Experiments in Lifelog Organisation and Retrieval at NTCIR

Gurrin, Cathal; Joho, Hideo; Hopfgartner, Frank; Zhou, Liting; Albatal, Rami; Healy, Graham; Nguyen, Duc-Tien Dang

doi:10.1007/978-981-15-5554-1_13

Cited by 3 publications

(2 citation statements)

References 29 publications

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“…This has also resulted in the creation of many large multimodal personal datasets [11] comprising of different data types (e.g, passive visual capture, mobile device context, physiological data) [8] that enables research community to develop intelligent systems to track individual's health and gain more insights of an individual's personal data such as daily-life event segmentation [9], activities of daily-living identification as an indicator in health tracking systems [10], etc. Although multiple data sources are recorded in multimodal personal datasets [12], only the combination of visual and related metadata including semantic locations, daily-life activities, date and time are employed extensively in research [10,20] while others has not yet been exploited. Typically, physiological signals are usually ignored due to the limited amount of research conducted using this type of data as well as the limitations of recording devices in terms of the granularity signal measurement.…”

Section: Introductionmentioning

confidence: 99%

An Improved Subject-Independent Stress Detection Model Applied to Consumer-grade Wearable Devices

Ninh¹,

Nguyen²,

Smyth³

et al. 2022

Preprint

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Stress is a complex issue with wide ranging physical and psychological impacts on human daily performance. Specifically, acute stress detection is becoming a valuable application in contextual human understanding. Two common approaches to training a stress detection model are subject-dependent and subject-independent training methods. Although subject-dependent training methods have proven to be the most accurate approach to build stress detection models, subject-independent models are a more practical and cost-efficient method, as they allow for the deployment of stress level detection and management systems in consumer-grade wearable devices without requiring training data for the end user. To improve the performance of subject-independent stress detection models, in this paper, we introduce a stress-related bio-signal processing pipeline with a simple neural network architecture using statistical features extracted from multimodal contextual sensing sources including Electrodermal Activity (EDA), Blood Volume Pulse (BVP), and Skin Temperature (ST) captured from a consumer-grade wearable device. Using our proposed model architecture, we compare the accuracy between stress detection models that use measures from each individual signal source, and one model employing the fusion of multiple sensor sources. Extensive experiments on the publicly available WESAD dataset demonstrate that our proposed model outperforms conventional methods as well as providing 1.63% higher mean accuracy score compared to the state-of-the-art model while maintaining a low standard deviation. Our experiments also show that combining features from multiple sources produces more accurate predictions than using only one sensor source individually.

show abstract

Section: Introductionmentioning

confidence: 99%

An Improved Subject-Independent Stress Detection Model Applied to Consumer-grade Wearable Devices

Ninh¹,

Nguyen²,

Smyth³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…This has also resulted in the creation of many large multimodal personal datasets [11] comprising of different data types (e.g, passive visual capture, mobile device context, physiological data) [8] that enables research community to develop intelligent systems to track individual's health and gain more insights of an individual's personal data such as daily-life event segmentation [9], activities of daily-living identification as an indicator in health tracking systems [10], etc. Although multiple data sources are recorded in multimodal personal datasets [12], only the combination of visual and related metadata including semantic locations, daily-life activities, date and time are employed extensively in research [10,19] while others has not yet been exploited. Typically, physiological signals are usually ignored due to the limited amount of research conducted using this type of data as well as the limitations of recording devices in terms of the granularity signal measurement.…”

Section: Introductionmentioning

confidence: 99%

An Improved Subject-Independent Stress Detection Model Applied to Consumer-grade Wearable Devices

Ninh

Nguyen

Smyth

et al. 2022

Advances and Trends in Artificial Intelligence. Theory and Practices in Artificial Intelligence

Self Cite

View full text Add to dashboard Cite

Stress is a complex issue with wide-ranging physical and psychological impacts on human daily performance. Specifically, acute stress detection is becoming a valuable application in contextual human understanding. Two common approaches to training a stress detection model are subject-dependent and subject-independent training methods. Although the subject-dependent training method is proven to be the most accurate approach to build stress detection models, subjectindependent one is a more practical and cost-efficient method, as it facilitates the deployment of stress level detection and management systems in consumer-grade wearable devices without requiring additional data for training from end-users. To improve the performance of subjectindependent stress detection models, in this paper, we introduce a stressrelated bio-signal processing pipeline with a simple neural network architecture using statistical features extracted from multimodal contextual sensing sources including Electrodermal Activity (EDA), Blood Volume Pulse (BVP), and Skin Temperature (ST) captured from a consumergrade wearable device. Using our proposed model architecture, we compare the accuracy of stress detection models that use measures from each individual signal source with the one employing the fusion of multiple sensor sources. Extensive experiments on the publicly available WESAD dataset demonstrate that our proposed model outperforms conventional methods as well as providing 1.63% higher mean accuracy score compared to the state-of-the-art model while maintaining a low standard deviation. Our experiments also show that combining features from multiple sources produces more accurate predictions than using only one sensor source individually.

show abstract