# Detection and Characterization of Physical Activity and Psychological Stress from Wristband Data
## 4 Discussion  
This paper presents an application for obtaining psychological and physiological states from the signals of a wearable device that can be used in everyday life--a wristband. The performance of several signal processing algorithms and ML methods were assessed. k-NN, NB, and DT performed worse than the other algorithms because these algorithms require more data. However, SVM, LD, and EL produced more accurate performance since they can work with a relatively small amount data with a low risk of overfitting. The GPR is a powerful ML model; however, it requires a large amount of data. For this reason, in our EE estimation model, it had a large estimation error. SVM, EL, and k-NN achieved similar results that were significantly better than those of the other ML techniques considered. Figure 9: EE estimation (separate data set). The signal processing algorithm denoised the wristband PPG signal and removed the artifacts to clearly show the peaks, enabling an accurate estimation of HR. Signals from wristband devices are corrupted with artifacts caused by arm and wrist motion during most physical activities. The developed algorithm improved HR estimation during PA (Figure 6). The contributions of signal processing to accuracy enhancements were evaluated by comparing processed and raw biosignals for the same implementation of various ML algorithms used in this work. Signal processing improved classification accuracies by 0.7% to 4.5%. It also significantly improved the EE estimation. Table 6 presents the improvements with the use of filtered data. During SS activities, NS was distinguished from other types of APS with a high accuracy (97.7%; Figure 8). However, during exercise, NS could not be discriminated as accurately from other types of APS (92.9-74.4%; Figure 8). The accuracy decreased because exercise and APS can affect some biosignals such as HR. Similarly, both APS and exercise increase HR. Filtering improves signal quality by smoothing the signals and reducing the amount of noise, sharp and sudden changes, and outliers. Features are extracted from both filtered and raw data. Features with filtered data represent reasonable distributions in a limited range scale with fewer outliers, which is crucial because features are used for the design of all ML algorithms. The raw BVP signal is also used for feature extraction because motion artifacts can help capture the type of PA and discriminate PA from APS. Figure 10 illustrates the improvements with filtering for various biosignals. Signal processing algorithms also improve the classification and estimation outcomes. Filtered signals performed better by up to 3% for APS classification and 18% for EE estimation (Table 6). Figure 10: Filtering and signal processing improvements for raw biosignals: (**Upper Left**) BVP signal processing improvements during TR exercise, (**Upper Right**) ST data filtering result during SB exercise, **(Lower Left)** GSR data filtering result SB exercise, and (**Lower Right**) feature extraction example comparison with and without signal processing for ACC data during SS activities. (Blue circles: improvements where there are visible abrupt changes). We analyzed the effect of each sensor modality on the classification accuracy. In this analysis, we excluded the feature variables derived from ACC, GSR, HR, BVP, and ST measurements, and built the classification models with the retained measurements only, which included the PS, APS-SS, APS-TR, and APS-SB classifications. The results showed a decrease in classification accuracy when any individual measurement variable was removed, thus demonstrating the advantage of the multisensor fusion method in improving the classification accuracy (Figure 11). The different modalities of sensing physiological variables provide various contributions to the classification accuracy. For example, the galvanic skin response measurements contribute significantly to improving the psychological stress during biking relative to the other biosignals.