Key result
Smartphone photoplethysmography achieves ~75% accuracy in stress detection, slightly trailing wearable devices.
Why the study?
Stress is highly correlated with pulse rate variability and can lead to health complications like heart disease, but stress detection performance using smartphone-extracted PPG compared to wearables needed evaluation.
Can smartphone-extracted photoplethysmography (PPG) accurately detect stress compared to wearable devices?
Observational (n=30)
Can smartphone-extracted photoplethysmography (PPG) accurately detect stress compared to wearable devices?
Absolute Event Rate: 74.51% vs 77.65%
Smartphone-extracted PPG combined with machine learning can detect stress with comparable accuracy to wearable devices.
Smartphone PPG may enable accessible stress monitoring; leaves open validation for clinical reliability in larger cohorts.
Stress is present in our daily live and can affect anyone. It can be induced by many factors and can lead to many health complications such as depression, anxiety and even heart disease. Multiple research found that stress is highly correlated with physiological signals especially pulse rate variability (PRV). In this paper, we generated multiple machine learning models which uses PRV features extracted via photoplethysmography (PPG). Since multiple studies proved PPG can be performed by smartphones, we further compared the stress detection performance for wearables and smartphone extracted data. Moreover, principal component analysis (PCA) and three different time length were observed to check how it will affect the performance of different models. Results from 30 participant shows that Random Forest algorithm achieved the highest classification accuracy for both smartphone and wearable device. An accuracy of 77.65% and 74.51% was achieved for the wearable and smartphone respectively.
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Panganiban et al. (2021) conducted an observational in Stress (n=30). Smartphone extracted photoplethysmography (PPG) vs. Wearable device extracted PPG was evaluated on Classification accuracy for stress detection. Stress detection using a Random Forest algorithm on smartphone-extracted photoplethysmography achieved an accuracy of 74.51%, compared to 77.65% for wearable devices.
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