## 6 Conclusions and Recommendations  
To summarize this study, three (3) important points were considered.  
* Points--This study found significant feature points to recognize proper and improper sitting postures. This includes nose to left shoulder, shoulder to elbow, elbow to wrist, and shoulder to mid-hip (thoracolumbar) for both Left and Right models. It is also noticeable that age and table height are also considered significant factors in the recognition.
* Patterns--It has been seen that the distance between the nose and the left shoulder is present in both the Left and Right models. This is because all the participants are right-handed, therefore the head motions are moving towards the left side. Also, the study found the optimal table height of higher than 30 inches across all body frame categories. Moreover, the upper back shows less true positive rate (TP) for proper samples in both the Left and Right models. The study found out that even if the domain experts all agreed that the captured ten (10) second video is proper upon their careful assessment, during the annotation process and getting the dominant label, they found out that proper sitting posture for the upper back does not last for more than five (5) seconds. The upper back point (thoracic) started to either drop or extend. Lastly, the lower back category shows the lowest accuracy compared to all other categories (Left: 92.765 and Right: 96.5223). In connection with the upper back, the lower back also drops or extends during the annotation process.
* Performance--Overall, the Left and Right camera datasets were compared that show accuracy of 91.5% and 97.05% and kappa of 0.7 and 0.9 respectively. The left model shows a lesser accuracy rate because of fewer motions exhibited on the left side. Since the users are also using other peripherals such as a mouse, there are much more variations between proper and improper motions on the right side compared to the left side.  
After a successful recognition of proper and improper sitting posture using virtual markers and a small-scale convolutional neural network, this study would like to recommend for future researchers to explore:  
* Since the convolutional neural network shows significant and fast-paced progress, the need to test and evaluate other new models is needed.
* The consideration of more feature points is also needed. For whole body points, 33 key points may not be optimal to capture other complex positions.
* The consideration of more capturing devices such as the integration of front camera and left camera.  
Figure 8: Processed Video Result.  
* The study also recommends the inclusion of other attributes such as eyesight, weight, the use of adjustable tables or chairs, and others.
* Future studies should also implement multiple-person recognition.  
Conceptualization, J.E.E.; methodology, J.E.E.; software, J.E.E.; validation, J.E.E., L.A.V. and M.D.; formal analysis, J.E.E.; investigation, J.E.E.; resources, J.E.E.; data curation, J.E.E.; writing--original draft preparation, J.E.E.; writing--review and editing, J.E.E., L.A.V. and M.D.; visualization, J.E.E.; supervision, J.E.E., L.A.V. and M.D.; project administration, J.E.E. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.