## 1 Introduction
proposed a framework to detect 13 ADLs to increase the independence of the elderly and improve the quality of life at home using third-vision RGB-D cameras [20]. In another work, [PERSON] and colleagues demonstrated lifelong human activity recognition (HAR) using indoor depth video. They used a single camera system with third vision and trained Hidden Markov Models (HMMs) for HAR using the body's joint information [21]. To the best of the author's knowledge, monitoring of patients with 2D (RGB) cameras has not been used by taking advantage of deep learning models for depth estimation. We are currently working on the development of a wearable system that integrates an egocentric camera (first-person perspective) to provide objective information particularly interested in monitoring upper limb rehabilitation, such as the objects the patient can interact with, the type of activities the patient can successfully perform, etc. The camera should capture the objects in the environment and the patient's hands during ADLs, such as during occupational therapy. This requires three-dimensional data to be captured so that the objects in the scene can be accurately located. Given the aforementioned drawbacks of 3D (depth) cameras, we use deep learning models to reconstruct the depth map from 2D (RGB) images. Considering this study and its final goal, some considerations must be made to choose the right Deep Learning model for our application. The robustness of the method with respect to different conditions, such as the ability to process a large amount of data, since our final goal is an application with embedded systems to monitor ADLs over a period of time (e.g., 2 months), resulting in a large amount of data, force us to choose a model with high accuracy and fast computational effort. More importantly, another condition to consider is lighting, since the environment is not controlled during the acquisition of patients' ADLs. Accordingly, this work focuses on comparing the estimated depth difference based on two different depth estimation methods and a 3D (depth) camera under different light ing conditions: MiDaS (Mixing Datasets for Zero-shot Cross-dataset Transfer [22]), High Quality Monocular Depth Estimation via Transfer Learning (based on [PERSON] et al. [13]), and Intel RealSense D435i 3D (depth) camera. We used MiDaS and Alhashim pre-trained models for our comparison to account for extreme cases, as we do not have enough data sets for fine-tuning. The models were already trained on large, diverse datasets with different range of depths, including indoor and outdoor environments, all of which are privileged in our case, to have a generalized model. The following parts of the article are organised as follows. In Section 2, we explain the depth estimation based on deep learning models in more detail. In Section 3, we describe our experimental setups, dataset preparation and outlines the methodology behind the work. In Section 4, we present results of the different depth estimation methods and, more importantly, examine the consistency of the depth difference between objects in each method. We also briefly inspect the effects of light on depth estimation. The final part of the Section 4 devotes to the application of depth estimation in hand-object interaction. Later, Section 5 dedicates to a discussion of results. Finally, in Section 6, we draw our conclusions.