Keep the Stress Away with SoDA: Stress Detection and Alleviation System

Akmandor, Ayten Ozge; Jha, Niraj K.

doi:10.1109/tmscs.2017.2703613

Cited by 109 publications

(47 citation statements)

References 45 publications

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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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Section: Comparison Of Resultsmentioning

confidence: 99%

Anxiety Level Recognition for Virtual Reality Therapy System Using Physiological Signals

et al. 2019

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“…Other studies practiced multimodality by employing multiple biosignals to achieve even higher accuracy levels. In a recent stress detection and alleviation research work, Akmandor et al [21] recorded EDA, ECG, blood pressure, blood oximeter, and respiration rates of the participants. In order to induce stress, memory games and IAPS were used, and for the alleviation part, they used various stress mitigation techniques such as classical music and micro-meditation.…”

Section: Related Workmentioning

confidence: 99%

“…In order to induce stress, memory games and IAPS were used, and for the alleviation part, they used various stress mitigation techniques such as classical music and micro-meditation. Their classification accuracy using SVM and kNN for their binary class problem was 95.8% [21]. Researchers used a combination of medical-grade devices with the highest possible accuracies in the laboratory settings.…”

Section: Related Workmentioning

confidence: 99%

How Laboratory Experiments Can Be Exploited for Monitoring Stress in the Wild: A Bridge Between Laboratory and Daily Life

Can

Gokay

Kılıç

et al. 2020

Sensors

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Chronic stress leads to poor well-being, and it has effects on life quality and health. Society may have significant benefits from an automatic daily life stress detection system using unobtrusive wearable devices using physiological signals. However, the performance of these systems is not sufficiently accurate when they are used in unrestricted daily life compared to the systems tested in controlled real-life and laboratory conditions. To test our stress level detection system that preprocesses noisy physiological signals, extracts features, and applies machine learning classification techniques, we used a laboratory experiment and ecological momentary assessment based data collection with smartwatches in daily life. We investigated the effect of different labeling techniques and different training and test environments. In the laboratory environments, we had more controlled situations, and we could validate the perceived stress from self-reports. When machine learning models were trained in the laboratory instead of training them with the data coming from daily life, the accuracy of the system when tested in daily life improved significantly. The subjectivity effect coming from the self-reports in daily life could be eliminated. Our system obtained higher stress level detection accuracy results compared to most of the previous daily life studies.

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“…This is a YOGA respiratory pattern which has the most resemblance to individuals respiratory pattern. Akmandor et al [26] designed a stress detection and reduction scheme. They collected ECG, EDA, blood pressure, respiration and blood oximeter data in the laboratory.…”

Section: Related Workmentioning

confidence: 99%

Personal Stress-Level Clustering and Decision-Level Smoothing to Enhance the Performance of Ambulatory Stress Detection With Smartwatches

et al. 2020

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Researchers strive hard to develop effective ways to detect and cope with enduring high-level daily stress as early as possible to prevent serious health consequences. Although research has traditionally been conducted in laboratory settings, a set of new studies have recently begun to be conducted in ecological environments with unobtrusive wearable devices. Since patterns of stress are ideographic, person-independent models have generally lower accuracies. On the contrary, person-specific models have higher accuracies but they require a long-term data collection period. In this study, we developed a hybrid approach of personal level stress clustering by using baseline stress self-reports to increase the success of person-independent models without requiring a substantial amount of personal data. We further added decision level smoothing to our unobtrusive smartwatch based stress level differentiation system to increase the performance by correcting false labels assigned by the machine learning algorithm. In order to test and evaluate our system, we collected physiological data from 32 participants of a summer school with wrist-worn unobtrusive wearable devices. This event is comprised of baseline, lecture, exam and recovery sessions. In the recovery session, a stress management method was applied to alleviate the stress of the participants. The perceived stress in the form of NASA-TLX questionnaires collected from the users as self-reports and physiological stress levels extracted using wearable sensors are examined separately. By using our system, we were able to differentiate the 3-levels of stress successfully. We further substantially increase our performance by personal stress level clustering and by applying high-level accuracy calculation and decision level smoothing methods. We also demonstrated the success of the stress reduction methods by analyzing physiological signals and self-reports.

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Keep the Stress Away with SoDA: Stress Detection and Alleviation System

Cited by 109 publications

References 45 publications

Anxiety Level Recognition for Virtual Reality Therapy System Using Physiological Signals

Anxiety Level Recognition for Virtual Reality Therapy System Using Physiological Signals

How Laboratory Experiments Can Be Exploited for Monitoring Stress in the Wild: A Bridge Between Laboratory and Daily Life

Personal Stress-Level Clustering and Decision-Level Smoothing to Enhance the Performance of Ambulatory Stress Detection With Smartwatches

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