Cluster-Based Analysis for Personalized Stress Evaluation Using Physiological Signals

Xu, Qianli; Nwe, Tin Lay; Guan, Cuntai

doi:10.1109/jbhi.2014.2311044

Cited by 137 publications

(78 citation statements)

References 19 publications

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“…This approach allowed to increase accuracy by 12% compared with the general model. Xu et al [36] studied stress detection with neural networks using physiological data, collected in a lab; clustering of the test subjects was also based on their unlabelled data. Assigning of 44 subjects to two clusters allowed to increase accuracy (i.e.…”

Section: Related Workmentioning

confidence: 99%

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Unobtrusive stress detection on the basis of smartphone usage data

Vildjiounaite

Kallio

Kyllönen

et al. 2018

Pers Ubiquit Comput

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Stress has become an important health problem, but existing stress detectors are inconvenient in long-term real-life use because users either have to wear dedicated devices or expend notable interaction efforts in system adaptation to specifics of each person. Adaptation is necessary because individuals significantly differ in their perception of stress and stress responses, but typical adaptation employs supervised learning methods and hence requires fairly large sets of labelled data (i.e. information on whether each reporting period was stressful or not) from every user. To address these problems, we propose a novel unsupervised stress detector, based on using a smartphone as the only device and using discrete hidden Markov models (HMM) with maximum posterior marginal (MPM) decisions for analysis of phone data. Our detector requires neither additional hardware nor data labelling and hence is truly unobtrusive and suitable for lifelong use. Its accuracy was evaluated using two real-life datasets: in the first case, adaptation was based on very short (a few days) phone interaction histories of each individual, and in the second case-on longer histories. In these tests, the proposed HMM-MPM achieved 59 and 70% accuracies, respectively, which is comparable with results of fully supervised methods, reported by other works.

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Section: Related Workmentioning

confidence: 99%

“…each target individual had to provide about 60 self-reports [10,21]. In addition, it was observed that success of reusing data of other individuals strongly depends on the degree of similarity between these individuals and the target person [21,36].…”

Section: Introductionmentioning

confidence: 99%

Unobtrusive stress detection on the basis of smartphone usage data

Vildjiounaite

Kallio

Kyllönen

et al. 2018

Pers Ubiquit Comput

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“…Many research studies use psychophysiological signals collected during stress induced by solving mental tasks in the laboratory environment [42,[44][45][46][47][48]. These tasks usually involve solving logical puzzles, mathematical equations, or dealing with time-sensitive challenges.…”

Section: Comparison Of Resultsmentioning

confidence: 99%

Anxiety Level Recognition for Virtual Reality Therapy System Using Physiological Signals

et al. 2019

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Virtual reality exposure therapy (VRET) can have a significant impact towards assessing and potentially treating various anxiety disorders. One of the main strengths of VRET systems is that they provide an opportunity for a psychologist to interact with virtual 3D environments and change therapy scenarios according to the individual patient’s needs. However, to do this efficiently the patient’s anxiety level should be tracked throughout the VRET session. Therefore, in order to fully use all advantages provided by the VRET system, a mental stress detection system is needed. The patient’s physiological signals can be collected with wearable biofeedback sensors. Signals like blood volume pressure (BVP), galvanic skin response (GSR), and skin temperature can be processed and used to train the anxiety level classification models. In this paper, we combine VRET with mental stress detection and highlight potential uses of this kind of VRET system. We discuss and present a framework for anxiety level recognition, which is a part of our developed cloud-based VRET system. Physiological signals of 30 participants were collected during VRET-based public speaking anxiety treatment sessions. The acquired data were used to train a four-level anxiety recognition model (where each level of ‘low’, ‘mild’, ‘moderate’, and ‘high’ refer to the levels of anxiety rather than to separate classes of the anxiety disorder). We achieved an 80.1% cross-subject accuracy (using leave-one-subject-out cross-validation) and 86.3% accuracy (using 10 × 10 fold cross-validation) with the signal fusion-based support vector machine (SVM) classifier.

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“…Both of them are able sensors and bio signal processing technologies to be developed for detecting the human stress. There are few types of bio signal processing technologies use for human stress detection such as Electrocardiography (ECG), Electromyography (EMG), Electroencephalography (EEG), Blood Pressure (BP), Blood Volume Pulses (BVP) and Galvanic Skin Resistance (GSR) [6]. The reaction of an athletes towards recovery process can be identified by using EEG.…”

Section: Introductionmentioning

confidence: 99%

Ice Bath Therapy on Athletes Recovery Response Using EEG

Hakimi¹,

Salleh²,

Ainul³

et al. 2018

IJET

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Sport recovery system is an integral aspect to help athletes adapt faster to training. This is an important process of physical preparation by reducing fatigue where the athletes can ready for the next competition or training. However, most of an athlete doing training without having the fully recovery after the training and can affect their performance. The cold bath water immersion is the one of common technique to recover from the fatigue. In this study, Neurosky mindwave is use to extract the brain wave of an athlete to know the response of an athlete when perform the cold water immersion. The responses of an athlete include meditation which is in alpha wave that state in relax condition and beta wave that is in fatigue condition in sport. The raw brain wave signal that extract using Neurosky mindwave is analysed using Matlab in terms of time domain. After that, Fast Fourier Transform (FFT) will use to analysed in terms of frequency domain. This project used alpha and beta band to collect the data. The analysis have made based on the peak value in frequency domain to know the best time for cold water immersion and best cold bath temperature.

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Cluster-Based Analysis for Personalized Stress Evaluation Using Physiological Signals

Cited by 137 publications

References 19 publications

Unobtrusive stress detection on the basis of smartphone usage data

Unobtrusive stress detection on the basis of smartphone usage data

Anxiety Level Recognition for Virtual Reality Therapy System Using Physiological Signals

Ice Bath Therapy on Athletes Recovery Response Using EEG

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