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COVID-19 is a multisystem infectious disease affecting the body systems. Its neurologic complications include -but are not limited to headache, loss of smell, encephalitis, and cerebrovascular accidents. Even though gait analysis is an objective measure of the neuro-motor system and may provide significant information about the pathophysiology of specific diseases, no studies have investigated the gait characteristics in adults after full recovery from COVID-19. This was a cross-sectional, controlled study that included 12 individuals (mean age, 23.0 ± 4.1 years) with mild-to-moderate COVID-19 history (COVD) and 20 sedentary controls (CONT; mean age, 24.0 ± 3.6 years). Gait was evaluated using inertial sensors on a motorized treadmill. Spatial-temporal gait parameters and gait symmetry were calculated by using at least 512 consecutive steps for each participant. The effect-size analyses were utilized to interpret the impact of the results. Spatial-temporal gait characteristics were comparable between the two groups. The COVD group showed more asymmetrical gait patterns than the CONT group in the double support duration symmetry (p = 0.042), single support duration symmetry (p = 0.006), loading response duration symmetry (p = 0.042), and pre-swing duration symmetry (p = 0.018). The effect size analyses of the differences showed large effects (d = 0.68-0.831). Individuals with a history of mild-to-moderate COVID-19 showed more asymmetrical gait patterns than individuals without a disease history. Regardless of its severity, the multifaceted long-term effects of COVID-19 need to be examined and the scope of clinical follow-up should be detailed., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

[Display omitted] • Sequels affect thoracic mobility after Covid-19 infection. • Covid-19 restricts the thoracic cage in antero-posterior and cranio-caudal directions. • Differences in thoracic movements emerge in postural changes with minimal effort. • Machine learning models classify with high performance in situations that require effort. • Boruta feature selection provided a noticeable improvement in results. Covid-19 led to deaths worldwide and left significant sequelae in a lot of people. Thoracic movements are important for the proper functioning of the respiratory system. However, there is no study on how the thoracic mobility of individuals who have recovered fully from Covid-19 is affected. In this study, the differences between thorax movements of healthy individuals and individuals with Covid-19 were investigated from a multidisciplinary perspective for the first time. Spontaneous and deep breathing data under two (at sitting- at standing) different conditions were collected and analyzed. In terms of engineering, using the Boruta feature selection method and various machine learning algorithms, discriminative features that will benefit clinically were determined. Clinically, the effect of Covid-19 was examined statistically in terms of respiratory biomechanics with thoracal motion-based analysis of 22 individuals. The use of Boruta in sitting and standing positions during deep breathing increased the classification performance. In spontaneous breathing, using Boruta only in the sitting position provided an increase in classification performance achieving an accuracy of 95.45 %. The results of the study showed that respiratory movements of the thoracic cage in the anteroposterior and craniocaudal directions were more restricted and had weaker respiratory acceleration skills in individuals with a Covid-19 history (p < 0.05). From a clinical point of view, it was observed that the respiratory acceleration movements were restricted in individuals with a Covid-19 history even though full recovery. Also, it was revealed that machine learning models can classify with high performance in situations requiring effort. [ABSTRACT FROM AUTHOR]