Stress detection in daily life scenarios using smart phones and wearable sensors: A survey

Can, Yekta Said; Arnrich, Bert; Ersoy, Cem

doi:10.1016/j.jbi.2019.103139

Cited by 280 publications

(150 citation statements)

References 41 publications

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“…There are multiple studies for emotion recognition while using videos, images, or music as an emotional stimulus [37][38][39]. Moreover, a recent survey showed that many studies that use machine leaning to detect stress and anxiety are creating stress stimuli through mental tasks in a laboratory setup [40]. However, in our case, anxiety was induced through VRET stimuli, which cannot be easily compared with a video or images.…”

Section: Vret As Stimulimentioning

confidence: 91%

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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: Vret As Stimulimentioning

confidence: 91%

“…Various factors can affect the reliability of mental stress (anxiety) recognition systems. One of the latest systematic reviews of stress recognition systems that uses wearable sensors as a source identifies these main challenges [40]:…”

Section: Vret and Anxiety Detection Limitationsmentioning

confidence: 99%

Anxiety Level Recognition for Virtual Reality Therapy System Using Physiological Signals

et al. 2019

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“…reveals ANS activity (Cho et al, 2017;Seoane et al, 2014). The aforementioned physiological signals are considered to be reliable stress indicators (Karthikeyan et al, 2013) as they can contain information related to the intensity and the quality of the experience of a subject (Can et al, 2019). An essential facilitator for physiological signal records is the use of new sensing capabilities that predominates mainly in urban research .…”

Section: Stress and Wearable Sensorsmentioning

confidence: 99%

“…The resulting data can be used for basic research, clinical application or during daily routines in real-life situations with less bias than selfreported surveys (Birenboim et al, 2019). These biosensors may be valuable tools to detect emotions as they provide high-quality data which are accurate, complete, timely, detailed, adequately portrayed and retain contextual information to support a decision-making process (Can et al, 2019). However, there are some preconditions for obtaining reliable and useful measurements that can be used for emotion extraction.…”

Section: Stress and Wearable Sensorsmentioning

confidence: 99%

Spatial Analysis of Moments of Stress Derived from Wearable Sensor Data

Kyriakou

Resch

2019

Adv. Cartogr. GIScience Int. Cartogr. Assoc.

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Abstract. Over the last years, we have witnessed an increasing interest in urban health research using physiological sensors. There is a rich repertoire of methods for stress detection using various physiological signals and algorithms. However, most of the studies focus mainly on the analysis of the physiological signals and disregard the spatial analysis of the extracted geo-located emotions. Methodologically, the use of hotspot maps created through point density analysis dominates in previous studies, but this method may lead to inaccurate or misleading detection of high-intensity stress clusters. This paper proposes a methodology for the spatial analysis of moments of stress (MOS). In a first step, MOS are identified through a rule-based algorithm analysing galvanic skin response and skin temperature measured by low-cost wearable physiological sensors. For the spatial analysis, we introduce a MOS ratio for the geo-located detected MOS. This ratio normalises the detected MOS in nearby areas over all the available records for the area. Then, the MOS ratio is fed into a hot spot analysis to identify hot and cold spots. To validate our methodology, we carried out two real-world field studies to evaluate the accuracy of our approach. We show that the proposed approach is able to identify spatial patterns in urban areas that correspond to self-reported stress.

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“…Stres telah menjadi penyebab yang signifikan karena berbagai penyakit di masyarakat modern (Can, Arnrich, & Ersoy, 2019). Stres psikososial kronis berkontribusi terhadap kondisi kesehatan, dengan demikian tantangan klinis yang berkembang dan meresap ke sistem perawatan kesehatan saat ini.…”

Section: Pendahuluanunclassified

Efektivitas Penggunaan Mindfulness App Berbasis Smartphone Untuk Mengurangi Stres

Saripah

Handiyani

2019

JIKBH

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Stres merupakan penyebab banyak penyakit di masyarakat modern. Pengurangan stres berbasis mindfulness merupakan salah satu cara mengurangi tingkat stres yang dialami oleh seseorang. Karena meningkatnya permintaan akan layanan kesehatan preventif terhadap stres, maka penting untuk mengembangkan strategi dalam mengelola stres pada smarthphone. Mindfulness dapat diintegrasikan ke dalam praktik terapi untuk mengurangi stres. Pelatihan berbasis bukti mampu mengurangi stres menggunakan mindfulness App pada smarthphone. Kajian ini betujuan untuk meninjau dan menganalisis secara sistematis mengenai efektivitas penggunaan mindfulness App untuk mengatasi stres. Metode kajian ini dirancang sebagai literature review dengan mengumpulkan literatur yang didapatkan dari database Scopus, ProQuest, dan Wiley Online Library. Uji coba ini menunjukkan bahwa meditasi mindfulness terpandu singkat yang disampaikan melalui smartphone dan dipraktikkan beberapa kali per minggu dapat meningkatkan hasil yang terkait dengan pengurangan stres dan kesejahteraan kerja, dengan efek yang berpotensi bertahan lama.

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Stress detection in daily life scenarios using smart phones and wearable sensors: A survey

Cited by 280 publications

References 41 publications

Anxiety Level Recognition for Virtual Reality Therapy System Using Physiological Signals

Anxiety Level Recognition for Virtual Reality Therapy System Using Physiological Signals

Spatial Analysis of Moments of Stress Derived from Wearable Sensor Data

Efektivitas Penggunaan Mindfulness App Berbasis Smartphone Untuk Mengurangi Stres

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