The analysis of human gait is an important tool in medicine and rehabilitation to evaluate the effects and the progression of neurological diseases resulting in neuromotor disorders. In these fields, the gold standard techniques adopted to perform gait analysis rely on motion capture systems and markers. However, these systems present drawbacks: they are expensive, time consuming and they can affect the naturalness of the motion. For these reasons, in the last few years, considerable effort has been spent to study and implement markerless systems based on videography for gait analysis. Unfortunately, only few studies quantitatively compare the differences between markerless and marker-based systems in 3D settings. This work presented a new RGB video-based markerless system leveraging computer vision and deep learning to perform 3D gait analysis. These results were compared with those obtained by a marker-based motion capture system. To this end, we acquired simultaneously with the two systems a multimodal dataset of 16 people repeatedly walking in an indoor environment. With the two methods we obtained similar spatio-temporal parameters. The joint angles were comparable, except for a slight underestimation of the maximum flexion for ankle and knee angles. Taking together these results highlighted the possibility to adopt markerless technique for gait analysis.

Objective: Sleep‐related hypermotor epilepsy (SHE) is a focal epilepsy with seizures occurring mostly during sleep. SHE seizures present different motor characteristics ranging from dystonic posturing to hyperkinetic motor patterns, sometimes associated with affective symptoms and complex behaviors. Disorders of arousal (DOA) are sleep disorders with paroxysmal episodes that may present analogies with SHE seizures. Accurate interpretation of the different SHE patterns and their differentiation from DOA manifestations can be difficult and expensive, and can require highly skilled personnel not always available. Furthermore, it is operator dependent.Methods: Common techniques for human motion analysis, such as wearable sensors (e.g., accelerometers) and motion capture systems, have been considered to overcome these problems. Unfortunately, these systems are cumbersome and they require trained personnel for marker and sensor positioning, limiting their use in the epilepsy domain. To overcome these problems, recently significant effort has been spent in studying automatic methods based on video analysis for the characterization of human motion. Systems based on computer vision and deep learning have been exploited in many fields, but epilepsy has received limited attention.Results: In this paper, we present a pipeline composed of a set of three‐dimensional convolutional neural networks that, starting from video recordings, reached an overall accuracy of 80% in the classification of different SHE semiology patterns and DOA.Significance: The preliminary results obtained in this study highlight that our deep learning pipeline could be used by physicians as a tool to support them in the differential diagnosis of the different patterns of SHE and DOA, and encourage further investigation.

Recent advancements in computer vision have positioned pose estimation as a powerful tool for analyzing human movement from video data. However, its application to neurological populations, particularly individuals with spinal cord injuries (SCI), remains largely unexplored. To evaluate the effectiveness of three video-based pose estimation algorithms in detecting upper limb motor impairments in individuals with cervical SCI, and to determine their reliability and sensitivity in comparison with marker-based motion capture. Twelve individuals with cervical SCI and twelve unimpaired controls performed four static arm poses from the Arm Stabilization Test (part of the Van Lieshout Arm/Hand Function Test). Three pose estimation methods - MediaPipe 2D, AlphaPose, and MediaPipe 3D - were applied to single-camera videos, and results were compared to kinematics captured by an eight-camera marker-based motion capture system. Motor performance was quantified using a compensation ratio (shoulder vs. elbow movement), and trial-by-trial repeatability was assessed. All three algorithms demonstrated strong repeatability. MediaPipe 3D, in particular, showed a significant correlation between the compensation metric and impairment severity, effectively distinguishing SCI from control participants. In contrast, 2D methods lacked this sensitivity, likely due to the absence of depth information. Markerless pose estimation, especially 3D approaches, offers a promising, non-invasive alternative for assessing upper limb motor function in neurological populations. These tools could expand accessibility to motor assessment in both clinical and research settings, although further research is needed to assess their sensitivity to subtle impairments.

Gait analysis plays a crucial role in assessing mobility impairments and monitoring disease progression in individuals with Multiple Sclerosis (MS). Markerless, video-based methods offer a non-invasive, practical alternative to traditional marker-based systems, making them particularly suitable for clinical applications. This study employs a markerless video-based approach to extract spatio-temporal and kinematic parameters from 25 individuals with MS and 25 age- and sex-matched unimpaired controls. The MS cohort was divided into two subgroups based on the Expanded Disability Status Scale (EDSS): "high" disability (EDSS ≥ 3) and "low" disability (EDSS < 3). Both normal and tandem gait patterns were evaluated. In normal gait, significant spatio-temporal and joint kinematic differences were observed between the high EDSS group and unimpaired controls, while the low EDSS group exhibited no notable deviations. In contrast, tandem gait analysis revealed significant differences in heel-to-toe distance between the low EDSS group and unimpaired controls, highlighting subtle changes that were undetectable in normal gait. These findings underscore the potential of video-based methods to enhance disease monitoring and guide targeted rehabilitation strategies in MS.