Detecting Subclinical Social Anxiety Using Physiological Data from a Wrist-worn Wearable: A Small-Scale Feasibility Study (Preprint)

2021 ◽  
Author(s):  
Ruksana Shaukat-Jali ◽  
Nejra van Zalk ◽  
David Edward Boyle
Keyword(s):  
Young Adults ◽  
Social Anxiety ◽  
Skin Temperature ◽  
Wearable Sensors ◽  
Supervised Machine Learning ◽  
Physiological Data ◽  
Support Vector ◽  
Socially Anxious ◽  
Anxiety States ◽  
Reactive Anxiety

BACKGROUND Subclinical (i.e., threshold) social anxiety can greatly affect young people’s lives, but existing solutions appear inadequate considering its rising prevalence. Wearable sensors may provide a novel way to detect social anxiety and result in new opportunities for monitoring and treatment that would be greatly beneficial for sufferers, society and healthcare services. Nevertheless, indicators such as skin temperature from wrist-worn sensors have not been used in prior work on physiological social anxiety detection. OBJECTIVE This study aimed to investigate whether subclinical social anxiety in young adults can be detected using physiological data obtained from wearable sensors, including Heart Rate (HR), Skin Temperature (ST) and Electrodermal Activity (EDA). METHODS Young adults (N = 12) with self-reported subclinical social anxiety (measured by the widely used self-reported version of the Liebowitz Social Anxiety Scale, LSAS-SR) participated in an impromptu speech task. Physiological data was collected using an E4 Empatica wearable device. Using the pre-processed data and following a supervised machine learning approach, various classification algorithms such as Support Vector Machine (SVM), Decision Tree, Random Forest and K-Nearest Neighbours (KNN) were used to develop models for three different contexts. Models were trained to (1) classify between baseline and socially anxious states, (2) differentiate between baseline, anticipation anxiety and reactive anxiety states, and (3) classify between social anxiety experienced by individuals with differing social anxiety severity. The predictive capability of the singular modalities was also explored in each of the three supervised learning experiments. The generalisability of the developed models was evaluated using 10-fold cross validation as a performance index. RESULTS With modalities combined, the developed models yielded accuracies between 97.54% and 99.48% when detecting between baseline and socially anxious states. Models trained to differentiate between baseline, anticipation anxiety and reactive anxiety states yielded accuracies between 95.18% and 98.10%. Alongside this, the models developed to detect between social anxiety experienced by individuals with differing anxiety severity scores successfully classified with accuracies between 98.86% and 99.52%. Surprisingly, EDA was identified as the most effective singular modality when differentiating between baseline and social anxiety states, whereas ST was the most effective modality when differentiating between anxiety experienced by individuals with differing social anxiety severity. CONCLUSIONS The results indicate that it is possible to accurately detect social anxiety as well as distinguish between levels of severity in young adults by leveraging physiological data collected from wearable sensors.

Detecting Subclinical Social Anxiety Using Physiological Data from a Wrist-worn Wearable: A Small-Scale Feasibility Study (Preprint)

2020 ◽  
Author(s):  
Ruksana Shaukat Jali ◽  
Nejra Van Zalk ◽  
David Boyle
Keyword(s):  
Young Adults ◽  
Social Anxiety ◽  
Skin Temperature ◽  
Wearable Sensors ◽  
Supervised Machine Learning ◽  
Physiological Data ◽  
Support Vector ◽  
Socially Anxious ◽  
Anxiety States ◽  
Reactive Anxiety

