## 1 Introduction  
Correct supervision during the performance of resistance exercises is imperative for exercise quality execution, which can generate improvements in performance and avoid injuries resulting from inadequate posture or incorrect loads [1, 2]. Resources for resistance training exercises have increased in recent years, and this increases the number of injuries caused by this type of training, mainly due to a lack of proper supervision associated with execution [3]. An assessment of load, posture and movement during a resistance exercise is often conducted by an experienced coach, who, through visual examination, analyses exercise movements without using any equipment that provides qualitative data on the observedparameters [4]. Coaches perform this form of supervision as a qualitative analysis, based on a systematic observation method, using introspective judgment of the quality of human movement that is based solely on the experience of the evaluator [5]. An increase in load during the performance of a resistance exercise can cause a change in movement patterns. A study that analyzed a squat exercise [6] verified a change in movement patterns with increased load. The results revealed that when load was added to the exercise, hip and knee angles increased to accommodate the increased load. Spatial trajectories, which are described by joint movements during the performance of an exercise, can also be used as references for movement pattern analysis. A trajectory can be defined as the set of all positions occupied by a moving body relative to a given reference [7]. This reference is considered to be the point of view of the evaluator who is supervising an exercise [5]; as such, physical exercises can be characterized by their movement patterns and specific space-time structures. Physical exercises are therefore susceptible to being modeled as temporal trajectories in a given space, using measurements that are intimately correlated with visual observations [8]. Studies that use trajectory analysis to evaluate human movement have been present in the literature for some time and can be considered a new resource for gesture recognition [9]. The trajectories described by the heel and toes during gait were previously analyzed [10]; according to the author of that study, these trajectories can be used to represent specific motor control tasks that are connected with posture and gait changes. Another study, an algorithm for extracting and classifying bidimensional movements in a frame sequence based on movement trajectories, was developed. The motion patterns were learned using trajectories extracted from neural networks [11] with a recognition rate of 94.42%. [PERSON] et al. [12] proposed a method for learning movement patterns and detecting anomalies in these patterns by analyzing human trajectories through long-term observation. The spatial and temporal features of those trajectories were analyzed using hidden Markov models (HMMs). Another study examined human movement patterns in a space-time context by clustering geographic data extracted from smartphone global positioning systems [13] (confidence: 80%), and [PERSON] et al. [14] explored movement analysis, dividing movement into three main components: body posture (posture), the range of motion (reach), and movement pattern (positional sequence), which obtained classification accuracy, with up to 56% for HMMs. With constant technological improvements, the motion capture of body movements has become more accessible and more widely utilized in sports activities. Currently, with the help of a smartphone camera, it is possible to evaluate movement patterns and measure physical performance [15]. Visual resources such as video assistant referee (VAR), which can perform reliable measurements of the studied object in a noninvasive manner and without interfering with the natural flow of the sport, are used, for example, in soccer.