# A Telepresence Wheelchair with 360-Degree Vision Using WebRTC
## 2 Methods
### QoE Estimation of Wireless Video Streaming Using Neural Networks  
In a wireless network-controlled telepresence system, the video transmission quality relies highly on the wireless network condition. Notably, the wireless signal fluctuations are affected by many factors and are difficult to forecast while the telepresence wheelchair is moving. The movement of the wheelchair into a weak wireless signal area can cause poor video streaming performance. Prediction of the quality of experience (QoE) is vital for a telepresence wheelchair in the short-term to react in time. However, the prediction of QoE while the wireless signal is not efficient to prevent loss of signal is a challenge. Thus, it is highly desirable to be able to predict the quality of service and, then, to properly control the wheelchair towards a wireless coverage area with strong signals. The quality of experience evaluation of video can be either subjective or objective. However, subjective quality assessment methods for video are costly and time-consuming. Thus, objective evaluation is highly desirable to overcome these limitations by providing a mathematical calculation for the estimation of quality. Based on the availability of the original video signal, objective video quality assessment methods are categorized as full reference (intrusive) methods and free reference (non-intrusive) methods. Intrusive prediction models require access to the source, for example, peak signal to noise ratio (PSNR), structural similarity index (SSIM) whereas non-intrusive models do not and, hence, are more appropriate for real-time applications. From the literature, there are several other artificial intelligent techniques (machine learning-based) used to measure video quality, such as random neural networks, fuzzy systems, and artificial neural networks [18]. However, very little work has been done on predicting video QoE, considering the impacts of the fluctuations of wireless network conditions. Therefore, in this section, we develop a model based on artificial neural networks which are applied to predict the subjective quality metric of mean opinion score (MOS) for QoE estimation of video streaming using WebRTC over wireless networks. This section aims to present an estimation model based on neural networks for the objective, non-intrusive prediction of video quality over wireless streaming using WebRTC for video applications. To find an effective method to deal with uncertainty in the quality of service, we introduce an artificial neural network-based quality estimation model in streaming media applications that can estimate the quality of the video. Accurate QoE estimation of the system is necessary for the remote user to control the wheelchair towards reliable wireless signal coverages to maximize the QoE to meet the needs of real-time communication. The accuracy improvement of QoE prediction is a significant first step towards a more optimized feedback system that can efficiently control the system while moving the wheelchair into weak wireless signal coverage in the short-term. In this section, we introduce the QoE estimation based on the Neural Networks (NNs), as shown in Figure 4. By exploiting the NNs, the measuring of the quality of the video can be done without accessing the original videos. After the NN is trained, the video quality evaluation can be performed in real-time. This model uses a combination of objective parameters in the application and network layers, such as sender bit rate (SBR), jitter, packet loss rate (PLR), and round-trip time (RTT). The video quality was predicted in terms of the mean opinion score (MOS). With the aim of estimation of the video quality of experience, a neural network with a feed-forward three-layer topology is developed as shown in Figure 5.  
There are two layers in the network, one hidden layer and one output layer. The input includes values of sender bit rate (SBR), jitter, packet loss rate (PLR), and round-trip time (RTT), and the output layer is the estimated QoE in terms of mean opinion score (MOS).