Sensor-based identification of human stress levels

Frank, Korbinian; Robertson, Patrick; Gross, Michael R.; Wiesner, Kevin

doi:10.1109/percomw.2013.6529469

Cited by 16 publications

(8 citation statements)

References 10 publications

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“…A Stroop test, especially a computer-based one, has been evaluated as an effective stressor in previous work on automatic stress recognition [5,6]. There are two Stroop test versions, 'congruent' and 'incongruent'.…”

Section: Related Workmentioning

confidence: 99%

Stress Recognition in Daily Work

Nakashima

Kim

Flutura

et al. 2016

Communications in Computer and Information Science

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Abstract. Automatic detection of work-related stress has attracted an increasing amount of attention from researchers from various disciplines and industries. An experiment is discussed in this paper that was designed to evaluate the efficacy of multimodal sensor measures that have often been used but not yet been systematically tested and compared with each other in previous work, such as pressure distribution sensor, physiological sensors, and an eye tracker. We used the Stroop test and information pick up task as the stressors. In the subject independent case in particular, signals from the combined (chair and floor) pressure distribution sensors, which we consider the most feasible sensors in the office environment, resulted in higher recognition accuracy rates than the physiological or eye tracker signals for the two stressors.

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Section: Related Workmentioning

confidence: 99%

Stress Recognition in Daily Work

Nakashima

Kim

Flutura

et al. 2016

Communications in Computer and Information Science

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“…Stress recognition applications are also fairly popular [97]. They can be realized using smartphone sensors.…”

Section: Health and Fitnessmentioning

confidence: 99%

Internet of Things: Architectures, Protocols, and Applications

Sethi

Sarangi

2017

Journal of Electrical and Computer Engineering

1,232

598

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The Internet of Things (IoT) is defined as a paradigm in which objects equipped with sensors, actuators, and processors communicate with each other to serve a meaningful purpose. In this paper, we survey state-of-the-art methods, protocols, and applications in this new emerging area. This survey paper proposes a novel taxonomy for IoT technologies, highlights some of the most important technologies, and profiles some applications that have the potential to make a striking difference in human life, especially for the differently abled and the elderly. As compared to similar survey papers in the area, this paper is far more comprehensive in its coverage and exhaustively covers most major technologies spanning from sensors to applications.

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“…A combination of several sensors at different parts of the body has been proposed among others by [6] (chest and thigh), [7] (chest and hip), [8] (left thigh, right ankle), [9] (wrist and thigh), [10] (right thigh, necklace, right and left wrists), [11] (torso, left and right sleeves, left upper and lower arm, left hand), and [9] (wrist and thigh).…”

Section: Related Workmentioning

confidence: 99%

Bayesian recognition of safety relevant motion activities with inertial sensors and barometer

Frank

Diaz

Robertson

et al. 2014

2014 IEEE/ION Position, Location and Navigation Symposium - PLANS 2014

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Activity recognition has been a hot topic in research throughout the last years. Walking, standing, sitting or lying have been detected with more or less confidence, in more or less suitable system designs. None of these systems however has entered daily life, neither in mass market, nor in professional environments. What is required is an unobtrusive system, requiring few resources and-most important-recognizing all important activities with high confidence. To this end, our research has focused on the professional market for safety related applications: first responders or also military use. Next to the classical motion related activities, our system supports motions in three dimensions that are necessary for all kinds of movements indoors as well as outdoors. These include falling, wriggling, crawling, climbing stairs up and down and using an elevator. We have proven this approach to run in real-time with only a single wireless sensor attached to the body while achieving robust and reliable recognition with a delay lower than two seconds.

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Sensor-based identification of human stress levels

Cited by 16 publications

References 10 publications

Stress Recognition in Daily Work

Stress Recognition in Daily Work

Internet of Things: Architectures, Protocols, and Applications

Bayesian recognition of safety relevant motion activities with inertial sensors and barometer

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