# The Impact of Contact Force on Signal Quality Indices in Photoplethysmography Measurements
## 2 Related Work  
The SQI is essential for evaluating PPG signal quality, distinguishing between high-quality signals and those affected by noise or motion artifacts. This evaluation is beneficial before analyzing the physiological parameters [36; 37]. Errors in the PPG signal waveform can increase false alarms and analysis errors, potentially leading to misdiagnoses, such as incorrect changes in the detected HR. These indices, presented as numerical values, percentages, or qualitative categories (good, acceptable, or poor), are derived from algorithms that evaluate stability, coherence, and the presence of artifacts or noise in the signal [38; 39]. Accuracy is especially important in real-time monitoring devices, which provide alerts regarding changes in vital signs [40; 41]. [PERSON] et al. [42] classify signal quality assessment into two categories based on the quality-related feature extraction and decision-making of the quality by the particular features. The first category reflects the signal characteristics that can help to determine certain signal artifacts. [PERSON] [39] examines various quality-related feature extraction SQIs, including PI, skewness, kurtosis, entropy, zero-crossing rate, signal-to-noise ratio (SNR), relative power, and matching systolic detectors. His research concludes that skewness emerges as the optimal SQI, offering significant potential to enhance the diagnostics and detect conditions such as hypertension and vascular age. Similarly, [PERSON] et al. [43] differentiate signal quality by determining whether some statistical features, such as kurtosis and Shannon entropy, are in the acceptable range. [PERSON] et al. [44] use time-frequency spectral features to detect motion and noise artifact-corrupted data. The second category involves determining the signal quality based on the extracted features. This process can be approached as a classification problem and is commonly divided into rule-based and data-driven methods. Rule-based approaches utilize extracted features and conditional statements to assess the signal quality. For example, a rule-based system might determine a signal as high-quality if its amplitude exceeds a certain threshold and its skewness and kurtosis fall within predefined ranges. Another example could involve using the zero-crossing rate to identify the noise and setting rules that classify signals with high zero-crossing rates as poor-quality due to increased noise levels. [PERSON] [45] emphasizes the importance of feature extraction and establishing decision rules for designing signal quality assessment. Her work categorizes feature extraction into time and frequency domain features, such as feasibility checks, morphological features, and kurtosis of the Fourier spectrum. In contrast, data-driven approaches leverage machine learning (ML) techniques, introducing significant computational complexity. Techniques such as deep learning models, including convolutional neural networks (CNNs) [46; 47; 48] and recurrent neural networks (RNNs) [49; 50; 51], require substantial computational resources for both training and inference. This complexity arises from the need to process large datasets, perform feature extraction, and optimize the model parameters to classify the signal quality accurately. While advanced ML models offer high accuracy, their effective deployment demands considerable computational power and expertise. [PERSON] [52] develop an atrial fibrillation screening system using ML on PPG signals, incorporating two specific SQIs: ACC-SQI for motion artifacts' accelerometer data and Relative Magnitude Frequency Component (RMFC-SQI) for noise management. [PERSON] et al. [53; 54] propose algorithms for signal segmentation and morphological analysis using Dynamic Time Warping (DTW) with barycenter averaging to create robust templates that account for the physiological variability in sinus rhythm signals. The studies in this field employ diverse methodologies regarding data acquisition, annotation, processing, and study scope.