# Efficient Online Engagement Analytics Algorithm Toolkit That Can Run on Edge
## 5 Conclusions  
To conclude, the authors have  
* Proposed an initiative open-source proctoring toolkit [32] for online engagement analytics with face orientation and eye blink detection algorithms that can be used on consumer devices or edge devices
* Demonstrated the effectiveness of z-score standardization for facial landmarks
* Statistically proven the impact of face orientation on eye blink
* Improved F1 score and Accuracy for Eye Aspect Ratio (EAR) using adaptive threshold
* Proposed a simple and efficient face orientation detection.  
Given the z-score standardization and adaptive threshold based on face orientation, eye blink detection with one vertical eyelid distance can achieve a better F1 score and accuracy than Eye Aspect Ratio (EAR) thresholding methods. The authors detect the individual eye blink based solely on facial landmarks. As demonstrated in [59], other factors such as timing and smoothing of eyelid distance signal can drastically improve the F1 score and the accuracy.  
In the paper, the MediaPipe Facemesh [31] is utilized, which generates 468 facial landmarks. However, only the landmarks related to the eyes and nose were used in this research. Future work should include examination of z-score standardization's impact when applied to fewer facial landmarks as in dlib [75].  
It is important to note that in real-world proctoring and online engagement analytics, authentication and security measures are also crucial to ensure the integrity. Future work should include the integration of authentication mechanisms in the proposed toolkit to make it more secure and practical for real-world scenarios.  
Conceptualization, S.T. and J.R.; methodology, S.T. and J.R.; software, S.T.; validation, S.T. and J.R.; formal analysis, S.T. and J.R.; investigation, S.T. and J.R.; resources, S.T.  
Figure 8: Runtime performance.  
and J.R.; data curation, S.T.; writing--original draft preparation, S.T. and J.R.; writing--review and editing, S.T. and J.R.; visualization, S.T.; supervision, J.R.; project administration, S.T. and J.R. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Data Availability Statement:** CMU dataset [73] can be found on UCI Machine Learning Repository [76] and may use the material free of charge for any educational purpose, provided attribution is given in any lectures or publications that make use of the material. RT-BENE dataset [70] is licensed under \"Creative Common License\" and cannot be used commercially. However, it can be used in publications given the proper citation and can be found on Open Science Platform, Zenedo [70]. The authors provided implementation of the proposed algorithms on GitHub and Zenedo [32] along with configuration script to download the datasets and the notebooks to reproduce the experimental results.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
**Sample Availability:** Sample web application for the proposed algorithm can be found on [77]. Although the algorithms were tested on MacOS and iOS by the time of writing, the authors have not supported any convenient sample application for these platforms. With the source code in [32], the algorithms can be integrated into Mediapipe and any custom solution.