## 1 Introduction  
Upper limb impairment is one of the most common problems for people with neurological disabilities. If affects the activity, performance, quality of life (QOL), and independence of the person [1]. There are different reasons why a patient may experience upper limb impairment. Stroke is the most common cause of long-term disability worldwide and is known to cause various impairments such as weakness or cognitive deficits in body functions. Each year, 13 million new strokes occur, and nearly 75% of stroke victims suffer from various degrees of functional impairment [2, 3, 4]. Moreover, it is estimated that by 2030, approximately 973 million adults worldwide will be 65 years of age or older [5]. As people age, more and more are affected by limb impairments and neurodegenerative diseases that cause them difficulties in activities of daily life (ADLs) [1, 6]. People who have suffered an accident or stroke must undergo a rehabilitation process to relearn skills that have been suddenly lost due to damage to part of the brain. As part of the rehabilitation process, occupational therapy aims to improve people's ability to perform activities they need to do (self-care and work) or want to do (e.g., leisure) [7]. Rehabilitation may initially take place in a specialized rehabilitation unit in the hospital before continuing in day rehabilitation centers or at home. Unfortunately, it has been found that patients respond very differently to interventions aimed at restoring upper limb function and that nowadays there are no standard means to objectively monitor and better understand the rehabilitation process [8; 9; 10]. Objective assessment of limb movement limitations is a promising way to help patients with neurological upper limb disorders. To objectively assess patients' performance in activities and understanding the impact of intervention, it is first necessary to record what tasks they perform in activities of daily living and during occupational therapies. Therefore, an automatic logging application that provides the interaction of the hand with the environment based on data from an egocentric camera will facilitate the assessment and lead to better rehabilitation process. ADLs monitoring patient requires a use of 3D (depth) cameras, such as active cameras with time-of-flight sensors, which measure the time it takes for a light pulse to travel from the camera to the object and back again; or stereo-vision cameras, which allow the camera to simulate human binocular vision, giving it the ability to perceive depth. Consequently, the depth map of the scene provides depth data to any given point in a scene. Accuracy of depth information in active cameras compared to stereo vision cameras is higher [11]. However, using 3D (depth) cameras is associated with some challenges: they are more expensive compared to normal 2D cameras, have a smaller field of view (FOV), are bulky, and require an external battery and memory. Using normal 2D (RGB) cameras, it is possible to only infer the distance of objects in a plane, but not in the 3D space [12]. Depth estimation based on a single image is a prominent task in understanding and reconstructing scenes [13; 14; 15; 16]. Thanks to the recent development of Deep Learning models, depth estimation can be done using deep neural networks trained in a fully supervised manner with the RGB images as input and the estimated depth as output. The accuracy of reconstructing 2D to 3D images has steadily increased in recent years, although the quality and resolution of these estimated depth maps is still poor and often results in a fuzzy approximation at low resolution [13]. Previous studies have shown that 3D (depth) cameras are used in clinical settings, e.g., to prevent/detect falls in the elderly [17; 18; 19] and to detect activities of daily living [20]. Most applications use a third view and multiple cameras rather than a single and egocentric view. In their study, [PERSON] et al.