Stress and sleep quality estimation with the use of smart Wearable sensors
Stress and sleep quality estimation with the use of smart
Wearable sensors
1
Harsha Puranik
Harshpuranik44@gmail.com
1
2
S.S. Kataria nileshpatni@indiatimes.com Institute Of Knowledge College Of Engineering, Pune
University of Pune
2
Amrutvahini College of Engineering, Sangamner
University of Pune
Abstract: The stress and poor sleep quality of a person may be used as two of several components for predicting the onset of mental health problems, in particular depression.
Continuous stress monitoring may help users better understand their stress patterns and provide physicians with more reliable data for interventions. Previously, studies on mental stress detection were limited to a laboratory environment where participants generally rested in a sedentary position. However, it is impractical to exclude the effects of physical activity while developing a pervasive stress monitoring application for everyday use. The physiological responses caused by mental stress can be masked by variations due to physical activity.We present an activity-aware mental stress detection scheme.
Electrocardiogram (ECG), galvanic skin response (GSR), and accelerometer data were gathered from 20 participants across three activities: sitting, standing, and walking. For each activity, we gathered baseline physiological measurements and measurements while users were subjected to mental stressors. The activity information derived from the accelerometer enabled us to achieve 92.4% accuracy of mental stress classification for 10-fold cross validation and
80.9%
accuracy for between-subjects classification.Ergonomic smart sensors that can determine the heart rate variations related
to stress and the variability of sleep may provide unique insights to the coping behavior of stressed people. Rather than relying on wearable computers, a single smart miniature sensor that is worn 24/7 should perform the complex embedded recognition
Wearable sensors
1
Harsha Puranik
Harshpuranik44@gmail.com
1
2
S.S. Kataria nileshpatni@indiatimes.com Institute Of Knowledge College Of Engineering, Pune
University of Pune
2
Amrutvahini College of Engineering, Sangamner
University of Pune
Abstract: The stress and poor sleep quality of a person may be used as two of several components for predicting the onset of mental health problems, in particular depression.
Continuous stress monitoring may help users better understand their stress patterns and provide physicians with more reliable data for interventions. Previously, studies on mental stress detection were limited to a laboratory environment where participants generally rested in a sedentary position. However, it is impractical to exclude the effects of physical activity while developing a pervasive stress monitoring application for everyday use. The physiological responses caused by mental stress can be masked by variations due to physical activity.We present an activity-aware mental stress detection scheme.
Electrocardiogram (ECG), galvanic skin response (GSR), and accelerometer data were gathered from 20 participants across three activities: sitting, standing, and walking. For each activity, we gathered baseline physiological measurements and measurements while users were subjected to mental stressors. The activity information derived from the accelerometer enabled us to achieve 92.4% accuracy of mental stress classification for 10-fold cross validation and
80.9%
accuracy for between-subjects classification.Ergonomic smart sensors that can determine the heart rate variations related
to stress and the variability of sleep may provide unique insights to the coping behavior of stressed people. Rather than relying on wearable computers, a single smart miniature sensor that is worn 24/7 should perform the complex embedded recognition
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