# A Survey of Photoplethysmography and Imaging Photoplethysmography Quality Assessment Methods
## 5 Design of SQI for iPPG
### Potential Developments in This Domain  
The creation of SQI for the measurement of iPPG comes with new challenges. As mentioned above, iPPG presents significant potential for telemedicine-based applications. However, to democratize this technology, its measurement must be reliable despite the high diversity of uncontrolled measurement contexts and perturbations (see Table 1). To overcome these difficulties, the implementation of an SQI is a solution to maintain the performance of these applications.  
In this regard, the use of a camera can be regarded as an opportunity in the design of an innovative SQI. This sensor allows one to extract information on the context of measurement and offers the possibility to design a more complete SQI. The exploitation of contextual information could allow one to distinguish perturbation factors linked to the metrological and physiological aspects of the quality of the signal. For example, [PERSON] et al. [108] created a signal quality index for iPPG by first designing indices for the quality of the context of measurement and then for the quality of the signal extracted. Within the quality of the measurement context, the authors considered the environment of the measurement by computing indices related to the intensity of the light source, the light spectrum and direction, the type of light source (punctual or parallel source of light), and the quantity of skin exposed to the measurement. The authors also considered a physiological factor, the skin tone, in the study of their SQI. The use of camera also allows one to pass to a spatio-temporal notion of quality. Indeed, the quality of an iPPG signal extracted may be uneven over the different skin regions [109] and may also change with time. Only a few  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **Data** & **Algorithm Type** & **Performance** \\ \hline
[100] & 31 video records & Rule-based algorithm & Evaluated on contribution over HR estimation \\ \hline
[61] & UBFC-RPPG dataset [105] & Hidden Markov Model & Evaluated on contribution over HR estimation \\ \hline
[104] & 200,000 smartphone PPG records & Rule-based algorithm & - \\ \hline
[36] &
\begin{tabular}{c} 5 different datasets \\ (Chon Lab and \\ UMass Medical Center) \\ \end{tabular} & SVM & Evaluated on contribution over HR estimation \\ \hline
[106] & 226 subjects & Fitting each pulse to a sinusoidal model using non-linear least square optimization. If the fitting & Evaluated on impact on error between HRV estimated from PPG and ECG \\
[106] & & falls (i.e., no convergence or error high) or the model & \\  & & parameters are outside a statistical range, the pulse is & \\  & & bad-quality. & \\ \hline
[101] &
\begin{tabular}{c} Bingamton–Pittsburgh–RPI \\ Multimodal Spontaneous \\ Emotion database [107] \\ \end{tabular} & Rule-based algorithm & Evaluated on contribution over HR estimation \\ \hline
[41] & \
\begin{tabular}{c} 6 subjects \\ + augmentation with PPG \\ DaLia dataset \\ \end{tabular} &
\begin{tabular}{c} 3-unit LSTM + dense layer \\  over HR estimation \\ \end{tabular} \\ \hline HR = Heart Rate; LSTM = Long Short-Term Memory, SVM = Support Vector Machine. \\ \hline \hline \end{tabular}
\end{table}
Table 9: Papers dedicated to signal quality indices for iPPG.  
papers [61; 98] take into account the spatial dimension of the quality. Thus, the field of SQI applied to iPPG has room for improvement and could be a sandbox for new signal quality algorithms.