Physiological data analysis for an emotional provoking exergame

Muller, Larissa; Bernin, Arne; Ghose, Sobin; Gozdzielewski, Wojtek; Wang, Qi; Grecos, Christos; Luck, Kai von; Vogt, Florian

doi:10.1109/ssci.2016.7850042

Cited by 14 publications

(11 citation statements)

References 26 publications

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“…There are examples in research where the use of multiple devices for data acquisition is necessary. For example, in the affective computing (AC) field some features must be obtained from several signals [29] like EEG, electrocardiogram, skin temperature, electrodermal activity [8], facial expressions [19], speech audio [35], etc., and using an adhoc user interface for eliciting emotions. Only ORE and LR would allow multiple devices operating at the same time but their use for a study like AC, due to their restrictions, would be very difficult to implement.…”

Section: ) Openvibementioning

confidence: 99%

An Easy-to-Use Multi-Source Recording and Synchronization Software for Experimental Trials

et al. 2020

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A typical routine in many scientific studies consists of recording data from devices and identifying which segment of data corresponds with an experimental interval. However, many current applications have been designed to obtain and save data from one single device, and synchronizing data with the markers that delimit the test phases can be difficult. To address this issue, we have developed LSRec, which is based on Lab-Streaming Layer, a C++ library that allows data synchronization. LSRec is an easy-to-use, open-source, multi-platform, recording system developed on Java that can save data from several devices at the same time, while maintaining synchronization with the experimental phase markers. It supports three explicit sync methods: the first uses one-integer-channel input Lab-Streaming Layer streams. The others use TCP/UDP socket messages and allow any existing software that generates sync markers through TCP/UDP messages to be used under the same conditions. In LSRec, the markers are saved together with input data, facilitating the process of linking data with test stages. A prerequisite for recording software is to guarantee suitable timing performance, with no data loss and an easy user interface. These features were assessed for LSRec, with no data loss detected for 20 hours (25 sessions). We evaluated performance by measuring timestamp deviations for sync marker and input data. The sync methods exhibited an average of deviations of around −34µs (which is more than acceptable, e.g., in studies involving living beings), whereas the absolute value of deviation for one-channel data was lower than 40µs. Finally, we assessed the system's usability with the technology acceptance model 3 survey (6 volunteers). Subjects saw the software as useful, and intended to use it in the future. In conclusion, LSRec is a useful tool for those who need to record data from multiple sources, and maintain synchronization with experimental phases with low delay.

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Section: ) Openvibementioning

confidence: 99%

An Easy-to-Use Multi-Source Recording and Synchronization Software for Experimental Trials

et al. 2020

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“…It is a variant of a cockpit scenario also suitable for research on the topic of games for physical therapy and orthopaedic rehabilitation. Our follow-up study [23] enhanced the experimental setup with the event-based analysis of galvanic skin response combined with facial expressions. For practical guidance, we presented a benchmark [3] of four automatic facial expression analysis systems with three emotion-labelled reference databases and a systematic method for performance analysis and improvement that allows to tailor for specific application needs.…”

Section: Previous Workmentioning

confidence: 99%

Automatic Classification and Shift Detection of Facial Expressions in Event-Aware Smart Environments

Bernin

Muller

Ghose

et al. 2018

Proceedings of the 11th PErvasive Technologies Related to Assistive Environments Conference

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Affective application developers often face a challenge in integrating the output of facial expression recognition (FER) software in interactive systems: although many algorithms have been proposed for FER, integrating the results of these algorithms into applications remains difficult. Due to interand within-subject variations further post-processing is needed. Our work addresses this problem by introducing and comparing three post-processing classification algorithms for FER output applied to an event-based interaction scheme to pinpoint the affective context within a time window. Our comparison is based on earlier published experiments with an interactive cycling simulation in which participants were provoked with game elements and their facial expression responses were analysed by all three algorithms with a human observer as reference. The three post-processing algorithms we investigate are mean fixed-window, matched filter, and Bayesian changepoint detection. In addition, we introduce a novel method for detecting fast transition of facial expressions, which we call emotional shift. The proposed detection pattern is suitable for affective applications especially in smart environments, wherever users' reactions can be tied to events.

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“…It has been shown, that interactive games enhance the experience of general exercises [6] and moreover Lathia et al [7] linked physical activity to happiness. In our prior works we introduced a physical cycling emotion provoking exergame, the EmotionBike Hamburg [3], [4]. In this work the cycling exergame controller was enhanced by a gear shift in Proceedings: IEEE Symposium Series on Computational Intelligence (SSCI), 2017…”

Section: Related Workmentioning

confidence: 99%

“…Furthermore stress can be induced for instance by negative feedback in game scenarios and measured by baseline differences in the EDA data [10], but the results are controversial [11]. Results of our previous works [4] have shown that an event-based analysis method of EDA data is an alternative approach and applying an activity peak detection is appropriate for the sensor data focusing on the physic component of the signal. Our experimental design involves the presence of an observer, who is introduced as a judge to provoke a social-evaluative threat.…”

Section: Related Workmentioning

confidence: 99%

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Enhancing exercise experience with individual multi-emotion provoking game elements

Muller

Bernin

Luck

et al. 2017

2017 IEEE Symposium Series on Computational Intelligence (SSCI)

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In this work we present a new affective game design method that includes multi-emotion provoking and continuous game elements. Evaluated with our physical cycling exergaming system it extends our previously designed singleemotion game scene approach. In addition we introduce new entertaining content that includes game elements to provoke both physical and mental stress as well as emotions with a controlled intensity to provide an exciting but not taxing exercise. Our previously developed analysis method for facial expressions and physiological data has been enhanced by a near real-time emotion evaluation, to allow the system to adapt the gameplay dynamically and in this work new multi-emotion provoking game elements are introduced. A case study with 25 participants have been conducted to showcase and evaluate the enhancing exercise experience by individual multi-emotion provoking game elements.

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Physiological data analysis for an emotional provoking exergame

Cited by 14 publications

References 26 publications

An Easy-to-Use Multi-Source Recording and Synchronization Software for Experimental Trials

An Easy-to-Use Multi-Source Recording and Synchronization Software for Experimental Trials

Automatic Classification and Shift Detection of Facial Expressions in Event-Aware Smart Environments

Enhancing exercise experience with individual multi-emotion provoking game elements

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