## 2 Related Works  
For many scenarios, images can provide valuable information about any particular scene. Within a group of images, one or more objects (targets) can be identified on the images by some algorithm, with practical applications in many areas such as remote sensing [15], medical diagnosis [16] and face recognition in public security systems [17]. In fact, when properly performed, the processing of images can be automatized in a way that a large group of them can be processed to extract some relevant information that could be hardly achieved otherwise. With the development of image processing algorithms and the availability of open-source libraries for imaging and computer vision tasks, useful approaches have emerged for different detection objectives [18].  
Overall, the adoption of computer-assisted solutions to support in dentistry and other medical areas is not a novelty, with many approaches being described in recent years comprising different strategies [2, 19, 20, 21, 22, 23]. With new advances in sensory technologies and image processing algorithms, the available computational resources have increased considerably in the last years, but image processing techniques have been usually favored due to their lower implementation and computational costs in many scenarios [24, 25].  
Although image processing has been largely adopted to support identifying diseases and providing helpful data for medical treatments [2], the quality assessment of biomedical tools has not been properly discussed in the literature, usually due to the fact that the configurations and settings of the manufacturers are expected to be fully trusted by the users. Moreover, it can be said that different biomedical tools will have particular operational characteristics that will prevent the development of generic methods that are useful for different tools in multiple contexts; however, with the adoption of image processing and computer vision algorithms, effective methods could be derived to support the assessment of many biomedical tools, particularly in a very instrument-heavy area such as dentistry [26].  
When concerning the movement patterns of reciprocating endodontic motors, previous studies have tried to evaluate the actual kinematics of endodontic motors using high-speed camera analysis [27, 28], which seems to be a promising approach considering the particularities of the assessed movements. In those works, image processing was adopted to identify actual movement considering a visual reference (target); however, the works in [27, 28] were centered on human-assisted identification of the kinematic phases considering manual analysis of each image captured by a high-speed camera. In fact, such type of manual analysis may be prone to errors and present low efficiency, specially when comparing different motors, which has fostered the development of automatized methods.  
In this scenario of high efficiency and low cost of image processing algorithms, especially considering the availability of public algorithms using well-known image processing filters, the kinematics of endodontic reciprocating motors can be computed in a fully automated way, bringing an important contribution to this research area. For that, we leverage existing visual computing algorithms designed for objects detection in consecutive images, and use this to create a database that tracks the actual movements of endodontic reciprocating motors. Then, a new geometry-based method is derived to compute the effectively covered angular distances over time. In doing so, we believe that a highly effective and affordable method is created, with a reasonable level of precision, bringing an important contribution not presented by previous works.