## 3 Healthcare
### Assistive Technologies for the Visually Impaired
#### 3.2.2 Real-Time Objects Detection Framework  
The system consists of two key elements: a smartphone and a personal computer (PC). By using machine learning, the framework is trained to recognise 82 objects from the common objects in context (COCO) dataset [50], such as a fork, knife, bowl, banana, and apple. Users verbally communicate with the smartphone, indicating the object that they are looking for and the smartphone transmits the object information to the PC via the network. The PC initiates the receiving video from the smartphone and while the user is moving the device around to help to locate the wanted object, the computer reads each frame, trying to identify the object that the user is looking for. Once detected by the 'You Only Look Once' (YOLO V3) [51] algorithm running on the PC, a verbal message is sent to the smartphone, saying: \"found the object of interest\", an illustration of the system shown in Figure 5.  
The efficiency of the framework here depends on the machine-learning algorithm performance. With the recent developments in computer vision and the amount of data freely available for developers, the accuracy of the detection is continuously improving. However, the challenge that we faced when implementing this framework was the computational requirement of these systems. We note that most of the users might not have powerful graphical processing units (GPU), which are necessary for optimising the speed of detecting objects in the streamed video. Therefore, we optimised its performance by controlling the number of frames per second so that it is compatible with the speed of detection. Initially, we modelled the framework as a queue: where the video frames per second (fps) represent inter-arrival rates of the queue, whereas time taken to detect the object is the service time. Using this model, we derived an expression for the optimal number of fps needed to achieve the best real-time performance. The optimisation was based on the information freshness evaluation metrics. In particular, we used the information peak age (PA) as a metric [52; 53; 54; 55], which is the maximum time taken since the generation of the preceding piece of information. The PA is determined by the inter-arrival time (fps in this case), and the service time (which is the time of detection). The PA for different inter-arrival and service times is presented in Figure 6. The inter-arrival time can be controlled by the smartphone, while the service time depends on the PC processing unit. We started with measuring the service time--how long it takes for the PC to recognise the objects from a low-resolution video input (160 \(\times\) 120)--and found that it takes 0.31 \(\pm\) 0.06 s (Figure 7a). The service time distribution was assessed, using the Fitter package [56] and we saw that most frames were processed within 0.30-0.31 s (Figure 7b). Figure 5: The framework flow of smartphone and computer tasks [49]. As shown in Figure 7, the service time has some variability but it is insignificant. Thus, we modelled the framework as an M/D/1 queue [57]. We assumed the inter-arrival rate (video frames per second) and delay time, caused by the network, to follow an exponential distribution. Using the PA expression, we optimised the fps to achieve a minimum PA of 130 ms, whereas the PA before optimisation was 1402 ms.  
Figure 6: The PA value with respect to the server utilisation, that is, the inter-arrival rate (\(\lambda\)) divided by the service rate (\(\mu\)). The achieved value is calculated for various queues. The queues differ in their inter-arrival time and service time distribution. The presented queues are M/M/1, which is a queue with both the inter-arrival rate and the service rate, following an exponential distribution. The D/D/1 queue has both rates to be deterministic and the M/D/1 queue has an exponential inter-arrival time and deterministic service time [49]. Figure 7: Measurement of the achieved service time—time to detect objects in a frame.