BACKGROUND Subclinical (i.e., threshold) social anxiety can greatly affect young people’s lives, but existing solutions appear inadequate considering its rising prevalence. Wearable sensors may provide a novel way to detect social anxiety and result in new opportunities for monitoring and treatment that would be greatly beneficial for sufferers, society and healthcare services. Nevertheless, indicators such as skin temperature from wrist-worn sensors have not been used in prior work on physiological social anxiety detection. OBJECTIVE This study aimed to investigate whether subclinical social anxiety in young adults can be detected using physiological data obtained from wearable sensors, including Heart Rate (HR), Skin Temperature (ST) and Electrodermal Activity (EDA). METHODS Young adults (N = 12) with self-reported subclinical social anxiety (measured by the widely used self-reported version of the Liebowitz Social Anxiety Scale, LSAS-SR) participated in an impromptu speech task. Physiological data was collected using an E4 Empatica wearable device. Using the pre-processed data and following a supervised machine learning approach, various classification algorithms such as Support Vector Machine (SVM), Decision Tree, Random Forest and K-Nearest Neighbours (KNN) were used to develop models for three different contexts. Models were trained to (1) classify between baseline and socially anxious states, (2) differentiate between baseline, anticipation anxiety and reactive anxiety states, and (3) classify between social anxiety experienced by individuals with differing social anxiety severity. The predictive capability of the singular modalities was also explored in each of the three supervised learning experiments. The generalisability of the developed models was evaluated using 10-fold cross validation as a performance index. RESULTS With modalities combined, the developed models yielded accuracies between 97.54% and 99.48% when detecting between baseline and socially anxious states. Models trained to differentiate between baseline, anticipation anxiety and reactive anxiety states yielded accuracies between 95.18% and 98.10%. Alongside this, the models developed to detect between social anxiety experienced by individuals with differing anxiety severity scores successfully classified with accuracies between 98.86% and 99.52%. Surprisingly, EDA was identified as the most effective singular modality when differentiating between baseline and social anxiety states, whereas ST was the most effective modality when differentiating between anxiety experienced by individuals with differing social anxiety severity. CONCLUSIONS The results indicate that it is possible to accurately detect social anxiety as well as distinguish between levels of severity in young adults by leveraging physiological data collected from wearable sensors.

scholarly journals Detecting Subclinical Social Anxiety Using Physiological Data From a Wrist-Worn Wearable: Small-Scale Feasibility Study

10.2196/32656 ◽  
2021 ◽  
Vol 5 (10) ◽  
pp. e32656
Author(s):  
Ruksana Shaukat-Jali ◽  
Nejra van Zalk ◽  
David Edward Boyle
Keyword(s):  
Young Adults ◽  
Social Anxiety ◽  
Skin Temperature ◽  
Wearable Sensors ◽  
Supervised Machine Learning ◽  
Physiological Data ◽  
Support Vector ◽  
Socially Anxious ◽  
Anxiety States ◽  
Reactive Anxiety

Background Subclinical (ie, threshold) social anxiety can greatly affect young people’s lives, but existing solutions appear inadequate considering its rising prevalence. Wearable sensors may provide a novel way to detect social anxiety and result in new opportunities for monitoring and treatment, which would be greatly beneficial for persons with social anxiety, society, and health care services. Nevertheless, indicators such as skin temperature measured by wrist-worn sensors have not been used in prior work on physiological social anxiety detection. Objective This study aimed to investigate whether subclinical social anxiety in young adults can be detected using physiological data obtained from wearable sensors, including heart rate, skin temperature, and electrodermal activity (EDA). Methods Young adults (N=12) with self-reported subclinical social anxiety (measured using the widely used self-reported version of the Liebowitz Social Anxiety Scale) participated in an impromptu speech task. Physiological data were collected using an E4 Empatica wearable device. Using the preprocessed data and following a supervised machine learning approach, various classification algorithms such as Support Vector Machine, Decision Tree, Random Forest, and K-Nearest Neighbours (KNN) were used to develop models for 3 different contexts. Models were trained to differentiate (1) between baseline and socially anxious states, (2) among baseline, anticipation anxiety, and reactive anxiety states, and (3) social anxiety among individuals with social anxiety of differing severity. The predictive capability of the singular modalities was also explored in each of the 3 supervised learning experiments. The generalizability of the developed models was evaluated using 10-fold cross-validation as a performance index. Results With modalities combined, the developed models yielded accuracies between 97.54% and 99.48% when differentiating between baseline and socially anxious states. Models trained to differentiate among baseline, anticipation anxiety, and reactive anxiety states yielded accuracies between 95.18% and 98.10%. Furthermore, the models developed to differentiate between social anxiety experienced by individuals with anxiety of differing severity scores successfully classified with accuracies between 98.86% and 99.52%. Surprisingly, EDA was identified as the most effective singular modality when differentiating between baseline and social anxiety states, whereas ST was the most effective modality when differentiating anxiety among individuals with social anxiety of differing severity. Conclusions The results indicate that it is possible to accurately detect social anxiety as well as distinguish between levels of severity in young adults by leveraging physiological data collected from wearable sensors.

scholarly journals Measuring Biomechanical Risk in Lifting Load Tasks Through Wearable System and Machine-Learning Approach

