A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Smartwatch-Based Human Stress Diagnosis Utilizing Physiological Signals and LSTM-Driven Machine Intelligence
Stress responses, primarily the ‘fight-or-flight’ reactions, are fundamental to human survival but can become harmful when they persist for extended periods. Prolonged stress exposure has been linked to various health issues and accelerated aging. But this can be addressed or controlled if a person becomes aware of their stress condition early. Traditional methods of identifying human stress relied heavily on self-reported questionnaires, but with the advancements in machine intelligence utilizing Machine Learning (ML) and Deep Learning (DL) techniques, the use of smart wearables like smartwatches for diagnosing stress has gained prominence. These devices can collect real-time physiological data, e.g., heart rate, skin conductance, and movement patterns, which are crucial for detecting stress. In this study, we utilized the ‘Stress-Predict Dataset’ built from the Empatica E4 wearable smartwatch device. After processing raw signals collected from 35 participants and aligning them to a uniform frequency of 32 Hz, we developed a Long Short-Term Memory (LSTM) model for stress classification. Training and validation sets achieved accuracies of 93.67 % and 91.13 %, respectively. The test set achieved an accuracy of 91.78 %, indicating a strong ability to identify stress events. This includes correctly classifying 30.31 % of True Positive (TP) stress occurrences with a low rate of False Negatives (FN) (missed stress events) at 4.56%. There were also 61.47% of True Negative (TN) classifications, demonstrating the model's ability to correctly identify non-stressful states with a low rate of False Positives (FN) (incorrectly classified non-stress events) at 3.66%. This research contributes to the ongoing exploration of stress diagnosis using wearable smartwatch device data, underlining the potential for further advancements in physiological stress monitoring.
Smartwatch-Based Human Stress Diagnosis Utilizing Physiological Signals and LSTM-Driven Machine Intelligence
Stress responses, primarily the ‘fight-or-flight’ reactions, are fundamental to human survival but can become harmful when they persist for extended periods. Prolonged stress exposure has been linked to various health issues and accelerated aging. But this can be addressed or controlled if a person becomes aware of their stress condition early. Traditional methods of identifying human stress relied heavily on self-reported questionnaires, but with the advancements in machine intelligence utilizing Machine Learning (ML) and Deep Learning (DL) techniques, the use of smart wearables like smartwatches for diagnosing stress has gained prominence. These devices can collect real-time physiological data, e.g., heart rate, skin conductance, and movement patterns, which are crucial for detecting stress. In this study, we utilized the ‘Stress-Predict Dataset’ built from the Empatica E4 wearable smartwatch device. After processing raw signals collected from 35 participants and aligning them to a uniform frequency of 32 Hz, we developed a Long Short-Term Memory (LSTM) model for stress classification. Training and validation sets achieved accuracies of 93.67 % and 91.13 %, respectively. The test set achieved an accuracy of 91.78 %, indicating a strong ability to identify stress events. This includes correctly classifying 30.31 % of True Positive (TP) stress occurrences with a low rate of False Negatives (FN) (missed stress events) at 4.56%. There were also 61.47% of True Negative (TN) classifications, demonstrating the model's ability to correctly identify non-stressful states with a low rate of False Positives (FN) (incorrectly classified non-stress events) at 3.66%. This research contributes to the ongoing exploration of stress diagnosis using wearable smartwatch device data, underlining the potential for further advancements in physiological stress monitoring.
Smartwatch-Based Human Stress Diagnosis Utilizing Physiological Signals and LSTM-Driven Machine Intelligence
Halder, Nabarun (author) / Setu, Jahanggir Hossain (author) / Rafid, Lishan (author) / Islam, Ashraful (author) / Amin, M Ashraful (author)
2024-06-03
779336 byte
Conference paper
Electronic Resource
English
Snowmelt-Driven Streamflow Prediction Using Machine Learning Techniques (LSTM, NARX, GPR, and SVR)
| DOAJ | 2020