Crowding density in urban environment and its effects on emotional responding of pedestrians: Using wearable device technology with sensors capturing proximity and psychophysiological emotion responses while walking in the street

Engelniederhammer, Anna; Papastefanou, Georgios; Xiang, Luyao

doi:10.1080/10911359.2019.1579149

Cited by 49 publications

(32 citation statements)

References 39 publications

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Section: Discussionmentioning

confidence: 99%

“…The physiology of the integumentary system in responding to stress is a well-understood phenomenon, with two studies [ 16 , 19 ] incorporating this physiology into a wearable device. Engelniederhammer et al (2019), who used a sensor smart wristband (Bodymonitor™, Gesis Leibniz-Institute for the Social Sciences, Mannheim, Germany), reported that the EDA is the most simplistic and accurate indicator of emotional arousal, notably stress or aggression [ 16 ]. EDA is useful in the detection of stress but may pose challenges with respect to reliability of results in populations who have comorbidities such as diabetes mellitus or hyperthyroidism (though this can be overcome using models as outlined by Kim et al, 2020 [ 19 ]).…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Smart Devices and Wearable Technologies to Detect and Monitor Mental Health Conditions and Stress: A Systematic Review

Hickey

Chalmers

Newton

et al. 2021

Sensors

120

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Recently, there has been an increase in the production of devices to monitor mental health and stress as means for expediting detection, and subsequent management of these conditions. The objective of this review is to identify and critically appraise the most recent smart devices and wearable technologies used to identify depression, anxiety, and stress, and the physiological process(es) linked to their detection. The MEDLINE, CINAHL, Cochrane Central, and PsycINFO databases were used to identify studies which utilised smart devices and wearable technologies to detect or monitor anxiety, depression, or stress. The included articles that assessed stress and anxiety unanimously used heart rate variability (HRV) parameters for detection of anxiety and stress, with the latter better detected by HRV and electroencephalogram (EGG) together. Electrodermal activity was used in recent studies, with high accuracy for stress detection; however, with questionable reliability. Depression was found to be largely detected using specific EEG signatures; however, devices detecting depression using EEG are not currently available on the market. This systematic review highlights that average heart rate used by many commercially available smart devices is not as accurate in the detection of stress and anxiety compared with heart rate variability, electrodermal activity, and possibly respiratory rate.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Smart Devices and Wearable Technologies to Detect and Monitor Mental Health Conditions and Stress: A Systematic Review

Hickey

Chalmers

Newton

et al. 2021

Sensors

120

View full text Add to dashboard Cite

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“…ball or stick) Traffic event detection [ 47 ] Task Shot location - Angle/distance of goal face visible Pocock et al [ 48 ], Goldsberry [ 49 ] Player and ball tracking aligned with game logs Task Time in possession - Individual possession length - Length of possession chain - Team split of previous 10 mins Higham et al [ 50 ], Robertson et al [ 32 ] Player and ball tracking aligned with game logs Task/individual Shot trends: ‘hot hand fallacy’ - Team - Individual Skinner [ 51 ], Bar-Eli et al [ 52 ] Player and ball tracking aligned with game logs Individual Disposal efficiency - In game - History Pocock et al [ 48 ], Reich et al [ 53 ] Player and ball tracking aligned with game logs paired with analytics Task Available space - Physical pressure - No. of players between ball and goal - Ratio of attackers to defenders Rein et al [ 54 ], Alexander et al [ 55 ] Player and ball tracking paired with improved analytics Proximity sensor Emotional response in crowds [ 56 ] Task Kick distance Blair et al [ 57 , 58 ] Ball tracking Automated measurement through computer vision Automated detection of distances in cars [ 59 ] Individual/task Physical output - Game time played - Time between efforts - High speed metres Almonroeder et al [ 60 ], Sarmento et al [ 61 ] Player and ball tracking paired with match events Task Ball weight Nimmins et al [ 62 ], Fitzpatrick et al [ 63 ] ...…”

Section: Technologymentioning

confidence: 99%

“…To represent the influence of technology on the measurement of constraints, the concept of pressure in a team sport context presents a useful example. As a scientific construct, pressure has been measured in multiple ways; through the proximity of opponents on the field as measured by player tracking systems [104,108], an athlete's physiological and emotional response measured via sensors [39,56] or the context of a game via the scoreboard or time remaining [48,90]. Adding more data types to define pressure more comprehensively will likely lead to a greater understanding of its influence on performance outcomes.…”

Section: Technologymentioning

confidence: 99%

Methodological Considerations for Furthering the Understanding of Constraints in Applied Sports

et al. 2021

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Commonly classified as individual, task or environmental, constraints are boundaries which shape the emergence of functional movement solutions. In applied sport, an ongoing challenge is to improve the measurement, analysis and understanding of constraints to key stakeholders. Methodological considerations for furthering these pursuits should be centred around an interdisciplinary approach. This integration of methodology and knowledge from different disciplines also encourages the sharing of encompassing principles, concepts, methods and data to generate new solutions to existing problems. This narrative review discusses how a number of rapidly developing fields are positioned to help guide, support and progress an understanding of sport through constraints. It specifically focuses on examples from the fields of technology, analytics and perceptual science. It discusses how technology is generating large quantities of data which can improve our understanding of how constraints shape the movement solutions of performers in training and competition environments. Analytics can facilitate new insights from numerous and complex data through enhanced non-linear and multivariate analysis techniques. The role of the perceptual sciences is discussed with respect to generating outputs from analytics that are more interpretable for the end-user. Together, these three fields of technology, analytics and perceptual science may enable a more comprehensive understanding of constraints in sports performance.

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“…Mobile signaling data, GPS data, and LBS data become readily accessible data to facilitate the study of social activities in a meso or micro scale [13][14][15]. Embedded sensors, especially wearable devices make it feasible to measure and visualize people's activities, motions and preference [16][17][18][19]. Besides, physical environment captured by Remote Sensing Imagery [20] and Street View Pictures [21][22][23] can be quantified now thanks to the achievement in deep learning algorithms for image processing.…”

Section: Introductionmentioning

confidence: 99%

Quantifying the usage of small public spaces using deep convolutional neural network

et al. 2020

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Small public spaces are the key built environment elements that provide venues for various of activities. However, existing measurements or approaches could not efficiently and effectively quantify how small public spaces are being used. In this paper, we utilized a deep convolutional neural network to quantify the usage of small public spaces through recorded videos as a reliable and robust method to bridge the literature gap. To start with, we deployed photographic devices to record videos that cover the minimum enclosing square of a small public space for a certain period of time, then utilized a deep convolutional neural network to detect people in these videos and converted their location from image-based position to real-world projected coordinates. To validate the accuracy and robustness of the method, we experimented our approach in a residential community in Beijing, and our results confirmed that the usage of small public spaces could be measured and quantified effectively and efficiently.

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Crowding density in urban environment and its effects on emotional responding of pedestrians: Using wearable device technology with sensors capturing proximity and psychophysiological emotion responses while walking in the street

Cited by 49 publications

References 39 publications

Smart Devices and Wearable Technologies to Detect and Monitor Mental Health Conditions and Stress: A Systematic Review

Smart Devices and Wearable Technologies to Detect and Monitor Mental Health Conditions and Stress: A Systematic Review

Methodological Considerations for Furthering the Understanding of Constraints in Applied Sports

Quantifying the usage of small public spaces using deep convolutional neural network

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