SELF-CARE: Selective Fusion with Context-Aware Low-Power Edge Computing for Stress Detection

Rashid, Nafiul; Mortlock, Trier; Faruque, Mohammad Abdullah Al

doi:10.1109/dcoss54816.2022.00019

Cited by 6 publications

(4 citation statements)

References 15 publications

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“…Three different classifications relating to emotional states are present in the dataset: baseline (neutral), amusement, and stress. Baseline and amusement are combined in the non-stress class of the two-class dilemma [18] , [19] , [20] , [21] , [22] .…”

Section: Methods Detailsmentioning

confidence: 99%

Improved method for stress detection using bio-sensor technology and machine learning algorithms

Nazeer,

Salagrama,

Kumar

et al. 2024

MethodsX

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Section: Methods Detailsmentioning

confidence: 99%

Improved method for stress detection using bio-sensor technology and machine learning algorithms

Nazeer,

Salagrama,

Kumar

et al. 2024

MethodsX

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“…Moreover, the developed method should be generalizable to both chest and wrist wearable devices as the noise context varies based on the location of wearable devices. Prior work has shown that stress detection using wrist-based wearable devices can be improved by modeling noise context [20], however, the differences in using chest-based wearable devices have yet to be examined.…”

Section: B Motivationmentioning

confidence: 99%

Stress Detection Using Context-Aware Sensor Fusion From Wearable Devices

Rashid¹,

Mortlock²,

Faruque³

2023

IEEE Internet Things J.

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Wearable medical technology has become increasingly popular in recent years. One function of wearable health devices is stress detection, which relies on sensor inputs to determine a patients mental state. This continuous, real-time monitoring can provide healthcare professionals with vital physiological data and enhance the quality of patient care. Current methods of stress detection lack: (i) robustness-wearable health sensors contain high levels of measurement noise that degrades performance, and (ii) adaptation-static architectures fail to adapt to changing contexts in sensing conditions. We propose to address these deficiencies with SELF-CARE, a generalized selective sensor fusion method of stress detection that employs novel techniques of context identification and ensemble machine learning. SELF-CARE uses a learning-based classifier to process sensor features and model the environmental variations in sensing conditions known as the noise context. SELF-CARE uses noise context to selectively fuse different sensor combinations across an ensemble of models to perform robust stress classification. Our findings suggest that for wrist-worn devices, sensors that measure motion are most suitable to understand noise context, while for chest-worn devices, the most suitable sensors are those that detect muscle contraction. We demonstrate SELF-CAREs state-of-the-art performance on the WESAD dataset. Using wrist-based sensors, SELF-CARE achieves 86.34% and 94.12% accuracy for the 3-class and 2class stress classification problems, respectively. For chest-based wearable sensors, SELF-CARE achieves 86.19% (3-class) and 93.68% (2-class) classification accuracy. This work demonstrates the benefits of utilizing selective, context-aware sensor fusion in mobile health sensing that can be applied broadly to Internet of Things applications.

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“…To help choose the random forest classifier as the highest accuracy model among the five applied algorithms, the F1-score metric was included. The authors of [ 47 ] suggested SELF-CARE, a wrist-based stress detection technique that uses context-aware selective sensor fusion and dynamic sensor data-driven adaptation. The proposed approach learns to change the fused sensors in the context of the system using motion, enhancing the performance while preserving energy.…”

Section: Related Workmentioning

confidence: 99%

Efficient Feature-Selection-Based Stacking Model for Stress Detection Based on Chest Electrodermal Activity

Almadhor

Sampedro

Abisado

et al. 2023

Sensors

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Contemporary advancements in wearable equipment have generated interest in continuously observing stress utilizing various physiological indicators. Early stress detection can improve healthcare by lessening the negative effects of chronic stress. Machine learning (ML) methodologies have been modified for healthcare equipment to monitor user health situations utilizing sufficient user information. Nevertheless, more data are needed to make applying Artificial Intelligence (AI) methodologies in the medical field easier. This research aimed to detect stress using a stacking model based on machine learning algorithms using chest-based features from the Wearable Stress and Affect Detection (WESAD) dataset. We converted this natural dataset into a convenient format for the suggested model by performing data visualization and preprocessing using the RESP feature and feature analysis using the Z-score, SelectKBest feature, the Synthetic Minority Over-Sampling Technique (SMOTE), and normalization. The efficiency of the proposed model was estimated regarding accuracy, precision, recall, and F1-score. The experimental outcome illustrated the efficacy of the proposed stacking technique, achieving 0.99% accuracy. The results revealed that the proposed stacking methodology performed better than traditional methodologies and previous studies.

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SELF-CARE: Selective Fusion with Context-Aware Low-Power Edge Computing for Stress Detection

Cited by 6 publications

References 15 publications

Improved method for stress detection using bio-sensor technology and machine learning algorithms

Improved method for stress detection using bio-sensor technology and machine learning algorithms

Stress Detection Using Context-Aware Sensor Fusion From Wearable Devices

Efficient Feature-Selection-Based Stacking Model for Stress Detection Based on Chest Electrodermal Activity

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