Wearable Long-Term Social Sensing for Mental Wellbeing

Long, Jiang; Gao, Bin; Gu, Juping; Chen, Yuanpeng; Gao, Zhao; Ma, Xiaole; Kendrick, Keith M.; Woo, Wai Lok

doi:10.1109/jsen.2018.2877427

Cited by 22 publications

(14 citation statements)

References 56 publications

(48 reference statements)

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“…It is difficult to assess an individual's psychological state objectively through the general questionnaire-based method. Recent studies suggested long-term mental health monitoring using the wearable devices [30,31]. It is expected that the developed algorithm will be improved by objective and continuous feedback from long-term monitoring methods in future studies.…”

Section: Discussionmentioning

confidence: 99%

A User-Specific Approach for Comfortable Application of Advanced 3D CAD/CAM Technique in Dental Environments Using the Harmonic Series Noise Model

2019

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Recently, there has been a focus on improving the user’s emotional state by providing high-quality sound beyond noise reduction against industrial product noise. Three-dimensional computer aided design and computer-aided manufacturing (3D CAD/CAM) dental milling machines are a major source of industrial product noise in the dental environment. Here, we propose a noise-control method to improve the sound quality in the dental environment. Our main goals are to analyze the acoustic characteristics of the sounds generated from the dental milling machine, to control the noise by active noise control, and to improve the sound quality of the residual noise by synthesized new sound. In our previous study, we demonstrated noise reduction in dental milling machines through tactile transducers. To improve the sound quality on residual noise, we performed frequency analysis, and synthesized sound similarly as musical instruments, using the harmonic series noise model. Our data suggest that noise improvement through synthesis may prove to be a useful tool in the development of dental devices.

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Section: Discussionmentioning

confidence: 99%

A User-Specific Approach for Comfortable Application of Advanced 3D CAD/CAM Technique in Dental Environments Using the Harmonic Series Noise Model

2019

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“…There are currently several studies that attempt to identify stress patterns of voice using neural networks and machine learning [ 57 , 58 , 59 ]. Some even combine speech signals with electrodermal activity [ 60 ] or use wearable devices with multi-sensors combining audio and physiological sensors together with deep neural learning networks to monitoring an individual’s well-being in a naturalistic environment [ 42 , 61 , 62 ].…”

Section: Human Stress Phenomenamentioning

confidence: 99%

The Concept of Advanced Multi-Sensor Monitoring of Human Stress

Vavrinský

Stopjaková

Kopáni

et al. 2021

Sensors

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Many people live under stressful conditions which has an adverse effect on their health. Human stress, especially long-term one, can lead to a serious illness. Therefore, monitoring of human stress influence can be very useful. We can monitor stress in strictly controlled laboratory conditions, but it is time-consuming and does not capture reactions, on everyday stressors or in natural environment using wearable sensors, but with limited accuracy. Therefore, we began to analyze the current state of promising wearable stress-meters and the latest advances in the record of related physiological variables. Based on these results, we present the concept of an accurate, reliable and easier to use telemedicine device for long-term monitoring of people in a real life. In our concept, we ratify with two synchronized devices, one on the finger and the second on the chest. The results will be obtained from several physiological variables including electrodermal activity, heart rate and respiration, body temperature, blood pressure and others. All these variables will be measured using a coherent multi-sensors device. Our goal is to show possibilities and trends towards the production of new telemedicine equipment and thus, opening the door to a widespread application of human stress-meters.

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“…The commonly acknowledged applications of these wearable devices include mental health assessment [34] and sleep monitoring [35]. There exist substantial research considering technological advancements in development of frameworks for long term mental health monitoring using wearable devices [36,37]. Many wearables have been proposed and developed in the recent past to assess sleep with inbuilt sensors.…”

Section: Related Workmentioning

confidence: 99%

Analysis of Data from Wearable Sensors for Sleep Quality Estimation and Prediction Using Deep Learning

2020

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Wearable devices such as smartwatches, wristbands, GPS shoes are increasingly used for fitness and wellness as they allow users to monitor their daily health. These devices have sensors for accumulating user activity data. Clinical actigraph devices fall in the category of wearable devices worn on the wrist determined to estimate sleep parameters by recording movements during sleep. This study aims to predict sleep quality from wearable sensors using deep learning techniques. Three sleep indicators are proposed which are calculated using the data collected automatically from wearable devices. These sleep indicators are Daily Sleep Quality, Weekly Sleep Quality, and Sleep Consistency. Two deep learning models namely Convolution Neural Network (CNN) and Multilayer Perceptron (MLP) have been implemented to predict sleep quality on the basis of the proposed indicators. Two datasets have been used to validate the work proposed in this study which include a dataset comprising sleep parameters using commercial wearable devices and another dataset consisting of sleep data using clinical actigraph device. Systematic Minority Oversampling Technique has been applied for data augmentation with the intent to increase data instances and to alleviate class imbalance. CNN is observed to outperform MLP in predicting sleep quality with the highest accuracy of 97.30%. This study also evaluates the worth of each sleep attribute using Information Gain algorithm in order to identify the most important attributes which contribute to the sleep quality. It has been concluded that in bed awake percentage contributes maximum to the Daily Sleep Quality, average sleep efficiency contributes maximum to the Weekly Sleep Quality and standard deviation of midpoint of in bed and out of bed times contributes maximum to the Sleep Consistency.

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Wearable Long-Term Social Sensing for Mental Wellbeing

Cited by 22 publications

References 56 publications

A User-Specific Approach for Comfortable Application of Advanced 3D CAD/CAM Technique in Dental Environments Using the Harmonic Series Noise Model

A User-Specific Approach for Comfortable Application of Advanced 3D CAD/CAM Technique in Dental Environments Using the Harmonic Series Noise Model

The Concept of Advanced Multi-Sensor Monitoring of Human Stress

Analysis of Data from Wearable Sensors for Sleep Quality Estimation and Prediction Using Deep Learning

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