The modern workplace often exposes individuals to privacy risks, such as the unauthorised visibility of their computer screens. MARS (mmWave-based Assistive Rehabilitation System for Smart Healthcare), coupled with VideowindoW screens, offers an innovative solution to these threats by using mmWave radar to reconstruct human poses and estimate the position of 19 key joints. This enables the screens to become opaque based on the viewer's position, ensuring privacy. Although originally designed as a rehabilitation system, MARS can be utilised for its pose estimation capabilities to enhance workplace privacy. This research explores two modifications to the MARS architecture to assess their impact on the system's accuracy and performance. Specifically, we modify the MARS architecture by increasing the depth of its convolutional neural network (CNN) and integrating the PointNet architecture. Results establish that an optimal CNN configuration with two convolutional and two dense layers, followed by the output layer, modestly improves joint location estimation. However, integrating PointNet does not improve performance, likely due to PointNet's limitations in capturing the necessary local structural details of point clouds. These findings inform future research of possible improvements when leveraging the MARS dataset in the fields of privacy enhancement and smart healthcare applications.