## 3 System Overview
### OCC Channel Model and Data Retrieval Technique  
The non-line-of-sight (NLOS) component of the optical signal minimally affects the OCC. The effect is almost negligible when the LED is very small. The data reception can be modeled by the Lambertian radiant intensity [26; 56]. The LOS channel model of OCC is depicted in Figure 3. The LED light source and camera are placed in \(\alpha\) and \(\beta\), respectively. The DC LOS channel gain can be represented as follows:  
\[\mathrm{G}_{\alpha,\beta}=\left\{ \begin{array}{ll}\mathrm{g}_{\mathrm{op}} \cos(\psi_{\mathrm{in}})\cos^{\mathrm{m}}(\psi_{\mathrm{ir}})\frac{\mathrm{A }_{\mathrm{c}}(\mathrm{m}_{\mathrm{i}}+1)}{2\pi\mathrm{d}_{\alpha,\beta}^{2}}& \text{,for }\psi_{\mathrm{in}}<\partial_{\mathrm{AOV}}\\ 0&\text{,for }\psi_{\mathrm{in}}\geq\partial_{\mathrm{AOV}}\end{array} \right. \tag{1}\]  
where \(\mathrm{d}_{\alpha,\beta}\) denotes the Euclidean distance between \(\alpha\) and \(\beta\), \(\psi_{\mathrm{ir}}\) indicates the angle of irradiance of the LED, \(\mathrm{m}_{\mathrm{i}}\) signifies the Lambertian emission index (which is a function of the half-intensity radiation angle \(\psi_{1/2}\) and formulated as \(\mathrm{m}_{\mathrm{i}}=-\log_{\cos\psi_{1/2}}(2)\)), \(\mathrm{g}_{\mathrm{op}}\) denotes the gain of the optical filter, \(\psi_{\mathrm{in}}\) is the angle of incidence, and \(\mathrm{A}_{\mathrm{c}}\) is the area of a projected image on the image sensor. If the pixel edge length is \(\rho\), \(\mathrm{A}_{\mathrm{c}}\) can be expressed as  
\[\mathrm{A}_{\mathrm{c}}=\frac{\mathrm{A}_{\mathrm{i}}\mathrm{f}_{0}^{2}}{\rho^ {2}\mathrm{d}_{\alpha,\beta}^{2}} \tag{2}\]  
where \(\mathrm{A}_{\mathrm{i}}\) denotes the entire area of the LED that is active to send the optical signals, \(\mathrm{f}_{0}\) is the focal length of the camera, and \(\partial_{\mathrm{AOV}}\) indicates the AOV of the camera. OCC is less affected by interferences generated from neighboring light sources owing to the nature of the image sensor. The interfering elements can be spatially separated by applying region of interest (ROI) techniques. Thus, OCC offers excellent SINR, which is represented as follows:  
\[\mathrm{\eta}(\mathrm{d}_{\alpha,\beta})=\frac{\left(\mathrm{vP_{t}G_{\alpha, \beta}}\right)^{2}}{\sum\limits_{i=0}^{\mathrm{N}}\left(\mathrm{vP_{t}G_{i, \beta}}\right)^{2}+\mathrm{N}_{0}\mathrm{f}_{\mathrm{r}}} \tag{3}\]  
where \(\mathrm{P_{t}}\) denotes the transmitted optical power, \(\mathrm{v}\) is the optical-to-electrical conversion efficiency, \(\mathrm{N}_{0}\) is the noise spectral density, and \(\mathrm{f}_{\mathrm{r}}\) is the camera sampling rate. In addition, \(\mathrm{N}\) is the total number of neighboring light sources and \(\mathrm{G}_{i,\beta}\) is the dc gain of a specific interfering light source. Figure 2: Topology of the proposed health monitoring system. The LED projected image in the camera is analyzed frame by frame to retrieve the actual data. When the LED is activated, it can be detected by the camera. Computer vision techniques have also been developed recently for object detection [57; 58]. We employ a convolutional neural network (CNN) to detect the ROI using the camera. First, the camera captures a series of image frames. Then, a pre-trained CNN is applied to detect the actual ROI to reduce the complexity. Considering that the size of the LED and the transmitted optical signal power are very small, applying CNN is effective and achieves less detection error. The image frames are examined by converting the pixels into grayscale. Subsequently, a certain threshold is set, and the images are binarized, resulting in the appearance of bright pixels that only contain the LED image. Because we use cameras with rolling shutters, the LED image will appear as dark and bright strips because of the \"on\" and \"off\" states of the LED. The width of the strips is the function of the LED modulation frequency and the read-out architecture of the camera. By analyzing the width and the number of strips, the data are extracted as binary bits.