, we evaluate the contribution of each feature type by comparing the performance of Neural Network models trained with all features against Neural Network models trained with specific feature groups. Our results show that WiFi features (which encode mobility patterns) and Phone Log features (which encode information correlated with sleep patterns), provide significative information for stress and depression prediction.

Related papers

• Integrating Wearable Sensor Data and Self-reported Diaries for Personalized Affect Forecasting [2.36325543943271]
  We propose a multimodal deep learning model for affect status forecasting. This model combines a transformer encoder with a pre-trained language model, facilitating the integrated analysis of objective metrics and self-reported diaries. Our results demonstrate that the proposed model achieves predictive accuracy of 82.50% for positive affect and 82.76% for negative affect, a full week in advance.
  arXiv  Detail & Related papers  (2024-03-16T17:24:38Z)
• A Novel Loss Function Utilizing Wasserstein Distance to Reduce Subject-Dependent Noise for Generalizable Models in Affective Computing [0.4818210066519976]
  Emotions are an essential part of human behavior that can impact thinking, decision-making, and communication skills. The ability to accurately monitor and identify emotions can be useful in many human-centered applications such as behavioral training, tracking emotional well-being, and development of human-computer interfaces.
  arXiv  Detail & Related papers  (2023-08-17T01:15:26Z)
• Exploiting the Brain's Network Structure for Automatic Identification of ADHD Subjects [70.37277191524755]
  We show that the brain can be modeled as a functional network, and certain properties of the networks differ in ADHD subjects from control subjects. We train our classifier with 776 subjects and test on 171 subjects provided by The Neuro Bureau for the ADHD-200 challenge.
  arXiv  Detail & Related papers  (2023-06-15T16:22:57Z)
• Dataset Bias in Human Activity Recognition [57.91018542715725]
  This contribution statistically curates the training data to assess to what degree the physical characteristics of humans influence HAR performance. We evaluate the performance of a state-of-the-art convolutional neural network on two HAR datasets that vary in the sensors, activities, and recording for time-series HAR.
  arXiv  Detail & Related papers  (2023-01-19T12:33:50Z)
• A Theoretical Analysis on Feature Learning in Neural Networks: Emergence from Inputs and Advantage over Fixed Features [18.321479102352875]
  An important characteristic of neural networks is their ability to learn representations of the input data with effective features for prediction. We consider learning problems motivated by practical data, where the labels are determined by a set of class relevant patterns and the inputs are generated from these. We prove that neural networks trained by gradient descent can succeed on these problems.
  arXiv  Detail & Related papers  (2022-06-03T17:49:38Z)
• An Empirical Investigation of Commonsense Self-Supervision with Knowledge Graphs [67.23285413610243]
  Self-supervision based on the information extracted from large knowledge graphs has been shown to improve the generalization of language models. We study the effect of knowledge sampling strategies and sizes that can be used to generate synthetic data for adapting language models.
  arXiv  Detail & Related papers  (2022-05-21T19:49:04Z)
• Analyzing the Effects of Handling Data Imbalance on Learned Features from Medical Images by Looking Into the Models [50.537859423741644]
  Training a model on an imbalanced dataset can introduce unique challenges to the learning problem. We look deeper into the internal units of neural networks to observe how handling data imbalance affects the learned features.
  arXiv  Detail & Related papers  (2022-04-04T09:38:38Z)
• The world seems different in a social context: a neural network analysis of human experimental data [57.729312306803955]
  We show that it is possible to replicate human behavioral data in both individual and social task settings by modifying the precision of prior and sensory signals. An analysis of the neural activation traces of the trained networks provides evidence that information is coded in fundamentally different ways in the network in the individual and in the social conditions.
  arXiv  Detail & Related papers  (2022-03-03T17:19:12Z)
• EEGminer: Discovering Interpretable Features of Brain Activity with Learnable Filters [72.19032452642728]
  We propose a novel differentiable EEG decoding pipeline consisting of learnable filters and a pre-determined feature extraction module. We demonstrate the utility of our model towards emotion recognition from EEG signals on the SEED dataset and on a new EEG dataset of unprecedented size. The discovered features align with previous neuroscience studies and offer new insights, such as marked differences in the functional connectivity profile between left and right temporal areas during music listening.
  arXiv  Detail & Related papers  (2021-10-19T14:22:04Z)
• Jointly Predicting Job Performance, Personality, Cognitive Ability, Affect, and Well-Being [42.67003631848889]
  We create a benchmark for predictive analysis of individuals from a perspective that integrates physical and physiological behavior, psychological states and traits, and job performance. We design data mining techniques as benchmark and uses real noisy and incomplete data derived from wearable sensors to predict 19 constructs based on 12 standardized well-validated tests.
  arXiv  Detail & Related papers  (2020-06-10T14:30:29Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.