Given the limitations of traditional approaches, wearable artificial intelligence (AI) is one of the technologies that have been exploited to detect or predict depression. The current review aimed at examining the performance of wearable AI in detecting and predicting depression. The search sources in this systematic review were 8 electronic databases. Study selection, data extraction, and risk of bias assessment were carried out by two reviewers independently. The extracted results were synthesized narratively and statistically. Of the 1314 citations retrieved from the databases, 54 studies were included in this review. The pooled mean of the highest accuracy, sensitivity, specificity, and root mean square error (RMSE) was 0.89, 0.87, 0.93, and 4.55, respectively. The pooled mean of lowest accuracy, sensitivity, specificity, and RMSE was 0.70, 0.61, 0.73, and 3.76, respectively. Subgroup analyses revealed that there is a statistically significant difference in the highest accuracy, lowest accuracy, highest sensitivity, highest specificity, and lowest specificity between algorithms, and there is a statistically significant difference in the lowest sensitivity and lowest specificity between wearable devices. Wearable AI is a promising tool for depression detection and prediction although it is in its infancy and not ready for use in clinical practice. Until further research improve its performance, wearable AI should be used in conjunction with other methods for diagnosing and predicting depression. Further studies are needed to examine the performance of wearable AI based on a combination of wearable device data and neuroimaging data and to distinguish patients with depression from those with other diseases. 

/pdf/systematic-review-and-meta-analysis-of-performance-of-2z6t1awd.pdf

Systematic review and meta-analysis of performance of wearable artificial intelligence in detecting and predicting depression

With the increasing proliferation of embedded sensors in wearable devices, there is potential for modeling individual emotional and mental state variations.The popular measure for the quantification of emotions outlines the affective states of arousal and valences, with high and low being the discrete categories of interest.Recent works explore the discernability of digital behavior differences between groups with and without mental disorders.However, the interaction between physiological states and affective states within a predominantly depressive population remains to be studied with the aid of wearables.Despite the pervasiveness of emotional state inference through the tracking of ubiquitous physiological trackers such as heart rate, blood volume pulse, skin conductance, and motion, a dearth of work is noted in the exploration of physiological markers in single and multimodal settings.This work provides an extensive evaluation of a convolutional neural network with an attention mechanism ensembled with a random forest algorithm to effectively leverage multiple raw signal-to-image transformations as feature inputs to predict depression severity and affective state.The proposed models are assessed on the Daily Ambulatory Psychological and Physiological recording for Emotion Research (DAPPER) dataset, and achieve the sensitivity: specificity scores of 58.75%:45.59%,62.34%:43.41%,49.43%:51.70%for predicting depression, valence, and arousal with a mixture of uni-and bi-modality applying Continuous Wavelet Transforms and Short-time Fourier Transform to motion skinconductance readings, respectively.This work is envisioned as a preliminary study to contribute towards the monitoring of affective states among a depressed population by utilizing low-frequency sensor recordings with the DAPPER dataset. 

Evaluating Multimodal Wearable Sensors for Quantifying Affective States and Depression With Neural Networks

Many countries around the world are facing a shortage of healthcare resources, especially during the post-epidemic era, leading to a dramatic increase in the need for self-detection and self-management of diseases. The popularity of smart wearable devices, such as smartwatches, and the development of machine learning (ML) bring new opportunities for the early detection and management of various prevalent diseases, such as cardiovascular diseases, Parkinson’s disease, and diabetes. In this survey, we comprehensively review the articles related to specific diseases or health issues based on small wearable devices and ML. More specifically, we first present an overview of the articles selected and classify them according to their targeted diseases. Then, we summarize their objectives, wearable device and sensor data, ML techniques, and wearing locations. Based on the literature review, we discuss the challenges and propose future directions from the perspectives of privacy concerns, security concerns, transmission latency and reliability, energy consumption, multimodality, multisensor, multidevices, evaluation metrics, explainability, generalization and personalization, social influence, and human factors, aiming to inspire researchers in this field.

Leveraging Machine Learning for Disease Diagnoses based on Wearable Devices: A Survey

Emotion recognition based on a limited number of multimodal physiological signals channels

Oil-water two-phase flow exists widely in the industrial process. At present, it is very difficult to describe the oil-water two-phase flow system theoretically. The mechanism of droplet fragmentation and coalescence, which restricts droplet size and flow stability, is still unclear. Accurate identification of oil-water two-phase flow patterns is of great significance to flow control and measurement. In this study, a refined composite multi-scale rescaled range fractional permutation entropy (MRSFPE) is proposed for identifying the flow pattern of oil-water two-phase flow in vertical pipes. Firstly, the proposed algorithm is simulated numerically to demonstrate its ability to uncover the complexity of nonlinear time series. Then, the conductance sensor signals are collected in the flow loop test facility of vertical upward oil-water two-phase flow. In the experiment, the observed flow patterns, captured by a high-speed camera, include very fine dispersed oil-in-water flow (VFD O/W), dispersed oil-in-water flow (D O/W), dispersed oil-in-water slug flow (D OS/W), and transition flow (TF). Finally, the proposed algorithm is applied to the experimental signal analysis, extracting two scale indices of the entropy rate at high scale (MRSFPE_rate) and the mean value entropy at low scale (MRSFPE_ave). The results show that the four flow patterns can be identified in the joint distribution of the two scale indices. Furthermore, the MRSFPE can be a useful approach to characterize the nonlinear dynamics of multiphase flow based on sensor measurement signals. 

Nonlinear Analysis for Identification of Vertical Upward Oil-Water Two-Phase Flow Patterns

Depression is one of the most common mental health disorders, affecting approximately 280 million people worldwide. This condition is defined as emotional dysregulation resulting in persistent feelings of sadness, loss of interest and inability to experience pleasure. Early detection can facilitate timely intervention in the form of psychological therapy and/or medication. With the widespread public adoption of wearable devices such as smartwatches and fitness trackers, it is becoming increasingly possible to gain insights relating the mental states of individuals in an unobtrusive manner within free-living conditions. This work presents a machine learning (ML) approach that utilizes retrospectively collected data-derived consumer-grade wearables for passive detection of depression severity. The experiments conducted in this work reveal that multimodal analysis of physiological signals in terms of their discrete wavelet transform (DWT) features exhibit considerably better performance than unimodal scenarios. Additionally, we conduct experiments to view the impact of severity on emotional valence-arousal detection. We believe that our work has implications towards guiding development in the domain of multimodal wearable-based screening of mental health disorders and necessitates appropriate treatment interventions.

/pdf/investigating-the-feasibility-of-assessing-depression-196epnli.pdf

Investigating the Feasibility of Assessing Depression Severity and Valence-Arousal with Wearable Sensors Using Discrete Wavelet Transforms and Machine Learning

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Evaluating Multimodal Wearable Sensors for Quantifying Affective States and Depression With Neural Networks

Investigating the Feasibility of Assessing Depression Severity and Valence-Arousal with Wearable Sensors Using Discrete Wavelet Transforms and Machine Learning