## 6 Conclusions  
Detecting hand-object interactions from an egocentric perspective by using 2D (RGB) cameras instead of 3D (depth) cameras is a first step toward objectively assessing patients' performance in performing ADLs. To achieve this goal, we first demonstrated the feasibility of replacing depth cameras with 2D cameras while providing depth information. We used Deep Learning Methods for depth reconstruction from RGB images to make this possible. In this paper, we presented the results of using Deep Learning models for estimating the depth information from only a single RGB image and compared the results with a 3D (depth) camera. We also examined the results under different lighting conditions. The results have shown that one of the methods, MiDaS is robust under different lighting conditions and the other method, Alhashim is very sensitive to different lighting conditions. In addition, we presented the results of using the depth estimated by MiDaS in a hand-object interaction detection task, which is common in ADLs, especially during drinking and eating. The performance of MiDaS has a slightly lower F1 score than that of the 3D (depth) camera but has the advantage of allowing us to envision monitoring patients with more ergonomic 2D (RGB) cameras. Moreover, such cameras are lighter, less bulky, do not need an external source of energy and the means for storage, and most importantly, they have a wider field of view. For future research, further studies on different depth estimates are needed to apply and compare with the current model in terms of complexity and time. There are potential studies that have shown promising results, e.g., single-stage refinement CNN [26], embedding semantic segmentation by using a hybrid CNN model [27] for depth estimation. In addition, future research could explore the appropriateness of data augmentation in our case, which is consistent with exploring datasets that are more conducive to the research topic. Furthermore, semantic segmentation may be an important component in future object recognition experiments to increase the accuracy of recognition with respect to position of objects in the scene. In addition, our next step will be to investigate the performance of our hand-object interaction detection system using known databases that are more related to activities of daily life where hands are visible in the scenes. It is important to mention that in our application we do not guarantee the absolute value of depth, but for us, it is the consistency of providing the depth difference of close objects in the scene that is important, because it can be enough to identify the disposition of the objects in the scene, which one is closer to the camera or to other objects. The purpose of our hand-object interaction application is to be able to create a log of patients' interactions and help in the objective assessment of the patient during his/her rehabilitation. Soon, we should be able to extend the use of this technology to the monitoring of patients at home allowing their assessment without the need for hospitalization. **Author Contributions:** [PERSON], [PERSON], [PERSON] and [PERSON] conceptualized the study design, while [PERSON] conducted the data collection. [PERSON] designed and implemented the algorithms with [PERSON], [PERSON] and [PERSON] guiding the study. [PERSON] and [PERSON] contributed to the analysis and interpretation of the data. [PERSON] drafted the manuscript, with inputs from [PERSON], [PERSON] and [PERSON] on its outline and structure. All authors revised it critically, approved the final version, and agreed to be accountable for all aspects of this work. All authors have read and agreed to the published version of the manuscript. **Funding:** This research received no external funding. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.