## 4 Discussion and Conclusions  
This research aims to design a smart monitoring system to detect a waterborne pathogen cell using microfluidic technology and a cost-effective microscopic camera. The sample was driven through the designed microfluidic channel at a selected flow rate on a micro-pump. The pathogen movement was tracked by a high-quality camera at a perspective angle. With repeated experiments and alterations of  
Figure 8: Gradual increase of velocity for the microsphere travelling within the microfluidic channel with respect of flow rate increase. the input flow rate, the output is hypothesized to find a consistent 'track line' that the pathogen travels within the channel, so a separation channel can be fabricated to separate the pathogen from the sample. The prominent challenges for the pathogen detection methods in practice are: (i) physical characteristics of different groups of pathogens, (ii) large volume of water containing low concentration of target pathogens and (iii) sample preparation protocols. The key advantages for the results delivered by this new method allows sample consumption to be decreased, the reduction of testing time and reducing the use of expensive equipment, providing an attractive cost. This will benefit existing methods such as Flow Cytometry and EPA Cryptosporidium detection approach by replacing procedures with a less complicated process that does not need specialized persons to operate. This method provides new possibilities for further analysis and research to improve results on finding precise intersection points for high recovery rates of waterborne pathogens with different sizes such as Giardia or _Escherichia coli_ (_E. coli_). This provides vital intersection points for a microchannel to be fabricated to remove the waterborne pathogen. A key objective for this paper is to investigate the design of an intersection point on the microchannel to separate the waterborne pathogen. To achieve this, the micro pump flow rates were changed slightly to find a consistent path of which the microsphere will travel. Figure 3 displays the movement of microspheres around a meander. This gives a general observation that the slower the flow rate is (45 \(\upmu\)L/min), the microsphere movement is towards the outside of the microchannel. Comparing this to 225 \(\upmu\)L/min flow rate, it portrays that the movement of the microsphere is on the inside of the microchannel. From the videos taken by the camera, Table 2 gathered data from the movement of the microsphere. This shows that, at slower flow rates of 45-105 \(\upmu\)L/min, the microsphere has a higher probability of 69% to be recovered at 0.4-0.8 mm of the microchannel. Whereas at faster flow rates of 135- 225 \(\upmu\)L/min, the recovery rate has a probability of 88% at 0.0-0.4 mm of the microchannel. However, as stated in Section 3.4, due to the fast velocities of microspheres at these flow rates, the detection became more difficult. This could slightly alter the probability rate of recovery, due to the possibility of some microspheres not been detected. The detection accuracy of the beads with the CHT algorithm used in this paper can be improved using different platforms such as ImageJ or Python. The processed image of a full drop can be performed using sequential steps to study different parameters such as target object size, the distance between two objects and threshold. Microfluidic chips can be manufactured using different types of materials such as PDMS (PolyDiMethylSiloxane), Silicon, Glass or PMMA (PolyMethylMethylAcrylate). These materials can be investigated to improve the images taken by the camera in order to improve recovery rates. Alongside this, the system described in this paper can be reduced even more into a single automated unit, to be able to optimize results that requires no manual intervention. From all factors discussed in this paper, the idea of detecting movement of a waterborne pathogen using a cost-effective camera is very promising.