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1557 ◽  
Author(s):  
Ilaria Conforti ◽  
Ilaria Mileti ◽  
Zaccaria Del Prete ◽  
Eduardo Palermo
Keyword(s):  
Machine Learning ◽  
Material Handling ◽  
Learning Algorithm ◽  
Occupational Injuries ◽  
Wearable Sensors ◽  
High Stress ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Kinematic Parameters

Ergonomics evaluation through measurements of biomechanical parameters in real time has a great potential in reducing non-fatal occupational injuries, such as work-related musculoskeletal disorders. Assuming a correct posture guarantees the avoidance of high stress on the back and on the lower extremities, while an incorrect posture increases spinal stress. Here, we propose a solution for the recognition of postural patterns through wearable sensors and machine-learning algorithms fed with kinematic data. Twenty-six healthy subjects equipped with eight wireless inertial measurement units (IMUs) performed manual material handling tasks, such as lifting and releasing small loads, with two postural patterns: correctly and incorrectly. Measurements of kinematic parameters, such as the range of motion of lower limb and lumbosacral joints, along with the displacement of the trunk with respect to the pelvis, were estimated from IMU measurements through a biomechanical model. Statistical differences were found for all kinematic parameters between the correct and the incorrect postures (p < 0.01). Moreover, with the weight increase of load in the lifting task, changes in hip and trunk kinematics were observed (p < 0.01). To automatically identify the two postures, a supervised machine-learning algorithm, a support vector machine, was trained, and an accuracy of 99.4% (specificity of 100%) was reached by using the measurements of all kinematic parameters as features. Meanwhile, an accuracy of 76.9% (specificity of 76.9%) was reached by using the measurements of kinematic parameters related to the trunk body segment.

scholarly journals Wearable Internet-of-Things platform for human activity recognition and health care

2020 ◽  
Vol 16 (6) ◽  
pp. 155014772091156 ◽  
Author(s):  
Asif Iqbal ◽  
Farman Ullah ◽  
Hafeez Anwar ◽  
Ata Ur Rehman ◽  
Kiran Shah ◽  
...  
Keyword(s):  
Physical Activity ◽  
Machine Learning ◽  
Internet Of Things ◽  
Activity Recognition ◽  
Wearable Sensors ◽  
Recognition Rate ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Data Set

We propose to perform wearable sensors-based human physical activity recognition. This is further extended to an Internet-of-Things (IoT) platform which is based on a web-based application that integrates wearable sensors, smartphones, and activity recognition. To this end, a smartphone collects the data from wearable sensors and sends it to the server for processing and recognition of the physical activity. We collect a novel data set of 13 physical activities performed both indoor and outdoor. The participants are from both the genders where their number per activity varies. During these activities, the wearable sensors measure various body parameters via accelerometers, gyroscope, magnetometers, pressure, and temperature. These measurements and their statistical are then represented in features vectors that used to train and test supervised machine learning algorithms (classifiers) for activity recognition. On the given data set, we evaluate a number of widely known classifiers such random forests, support vector machine, and many others using the WEKA machine learning suite. Using the default settings of these classifiers in WEKA, we attain the highest overall classification accuracy of 90%. Consequently, such a recognition rate is encouraging, reliable, and effective to be used in the proposed platform.

scholarly journals Longitudinal Classification of Mental Effort Using Electrodermal Activity, Heart Rate, and Skin Temperature Data from a Wearable Sensor

2021 ◽  
Author(s):  
William Romine ◽  
Noah Schroeder ◽  
Anjali Edwards ◽  
Tanvi Banerjee
Keyword(s):  
Heart Rate ◽  
Skin Temperature ◽  
Electrodermal Activity ◽  
Wearable Sensors ◽  
Mental Effort ◽  
Physiological Data ◽  
Cognitive State ◽  
Switching Regression ◽  
Longitudinal Case Study ◽  
Time Duration

Recent studies show that physiological data can detect changes in mental effort, making way for the development of wearable sensors to monitor mental effort in school, work, and at home. We have yet to explore how such a device would work with a single participant over an extended time duration. We used a longitudinal case study design with ~38 hours of data to explore the efficacy of electrodermal activity, skin temperature, and heart rate for classifying mental effort. We utilized a 2-state Markov switching regression model to understand the efficacy of these physiological measures for predicting self-reported mental effort during logged activities. On average, a model with state-dependent relationships predicted within one unit of reported mental effort (training RMSE = 0.4, testing RMSE = 0.7). This automated sensing of mental effort can have applications in various domains including student engagement detection and cognitive state assessment in drivers, pilots, and caregivers.

