HealthyOffice: Mood recognition at work using smartphones and wearable sensors

Zenonos, Alexandros; Khan, Aftab; Kalogridis, Georgios; Vatsikas, Stefanos; Lewis, Tim; Sooriyabandara, Mahesh

doi:10.1109/percomw.2016.7457166

Cited by 90 publications

(72 citation statements)

References 22 publications

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“…It is mandatory to underline that these discriminant rates have been reached by combining parameters computed from different domains through advanced classifiers. Finally, there is also a number of recent works that have evaluated stress detection by using HRV in the context of long-term analysis, reporting stress detection classification rates ranging from 70 to 78% [36,[44][45][46][47].…”

Section: Discussionmentioning

confidence: 99%

Estimation of Mental Distress from Photoplethysmography

et al. 2018

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This paper introduces the design of a new wearable photoplethysmography (PPG) sensor and its assessment for mental distress estimation. In our design, a PPG sensor obtains blood volume information by means of an optical plethysmogram technique. A number of temporal, morphological and frequency markers are computed using time intervals between adjacent normal cardiac cycles to characterize pulse rate variability (PRV). In order to test the efficiency of the developed wearable for classifying distress versus calmness, the well-known International Affective Picture System has been used to induce different levels of arousal in forty-five healthy participants. The obtained results have shown that temporal features present a single discriminant power between emotional states of calm and stress, ranging from 67 to 72%. Moreover, a discriminant tree-based model is used to assess the possible underlying relationship among parameters. In this case, the combination of temporal parameters reaches 82.35% accuracy. Considering the low difficulty of metrics and methods used in this work, the algorithms are prepared to be embedded into a micro-controller device to work in real-time and in a long-term fashion.

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Section: Discussionmentioning

confidence: 99%

Estimation of Mental Distress from Photoplethysmography

et al. 2018

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“…Potentially, this gives employers opportunities to monitor the workrelated stress, mood (Setz et al, 2010;Milosevic et al, 2012;Muaremi et al, 2013;Shirouzu et al, 2015;Lavallière et al, 2016), individual and social behaviour (Kim et al, 2009;Lavallière et al, 2016) and progress (Chen and Kamara, 2011) of employees. For example, Zenonos et al (2016) uses wearable fitness and activity monitoring sensors in conjunction with external devices (i.e. smartphones) with associated applications (i.e.…”

Section: Monitoringmentioning

confidence: 99%

“…A significant benefit of wearable technology involves actively monitoring employees and having access to the data collected by those devices (Kritzler et al, 2015). With the collected data, employers can understand the general feeling of the work environment at any given time without explicitly asking any employees (Zenonos et al, 2016); encourage employees to be more active in their day-to-day life by generating personalised recommendations/prescriptions, utilising gamification or encouraging various well-being incentive programmes (Singh et al, 2015); and predict the health issues of employees and take active steps toward assisting them via specialised prevention programmes (Nikayin et al, 2014).…”

Section: Wearable Device Revolutionmentioning

confidence: 99%

Tapping into the wearable device revolution in the work environment: a systematic review

Khakurel

Melkas

Porras

2018

ITP

113

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Purpose The purpose of this paper is to expand current knowledge about the recent trend of wearable technology to assess both its potential in the work environment and the challenges concerning the utilisation of wearables in the workplace. Design/methodology/approach After establishing exclusion and inclusion criteria, an independent systematic search of the ACM Digital Library, IEEE Xplore, ScienceDirect and Web of Science databases for relevant studies was performed. Out of a total of 359 articles, 34 met the selection criteria. Findings This review identifies 23 categories of wearable devices. Further categorisation of the devices based on their utilisation shows they can be used in the work environment for activities including monitoring, augmenting, assisting, delivering and tracking. The review reveals that wearable technology has the potential to increase work efficiency among employees, improve workers’ physical well-being and reduce work-related injuries. However, the review also reveals that technological, social, policy and economic challenges related to the use of wearable devices remain. Research limitations/implications Many studies have investigated the benefits of wearable devices for personal use, but information about the use of wearables in the work environment is limited. Further research is required in the fields of technology, social challenges, organisation strategies, policies and economics to enhance the adoption rate of wearable devices in work environments. Originality/value Previous studies indicate that occupational stress and injuries are detrimental to employees’ health; this paper analyses the use of wearable devices as an intervention method to monitor or prevent these problems. Introducing a categorisation framework during implementation may help identify which types of device categories are suitable and could be beneficial for specific utilisation purposes, facilitating the adoption of wearable devices in the workplace.

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“…Among the most typical application fields are medical applications like smoking detection [67], sleep detection [68], or affect recognition [69] and the large field of activity recognition [70,71]. While earlier work has focused on collecting labels from diaries filled out by study participants, smartphone apps have taken over the field of human annotation [72][73][74][75]. The main advantage of collecting labels via smart phones is timely labeling triggered by events (e.g., from sensor data) paired with visualization of context data in order to give the user a sensible amount of information during annotation.…”

Section: Label Comparison Without Knowing a Ground Truthmentioning

confidence: 99%

Collecting Labels for Rare Anomalies via Direct Human Feedback—An Industrial Application Study

2019

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Many systems rely on the expertise from human operators, who have acquired their knowledge through practical experience over the course of many years. For the detection of anomalies in industrial settings, sensor units have been introduced to predict and classify such anomalous events, but these critically rely on annotated data for training. Lengthy data collection campaigns are needed, which tend to be combined with domain expert annotations of the data afterwards, resulting in costly and slow process. This work presents an alternative by studying live annotation of rare anomalous events in sensor streams in a real-world manufacturing setting by experienced human operators that can also observe the machinery itself. A prototype for visualization and in situ annotation of sensor signals is developed with embedded unsupervised anomaly detection algorithms to propose signals for annotation and which allows the operators to give feedback on the detection and classify anomalous events. This prototype allowed assembling a corpus of several weeks of sensor data measured in a real manufacturing surrounding and was annotated by domain experts as an evaluation basis for this study. The evaluation of live annotations reveals high user motivation after getting accustomed to the labeling prototype. After this initial period, clear anomalies with characteristic signal patterns are detected reliably in visualized envelope signals. More subtle signal deviations were less likely to be confirmed an anomaly due to either an insufficient visibility in envelope signals or the absence of characteristic signal patterns.

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HealthyOffice: Mood recognition at work using smartphones and wearable sensors

Cited by 90 publications

References 22 publications

Estimation of Mental Distress from Photoplethysmography

Estimation of Mental Distress from Photoplethysmography

Tapping into the wearable device revolution in the work environment: a systematic review

Collecting Labels for Rare Anomalies via Direct Human Feedback—An Industrial Application Study

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