Embedded Low-Power Processor for Personalized Stress Detection

Attaran, Nasrin; Puranik, Abhilash; Brooks, Justin

doi:10.1109/tcsii.2018.2799821

Cited by 43 publications

(22 citation statements)

References 9 publications

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“…Moreover, the trees are shallow (maximum depth=2) to reduce the model's complexity; thus, minimize overfitting. Finally, our results is similar to other published literature: in general, person-specific models achieve accuracy greater than 90% [48], [ [37].…”

Section: Individual Differences In Stress Predictionsupporting

confidence: 92%

Effect of Person-specific Biometrics in Improving Generic Stress Predictive Models

Nkurikiyeyezu¹,

Yokokubo²,

Lopez³

2020

Sensors and Materials

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Because stress is subjective and is expressed differently from one person to another, generic stress prediction models (i.e., models that predict the stress of any person) perform crudely. Only person-specific ones (i.e., models that predict the stress of a preordained person) yield reliable predictions, but they are not adaptable and costly to deploy in real-world environments. For illustration, in an office environment, a stress monitoring system that uses person-specific models would require collecting new data and training a new model for every employee. Moreover, once deployed, the models would deteriorate and need expensive periodic upgrades because stress is dynamic and depends on unforeseeable factors. We propose a simple, yet practical and costeffective calibration technique that derives an accurate and personalized stress prediction model from physiological samples collected from a large population. We validate our approach on two stress datasets. The results show that our technique performs much better than a generic model. For instance, a generic model achieved only a 42.5% ± 19.9% accuracy. However, with only 100 calibration samples, we raised its accuracy to 95.2%±0.5%. We also propose a blueprint for a stress monitoring system based on our strategy, and we debate its merits and limitation. Finally, we made public our source code and the relevant datasets to allow other researchers to replicate our findings.

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Section: Individual Differences In Stress Predictionsupporting

confidence: 92%

Effect of Person-specific Biometrics in Improving Generic Stress Predictive Models

Nkurikiyeyezu¹,

Yokokubo²,

Lopez³

2020

Sensors and Materials

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“…So ist es möglich, durch ML physiologische Daten in Echtzeit zu verarbeiten (Attaran et al. 2018 ), Migräne frühzeitig zu erkennen (Koskimäki et al. 2017 ), sowie das Stresslevel von Patienten vor Operationen im Vorfeld zu bestimmen (Anusha et al.…”

Section: Motivation Und Projektvorstellung „Dynamische Pause“unclassified

Einsatz von Maschinellem Lernen für die Vorhersage von Stress am Beispiel der Logistik

et al. 2021

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ZusammenfassungStress und seine komplexen Wirkungen werden bereits seit Anfang des 20. Jahrhunderts erforscht. Die vielfältigen psychischen und physischen Stressoren in der Arbeitswelt können in Summe zu Störungen des Organismus und zu Erkrankungen führen. Da die Ausprägung körperlicher und subjektiver Folgen von Stress individuell unterschiedlich ist, lassen sich keine absoluten Grenzwerte ermitteln. Zur Erforschung der systematischen Mustererkennung physiologischer und subjektiver Stressparameter sowie einer Stressvorhersage, werden in dem vorliegenden Beitrag Methoden des maschinellen Lernens (ML) eingesetzt. Als praktischer Anwendungsfall dient die Logistikbranche, in der Belastungsfaktoren häufig in der Tätigkeit und der Arbeitsorganisation begründet liegen. Ein Gestaltungselement bei der Prävention von Stress ist die Arbeitspause. Mit ML-Methoden wird untersucht, inwieweit Stress auf Basis physiologischer und subjektiver Parameter vorhergesagt werden kann, um Pausen individuell zu empfehlen. Im Beitrag wird der Zwischenstand einer Softwarelösung für ein dynamisches Pausenmanagement für die Logistik vorgestellt.Praktische Relevanz: Das Ziel der Softwarelösung „Dynamische Pause“ besteht darin, Stress in Folge mentaler und physischer Belastungsfaktoren in der Logistik präventiv vorzubeugen und die Beschäftigten auf lange Sicht gesund, zufrieden, arbeitsfähig und produktiv zu halten. Infolge individualisierter Erholungspausen als Gestaltungselement, können Unternehmen unterstützt werden, Personalressourcen entsprechend der dynamischen Anforderungen der Logistik flexibler einzusetzen.

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“…However, this combination of sensors cannot be applied in the scenario of a public presentation, where the presenter must be free to move and the audience must not notice the presence of the sensors. At best of authors' knowledge the only work in literature that aims at estimating stress leveraging an FPGA architecture is the one in [12]. Again, the proposed setup is not designed to be applicable in a public speaking scenario.…”

Section: Related Work and Evaluationmentioning

confidence: 99%

EMPhASIS: An EMbedded Public Attention Stress Identification System

Leoni

Ciallella

Stornaiuolo

et al. 2020

2020 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)

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Stress is a general adaptation syndrome, caused by a person's perception of danger about an event. While the perception of an event as stressful varies for each individual, several studies show that public speaking is a source of anxiety and panic for most people. Moreover, the inability to prevent or manage these conditions affects their overall performance negatively. The literature depicts different systems developed in order to recognize the subject's stress level by monitoring physiological signals. However, they are specifically designed for laboratory environments and clinical analysis and are often characterized by several limitations. Therefore, aiming at developing a dailylife application able to help people prevent and handle anxiety conditions by improving their self-perception, we propose an embedded system for real-time biometric feature analysis. This system is able to handle all the steps from the acquisition to the stress and anxiety status classification. The whole framework is implemented on a Xilinx PYNQ-Z1 board, leveraging both the ARM processor and the FPGA. Taking advantage of the programmable logic brings to a system able to process data with higher performance and energy efficiency, helping to face the embedded application constraints.

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Embedded Low-Power Processor for Personalized Stress Detection

Cited by 43 publications

References 9 publications

Effect of Person-specific Biometrics in Improving Generic Stress Predictive Models

Effect of Person-specific Biometrics in Improving Generic Stress Predictive Models

Einsatz von Maschinellem Lernen für die Vorhersage von Stress am Beispiel der Logistik

EMPhASIS: An EMbedded Public Attention Stress Identification System

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