Social context and the real-world consequences of social anxiety

2019 ◽  
Author(s):  
Juyoen Hur ◽  
Kathryn A. DeYoung ◽  
Samiha Islam ◽  
Allegra S. Anderson ◽  
Matthew Barstead ◽  
...  
Keyword(s):  
Young Adults ◽  
Social Anxiety ◽  
Real World ◽  
Emotional Experience ◽  
Romantic Partners ◽  
Social Contexts ◽  
Socially Anxious ◽  
Daily Lives ◽  
Ecological Momentary

Social anxiety lies on a continuum, and young adults with elevated symptoms are at risk for developing a range of debilitating psychiatric disorders. Yet, relatively little is known about the factors that govern the hour-by-hour experience and expression of social anxiety in daily life. Here, we used smartphone-based ecological momentary assessment (EMA) to intensively sample emotional experience across different social contexts in the daily lives of 228 young adults selectively recruited to represent a broad spectrum of social anxiety symptoms. Leveraging data from over 11,000 real-world assessments, results highlight the central role of close friends, family members, and romantic partners. The presence of close companions is associated with enhanced mood, yet socially anxious individuals have smaller confidant networks and spend less time with their close companions. Although higher levels of social anxiety are associated with a general worsening of mood, socially anxious individuals appear to derive larger benefits—lower levels of negative affect, anxiety, and depression—from the presence of their closest companions. In contrast, variation in social anxiety was unrelated to the amount of time spent with strangers, co-workers, and acquaintances; and we uncovered no evidence of emotional hypersensitivity to less-familiar individuals. Collectively, these findings provide a framework for understanding the deleterious consequences of social anxiety in emerging adulthood and set the stage for developing improved intervention strategies.

scholarly journals An effort-based social feedback paradigm reveals aversion to popularity in socially anxious participants and increased motivation in adolescents

PLoS ONE ◽  
2021 ◽  
Vol 16 (4) ◽  
pp. e0249326
Author(s):  
Dienke J. Bos ◽  
Emily D. Barnes ◽  
Benjamin M. Silver ◽  
Eliana L. Ajodan ◽  
Elysha Clark-Whitney ◽  
...  
Keyword(s):  
Young Adults ◽  
Social Anxiety ◽  
Social Status ◽  
Positive Feedback ◽  
High Status ◽  
Social Feedback ◽  
Physical Effort ◽  
Socially Anxious ◽  
Adolescents And Adults ◽  
Socially Motivated

We created a novel social feedback paradigm to study how motivation for potential social links is influenced in adolescents and adults. 88 participants (42F/46M) created online posts and then expended physical effort to show their posts to other users, who varied in number of followers and probability of positive feedback. We focused on two populations of particular interest from a social feedback perspective: adolescents relative to young adults (13–17 vs 18–24 years of age), and participants with social anxiety symptoms. Individuals with higher self-reported symptoms of social anxiety did not follow the typical pattern of increased effort to obtain social feedback from high status peers. Adolescents were more willing to exert physical effort on the task than young adults. Overall, participants were more likely to exert physical effort for high social status users and for users likely to yield positive feedback, and men were more likely to exert effort than women, findings that parallel prior results in effort-based tasks with financial rather than social rewards. Together the findings suggest social motivation is malleable, driven by factors of social status and the likelihood of a positive social outcome, and that age, sex, and social anxiety significantly impact patterns of socially motivated decision-making.

scholarly journals A Workflow for Affective Computing and Stress Recognition from Biosignals

2020 ◽  
Vol 2 (1) ◽  
pp. 85
Author(s):  
Dilana Hazer-Rau ◽  
Lin Zhang ◽  
Harald C. Traue
Keyword(s):  
Machine Learning ◽  
Affective Computing ◽  
Nearest Neighbor ◽  
Wearable Sensors ◽  
Data Conversion ◽  
Physiological Data ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Forward Selection ◽  
Stress Recognition

