Daily Stress Recognition System Using Activity Tracker and Smartphone Based on Physical Activity and Heart Rate Data

Lawanont, Worawat; Mongkolnam, Pornchai; Nukoolkit, Chakarida; Inoue, Masahiro

doi:10.1007/978-3-319-92028-3_2

Cited by 12 publications

(8 citation statements)

References 14 publications

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“…Altogether, 67 full-text papers were read; of these, 16 were excluded because they did not relate sensor data to health status assessment [25-40], while another six were feasibility studies with no relation to health status assessment [41-46].…”

Section: Resultsmentioning

confidence: 99%

Mobile Phone and Wearable Sensor-Based mHealth Approaches for Psychiatric Disorders and Symptoms: Systematic Review

et al. 2019

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Background Mobile Therapeutic Attention for Patients with Treatment-Resistant Schizophrenia (m-RESIST) is an EU Horizon 2020-funded project aimed at designing and validating an innovative therapeutic program for treatment-resistant schizophrenia. The program exploits information from mobile phones and wearable sensors for behavioral tracking to support intervention administration. Objective To systematically review original studies on sensor-based mHealth apps aimed at uncovering associations between sensor data and symptoms of psychiatric disorders in order to support the m-RESIST approach to assess effectiveness of behavioral monitoring in therapy. Methods A systematic review of the English-language literature, according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, was performed through Scopus, PubMed, Web of Science, and the Cochrane Central Register of Controlled Trials databases. Studies published between September 1, 2009, and September 30, 2018, were selected. Boolean search operators with an iterative combination of search terms were applied. Results Studies reporting quantitative information on data collected from mobile use and/or wearable sensors, and where that information was associated with clinical outcomes, were included. A total of 35 studies were identified; most of them investigated bipolar disorders, depression, depression symptoms, stress, and symptoms of stress, while only a few studies addressed persons with schizophrenia. The data from sensors were associated with symptoms of schizophrenia, bipolar disorders, and depression. Conclusions Although the data from sensors demonstrated an association with the symptoms of schizophrenia, bipolar disorders, and depression, their usability in clinical settings to support therapeutic intervention is not yet fully assessed and needs to be scrutinized more thoroughly.

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Section: Resultsmentioning

confidence: 99%

Mobile Phone and Wearable Sensor-Based mHealth Approaches for Psychiatric Disorders and Symptoms: Systematic Review

et al. 2019

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“…There are numerous different methods reported in the stress detection literature for inducing stress. These include different arithmetic tasks [20,26,28,29,34], games/puzzles [25,30], exam-like conditions [14,16,19,31], and everyday situations such as driving [38,39] or work shifts [9,10,33]. However, not all of these are standardised and reliable stressors, only ad hoc methods designed and implemented by the researchers themselves, often without psychological expertise.…”

Section: State Of the Artmentioning

confidence: 99%

“…Chronic stress assessment is mainly executed based on data recorded throughout multiple days or weeks, sometimes months. In general, longer time intervals spanning hours are identified and classified as stressful or resting periods, while some solutions also try to recognise physical activity and sleeping phases as well [8][9][10]. On the other hand, detecting the presence or build-up of acute stress is usually initiated by analysing recordings of a couple of minutes, covering a total of 0.5-1 h at most.…”

Section: Introductionmentioning

confidence: 99%

“…In other cases, electrodermal activity (EDA) [27] or electromagnetic waves ("bioradar") [28] are used. Additional sensors used by multimodal approaches include the galvanic skin response (GSR) [29][30][31][32], respiration [29,30,33], electromyography (EMG) [34], and even such data as physical (in)activity, calories used or sleep quality, measured by activity trackers [9,10]. The focus of the research is shifting to developing methods that utilise compact, inexpensive wearable sensor devices suitable for everyday use for both approaches.…”

Section: Introductionmentioning

confidence: 99%

“…The focus of the research is shifting to developing methods that utilise compact, inexpensive wearable sensor devices suitable for everyday use for both approaches. Such devices are chest belts [13,22,33] or ECG-infused clothing [19], wrist bracelets or activity trackers [9,10,20,27], or other portable ECG devices [13,21]. Unfortunately, these are not yet without some drawbacks.…”

Section: Introductionmentioning

confidence: 99%

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Efficient methods for acute stress detection using heart rate variability data from Ambient Assisted Living sensors

Szakonyi

Vassányi

Schumacher³

et al. 2021

BioMed Eng OnLine

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Background Using Ambient Assisted Living sensors to detect acute stress could help people mitigate the harmful effects of everyday stressful situations. This would help both the healthy and those affected more by sudden stressors, e.g., people with diabetes or heart conditions. The study aimed to develop a method for providing reliable stress detection based on heart rate variability features extracted from portable devices. Methods Features extracted from portable electrocardiogram sensor recordings were used for training various classification algorithms for stress detection purposes. Data were recorded in a clinical trial with 7 participants and two stressors, the Trier Social Stress Test and the Stroop colour word test, both validated by standardised questionnaires. Different heart rate variability feature sets (all, time-domain and non-linear only, frequency-domain only) were tested to investigate how classification performance is affected, in addition to various time window length setups and participant-wise training sessions. The accuracy and F1 score of the trained models were compared and analysed. Results The best results were achieved with models using time-domain and non-linear heart rate variability features with 5-min-long overlapping time windows, yielding 96.31% accuracy and 96.26% F1 score. Shorter overlapping windows had slightly lower performance, with 91.62–94.55% accuracy and 91.77–94.55% F1 score ranges. Non-overlapping window configurations were less effective, with both accuracy and F1 score below 88%. For participant-wise learning, average F1 scores of 99.47%, 98.93% and 96.1% were achieved for feature sets using all, time-domain and non-linear, and frequency-domain features, respectively. Conclusion The tested stress detector models based on heart rate variability data recorded by a single electrocardiogram sensor performed just as well as those published in the literature working with multiple sensors, or even better. This suggests that once portable devices such as smartwatches provide reliable hear rate variability recordings, efficient stress detection can be achieved without the need for additional physiological measurements.

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Human Energy in Organizations: Theoretical Foundations and IT-Based Assessment

Fellmann

Lambusch

Weigelt

2020

Lecture Notes in Computer Science

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Daily Stress Recognition System Using Activity Tracker and Smartphone Based on Physical Activity and Heart Rate Data

Cited by 12 publications

References 14 publications

Mobile Phone and Wearable Sensor-Based mHealth Approaches for Psychiatric Disorders and Symptoms: Systematic Review

Mobile Phone and Wearable Sensor-Based mHealth Approaches for Psychiatric Disorders and Symptoms: Systematic Review

Efficient methods for acute stress detection using heart rate variability data from Ambient Assisted Living sensors

Human Energy in Organizations: Theoretical Foundations and IT-Based Assessment

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