## 4 Discussion
This similarity between the two methods (CI and CA) emphasizes that movement pattern analysis, in the absence of data from the environment, is sufficient to detect changes in movement patterns during exercise. The use of image coordinates alone for movement pattern analysis by means of the performed trajectory of the objects, joints and limbs during the exercise could assist inexercise supervision, since it is an ecologic method. No information from the environment or any other type of information is necessary. Additionally, the analysis does not hinder exercise execution. Furthermore, this analysis could provide a more solid basis for qualitative exercise analysis, as described by [PERSON] and [PERSON] [5], as well as single out inadequate loads [6] or postures during the performance of resistance exercises. For example, the HMM method has been explored as an additional tool to help amateur athletes learn correct movements for a specific action in a sport, aiming to improve their skill level [63], as well as an analysis to support personalized sport training prescriptions according to the uncommon abilities of practitioners [66]. Unlike previously described methods [63], the method described in this work does not require the use of environmental measurements, expensive computational resources, or wearable sensors, all of which can increase the cost of performing a supervised activity [62]. Based on videos, it is possible to analyze the movements performed in exercises even if the videos were not recorded for this purpose. Thus, it is possible to analyze videos recorded in the past using the method described here. A limitation of our method is that it analyzes movement displacement in a two-dimensional (2D) way. In turn, the three-dimensional (3D) analyses could provide detailed information on motion in other planes of movement, and therefore provide information of the movements in the transverse plane, which were possible changes in joint position that might occur to maintain postural adequacy with increased load. In addition, the 3D sagittal model would provide more accurate data and is not subject to the parallax and perspective errors that occur in 2D analyses. However, the biceps-curl movement is basically in the sagittal plane rather than in the transverse and frontal planes [67], and although the 3D approach has an advantage over the 2D approach, the 2D analyses such as the one described in this study allow the analysis of motion using data collected in any type of video. Regardless, the analysis by HMM using 3D data could identify further alterations caused by the increase in load, and thus provide more information about changes in the trajectories of movements through simultaneous modeling in different planes [68]. Future work should include the analyses of subjects' movements in a plane other than the sagittal plane (i.e., the use of 3D coordinates). This would make it possible to verify whether the positional sequence of joints and objects can be further refined and whether HMM training can be extended beyond the results obtained with bi-dimensional analysis. Therefore, this makes HMM a potential tool to support the human-based diagnosis of motor patterns, with application varying from disorders in neural pathologies to the motor optimization of athlete's performance.