Affective computing and stress recognition from biosignals have a high potential in various medical applications such as early intervention, stress management and risk prevention, as well as monitoring individuals’ mental health. This paper presents an automated processing workflow for the psychophysiological recognition of emotion and stress states. Our proposed workflow allows the processing of biosignals in their raw state as obtained from wearable sensors. It consists of five stages: (1) Biosignal Preprocessing—raw data conversion and physiological data triggering, relevant information selection, artifact and noise filtering; (2) Feature Extraction—using different mathematical groups including amplitude, frequency, linearity, stationarity, entropy and variability, as well as cardiovascular-specific characteristics; (3) Feature Selection—dimension reduction and computation optimization using Forward Selection, Backward Elimination and Brute Force methods; (4) Affect Classification—machine learning using Support Vector Machine, Random Forest and k-Nearest Neighbor algorithms; (5) Model Validation—performance matrix computation using k-Cross, Leave-One-Subject-Out and Split Validations. All workflow stages are integrated into embedded functions and operators, allowing an automated execution of the recognition process. The next steps include further development of the algorithms and the integration of the developed tools into an easy-to-use system, thereby satisfying the needs of medical and psychological staff. Our automated workflow was evaluated using our uulmMAC database, previously developed for affective computing and machine learning applications in human–computer interaction.

scholarly journals Social context and the real-world consequences of social anxiety

2019 ◽  
Vol 50 (12) ◽  
pp. 1989-2000 ◽  
Author(s):  
Juyoen Hur ◽  
Kathryn A. DeYoung ◽  
Samiha Islam ◽  
Allegra S. Anderson ◽  
Matthew G. Barstead ◽  
...  
Keyword(s):  
Young Adults ◽  
Social Anxiety ◽  
Real World ◽  
Emotional Experience ◽  
Romantic Partners ◽  
Social Contexts ◽  
Socially Anxious ◽  
Daily Lives ◽  
The Real ◽  
Ecological Momentary

AbstractBackgroundSocial anxiety lies on a continuum, and young adults with elevated symptoms are at risk for developing a range of psychiatric disorders. Yet relatively little is known about the factors that govern the hour-by-hour experience and expression of social anxiety in the real world.MethodsHere we used smartphone-based ecological momentary assessment (EMA) to intensively sample emotional experience across different social contexts in the daily lives of 228 young adults selectively recruited to represent a broad spectrum of social anxiety symptoms.ResultsLeveraging data from over 11 000 real-world assessments, our results highlight the central role of close friends, family members, and romantic partners. The presence of such close companions was associated with enhanced mood, yet socially anxious individuals had fewer confidants and spent less time with the close companions that they do have. Although higher levels of social anxiety were associated with a general worsening of mood, socially anxious individuals appear to derive larger benefits – lower levels of negative affect, anxiety, and depression – from their close companions. In contrast, variation in social anxiety was unrelated to the amount of time spent with strangers, co-workers, and acquaintances; and we uncovered no evidence of emotional hypersensitivity to these less-familiar individuals.ConclusionsThese findings provide a framework for understanding the deleterious consequences of social anxiety in emerging adulthood and set the stage for developing improved intervention strategies.

scholarly journals Longitudinal Classification of Mental Effort Using Electrodermal Activity, Heart Rate, and Skin Temperature Data from a Wearable Sensor

2021 ◽  
Author(s):  
William Romine ◽  
Noah Schroeder ◽  
Anjali Edwards ◽  
Tanvi Banerjee
Keyword(s):  
Heart Rate ◽  
Skin Temperature ◽  
Electrodermal Activity ◽  
Wearable Sensors ◽  
Mental Effort ◽  
Physiological Data ◽  
Cognitive State ◽  
Switching Regression ◽  
Longitudinal Case Study ◽  
Time Duration

Recent studies show that physiological data can detect changes in mental effort, making way for the development of wearable sensors to monitor mental effort in school, work, and at home. We have yet to explore how such a device would work with a single participant over an extended time duration. We used a longitudinal case study design with ~38 hours of data to explore the efficacy of electrodermal activity, skin temperature, and heart rate for classifying mental effort. We utilized a 2-state Markov switching regression model to understand the efficacy of these physiological measures for predicting self-reported mental effort during logged activities. On average, a model with state-dependent relationships predicted within one unit of reported mental effort (training RMSE = 0.4, testing RMSE = 0.7). This automated sensing of mental effort can have applications in various domains including student engagement detection and cognitive state assessment in drivers, pilots, and caregivers.

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