Key result
Low-power flexible ECG wearable achieves 89% accuracy in classifying tasks among healthy volunteers.
Why the study?
Mental illnesses induce physiological, cardiac, and autonomic changes that are difficult to examine in controlled settings, motivating wearable devices to assess these parameters in natural living conditions.
A novel flexible wearable device accurately measures multimodal physiological signals and classifies physical and mental tasks with 89% accuracy in healthy volunteers.
Answers are grounded in this paper and cite their sources.
Hypothesis-generating for at-home mental health monitoring; leaves open validation in clinical populations.
Mental illnesses are vast and cause a lot of individual and social discomfort, with significant healthcare costs associated in terms of diagnosis and treatment. They can be triggered by a number of factors including stress, fatigue or anxiety. The associated physiological, cardiac and autonomic changes can be assessed, centrally, through brain imaging or, peripherally, by other signal recording modalities. With recent advances in wearable devices, these parameters can now be assessed in natural living conditions as associated mood disorders such as obsessive/compulsive behavior or depression are difficult to be examined in controlled settings. In this paper, we present a low-powered and flexible device with electrocardiogram (ECG), galvanic skin response (GSR), temperature and bio-motion detection channels, with signal accuracies of 62 μV for ECG, 6.6 kΩ for GSR, 0.13 °C for temperature and 0.04 g for acceleration. Potential applications include mental health assessment of patients during daily activities at home and/or work through non-continuous and multimodal sensing as demonstrated in this paper during exercise, rest and mental activities performed by healthy individuals only, achieving an overall accuracy of 89% in the classification of the different tasks executed by volunteers.
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Rosa et al. (2019) studied Mental health assessment. Flexible wearable device (ECG, GSR, temperature, bio-motion) was evaluated on Classification accuracy of different tasks executed by volunteers. A low-powered, flexible wearable device measuring ECG, galvanic skin response, temperature, and bio-motion achieved an overall accuracy of 89% in classifying tasks executed by healthy volunteers.
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