Key result
A proposed pre- and post-processing technique for PPG-based mental workload assessment achieved higher accuracy (74.2% vs 66.7%) and lower variance (10.8% vs 11.3%) than baseline methods.
Why the study?
PPG signals from smartwatches are prone to artifacts causing invalid inter-beat intervals, making reliable PPG-based heart rate variability assessment of mental workload challenging.
Absolute Event Rate: 74.2% vs 66.7%
A novel pre- and post-processing technique for PPG signals improves the accuracy and reliability of mental workload assessment using wearable devices.
Hypothesis-generating for PPG mental workload tools; should not change practice pending larger validation.
Heart rate variability (HRV) has been used in assessing mental workload (MW) level. Compared with ECG, photoplethysmogram (PPG) provides convenient in assessing MW with wearable devices, which is more suitable for daily usage. However, PPG collected by smartwatches are prone to suffer from artifacts. Those signal corruptions cause invalid Inter-beat Intervals (IBI), making it challenging to evaluate the HRV feature. Hence, the PPG-based MW assessment system is difficult to obtain a sustainable and reliable assessment of MW. In this paper, we propose a pre- and post- processing technique, called outlier removal and uncertainty estimation, respectively, to reduce the negative influences of invalid IBIs. The proposed method helps to acquire accurate HRV features and evaluate the reliability of incoming IBIs, rejecting possibly misclassified data. We verified our approach in two open datasets, which are CLAS and MAUS. Experiment results show proposed method achieved higher accuracy (66.7% v.s. 74.2%) and lower variance (11.3% v.s. 10.8%) among users, which has comparable performance to an ECG-based MW system.
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Beh et al. (2021) studied Mental workload. Pre- and post-processing technique (outlier removal and uncertainty estimation) vs. Baseline method was evaluated on Accuracy. A proposed pre- and post-processing technique for PPG-based mental workload assessment achieved higher accuracy (74.2% vs 66.7%) and lower variance (10.8% vs 11.3%) than baseline methods.
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