Monitoring the impact of stress on the sleep patterns of pilgrims using wearable sensors

Muaremi, Amir; Bexheti, Agon; Gravenhorst, Franz; Arnrich, Bert; Tröster, Gerhard

doi:10.1109/bhi.2014.6864335

Cited by 50 publications

(26 citation statements)

References 17 publications

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“…A limitation of this work is that it does not consider sleep. Stress is not only experienced during the day but also at night, whilst we sleep [26]. Recording data 24 hours a day would be ideal to examine the manifestation and difference between conscious and unconscious stress.…”

Section: Discussionmentioning

confidence: 99%

A Lifelogging Platform Towards Detecting Negative Emotions in Everyday Life using Wearable Devices

Dobbins

Fairclough

Lisboa

et al. 2018

2018 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)

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Repeated experiences of negative emotions, such as stress, anger or anxiety, can have long-term consequences for health. These episodes of negative emotion can be associated with inflammatory changes in the body, which are clinically relevant for the development of disease in the long-term. However, the development of effective coping strategies can mediate this causal chain. The proliferation of ubiquitous and unobtrusive sensor technology supports an increased awareness of those physiological states associated with negative emotion and supports the development of effective coping strategies. Smartphone and wearable devices utilise multiple on-board sensors that are capable of capturing daily behaviours in a permanent and comprehensive manner, which can be used as the basis for self-reflection and insight. However, there are a number of inherent challenges in this application, including unobtrusive monitoring, data processing, and analysis. This paper posits a mobile lifelogging platform that utilises wearable technology to monitor and classify levels of stress. A pilot study has been undertaken with six participants, who completed up to ten days of data collection. During this time, they wore a wearable device on the wrist during waking hours to collect instances of heart rate (HR) and Galvanic Skin Resistance (GSR). Preliminary data analysis was undertaken using three supervised machine learning algorithms: Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA) and Decision Tree (DT). An accuracy of 70% was achieved using the Decision Tree algorithm.

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Section: Discussionmentioning

confidence: 99%

A Lifelogging Platform Towards Detecting Negative Emotions in Everyday Life using Wearable Devices

Dobbins

Fairclough

Lisboa

et al. 2018

2018 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)

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“…For example, GPS traces can assess amount of time spent outdoors, accelerometers provide an indication of physical activity, and detection of other Bluetooth devices can estimate a person's social contacts. Despite the field's infancy, there exist a few applications of mobile/wearable devices to mental health: stress monitoring in everyday life and the workplace 123,124 , early detection of Parkinson's disease 125 , and remote monitoring of sleep-awake activities to predict relapse in psychosis. 126,127…”

Section: Passive Sensing and Analyticsmentioning

confidence: 99%

How data science can advance mental health research

et al. 2018

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Accessibility of powerful computers and availability of so-called "big data" from a variety of sources means that data science approaches are becoming pervasive. However, their application in mental health research is often considered to be at an earlier stage than in other areas despite the complexity of mental health and illness making such a sophisticated approach particularly suitable. In this article we discuss current and potential applications of data science in mental health research using the UK Clinical Research Collaboration classification: underpinning research; aetiology; detection and diagnosis; treatment development; treatment evaluation; disease management; and health services research. We demonstrate that data science is already being widely applied in mental health research, but there is much more to be done now and in the future. The possibilities for data science in mental health research are substantial.

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“…Their model achieved a median accuracy of 72%. In other work, Muaremi et al [13] detected stress using sleeping patterns. Their work captured self-reports and data from ECG, heart rate variability (HRV), respiration, body temperature, galvanic skin response (GSR) and accelerometer data to classify the results using SVM, logistic regression (Logit), k-nearest-neighbour (kNN), random forest (RF), and neuronal network (NN).…”

Section: Related Workmentioning

confidence: 99%

Detecting Negative Emotions During Real-Life Driving via Dynamically Labelled Physiological Data

Dobbins

Fairclough

2018

2018 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)

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Driving is an activity that can induce significant levels of negative emotion, such as stress and anger. These negative emotions occur naturally in everyday life, but frequent episodes can be detrimental to cardiovascular health in the long term. The development of monitoring systems to detect negative emotions often rely on labels derived from subjective self-report. However, this approach is burdensome, intrusive, low fidelity (i.e. scales are administered infrequently) and places huge reliance on the veracity of subjective self-report. This paper explores an alternative approach that provides greater fidelity by using psychophysiological data (e.g. heart rate) to dynamically label data derived from the driving task (e.g. speed, road type). A number of different techniques for generating labels for machine learning were compared: 1) deriving labels from subjective self-report and 2) labelling data via psychophysiological activity (e.g. heart rate (HR), pulse transit time (PTT), etc.) to create dynamic labels of high vs. low anxiety for each participant. The classification accuracy associated with both labelling techniques was evaluated using Linear Discriminant Analysis (LDA) and Support Vector Machines (SVM). Results indicated that classification of driving data using subjective labelled data (1) achieved a maximum AUC of 73%, whilst the labels derived from psychophysiological data (2) achieved equivalent performance of 74%. Whilst classification performance was similar, labelling driving data via psychophysiology offers a number of advantages over self-reports, e.g. implicit, dynamic, objective, high fidelity.

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Monitoring the impact of stress on the sleep patterns of pilgrims using wearable sensors

Cited by 50 publications

References 17 publications

A Lifelogging Platform Towards Detecting Negative Emotions in Everyday Life using Wearable Devices

A Lifelogging Platform Towards Detecting Negative Emotions in Everyday Life using Wearable Devices

How data science can advance mental health research

Detecting Negative Emotions During Real-Life Driving via Dynamically Labelled Physiological Data

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