## 1 Introduction  
In the field of rehabilitation, clinicians continuously assess the improvement of patients to verify that treatments or therapies are achieving satisfactory results. In this context, numerous treatments are aimed at improving the ability to walk because this activity is essential to quality of life and personal autonomy. One clear example of this is the importance of rehabilitation treatments in the recovery of an individual with hemiplegic spasticity following a stroke who experiences walking impairment [1; 2; 3; 4; 5; 6]. Currently, assessing gait improvements after treatment is normally conducted through qualitative techniques, either by observation or through interviews with the patient [7]. However, these existing techniques could incorporate technology providing objective information regarding the patient's progress without requiring excessive time or advanced technological knowledge on the part of clinicians. In this regard, gait analysis based on motion capture (MoCap) involves the measurement, analysis, and interpretation of human locomotion [8]. In the rehabilitation field, the information this analysis provides offers a wide variety of applications, including supporting decision-making for treatments and interventions [9; 10; 11; 12; 13; 14]. It can support decisions regarding changing; adjusting (e.g., dosage) or discontinuing treatment [15]. Among the existing MoCap technologies, those based on inertial measurement units (IMUs) have been gaining particular relevance because they do not require external cameras and can be embedded into wearable technology [16; 17]. [PERSON] et al. [7] demonstrated that it is possible to integrate an IMU gait analysis test into a rehabilitation service such as a medical test. In the same manner, [PERSON] et al. [17] proposed a gait analysis system based on IMUs free of magnetic disturbances, overcoming a limitation of this technology. Additionally, they demonstrated that this technology was reproducible and that the duration of the gait test was compatible with the daily practice of a rehabilitation service. However, despite the great utility of clinical gait analysis [15; 18] and the described advances, for its complete integration into the clinical setting, this technology must still overcome various challenges. One of these is the data analysis resulting from measuring an individual's gait. Methods must be developed to automatically and repeatedly process the generated spatiotemporal and kinematic variables [8]. More specifically, it is necessary to standardize methods in order to compare the variables generated in two different measurement sessions, between which the patient may have changed his or her gait pattern. For example, this could happen before and after a treatment or intervention or at the beginning of and during the rehabilitation process. Since this comparison of individual gait analysis data is not yet standardized or automated, the time required for data processing, the need for highly qualified professionals to interpret the results, and the handling of massive amounts of data hinder the use of gait analysis as standard practice [8; 11; 16; 19; 20]. The comparison of gait variables from two different measurement sessions has been executed among groups of patients in clinical trials studies. Such studies are widespread in clinical research, and many of them have used gait analysis for this purpose [21; 22; 23; 24]. This type of study selects a homogeneous group of patients with a specific pathology and applies a pre- and post-treatment gait test to compare the results between both measurements. However, this study did not focus on assessing a group of patients. Instead, we deal with the heterogeneity among rehabilitation patients in daily clinical practice. We face the challenge of assessing each patient, comparing the data in one session with the corresponding data of the patient in another session. Concerning this issue, little research has been conducted in biomedical studies [18].