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Adolescent Idiopathic Scoliosis (AIS) is a musculoskeletal condition commonly seen in pediatric children that causes deformity of the spine. The study aims for early detection and diagnosis as these are the possible options to delimit the progression of the disorder. The work has explored the development of an algorithm that could detect the landmarks and extract the shape-based features from the markerless 3D surface data in AIS patients. An approach to classifying these extracted features using the machine learning algorithm, Support Vector Machine (SVM), has been investigated. The objectives of the work were divided into three frameworks. Framework-1 is aimed at classifying the data based on the asymmetry pattern observed in the spinal surface of the patients. The data corresponding to normal posture were considered as 'without deformity' and data with an asymmetry spinal curve were considered as 'with deformity' based on indicators extracted using the ScolioSIM tool. Framework-2 is aimed at classifying the AIS patients' data based on the three deformity levels namely, mild, moderate and severe. Framework-3 is aimed at classifying the shape orientation of the AIS condition as right or left based on the extracted shape features. The SVM algorithm was able to classify the asymmetry spinal surface pattern and the three deformity levels with accuracy values of 72.4% and 80%, respectively. Furthermore, an accuracy of 94.9% was obtained to classify the shape orientation either as right-or left-oriented. Hence, this noninvasive diagnosis and assessment study paves a new way of approach for the 2D and 3D shape classifications of AIS and expedites the treatment planning process.

Dexterity training helps improve our motor skills while engaging in precision tasks such as surgery in the medical field and playing musical instruments. In addition, post-stroke recovery also requires extensive dexterity training to recover the original motor skills associated with the affected portion of the body. Recent years have seen a rise in the usage of soft-type actuators to perform such training, giving higher levels of comfort, compliance, portability, and adaptability. Their capabilities of performing high dexterity and safety enhancement make them specific biomedical applications and serve as a sensitive tools for physical interaction. The scope of this article discusses the soft actuator types, characterization, sensing, and control based on the interaction modes and the 5 most relevant articles that touch upon the skill improvement models and interfacing nature of the task and the precision it demands. This review attempts to report the latest developments that prioritize soft materials over hard interfaces for dexterity training and prospects of end-user satisfaction.

Adolescent Idiopathic Scoliosis (AIS) is lifetime disorder indicated by the abnormal spinal curvature, and it is usually detected in children and adolescents. Traditional radiographic assessment of scoliosis is time-consuming and unreliable due to high variability in images and manual interpretation. Vertebrae localization and centerline extraction from a biplanar X-ray is essential for pathological diagnosis, treatment planning, and decision making. The aim of this paper is to develop a fully automated framework to provide correct evaluation of anatomical landmarks and to extract vertebral and intervertebral discs’ centroids. By knowing coordinates of each centroid, developed framework will estimate 2D deformity curve (centerline) called Middle Spinal Alignment (MSA) in frontal plane. By analyzing the MSA lines and deformity segments, many deformity parameters can be calculated which include vertebral transpositions, Cobb angles, apex vertebra position, etc., for planning spinal correction strategies and monitoring.

(1) Flatfoot is a common malformation in both children and adults, in which a proper arch fails to develop. This study aimed to see how over-the-counter running shoes improved the gait patterns of flatfoot patients. (2) Methods: Three healthy flatfoot subjects were included in the study. Flatfoot was diagnosed by a lateral talometatarsal angle of more than 4 degrees and a talocalcaneal angle of more than 30 degrees. All the patient data were captured using Vicon motion caption cameras. The subjects were allowed to walk at self-selected speeds with and without running shoes. (3) Results: Significant differences in lower limb kinematics were observed between barefoot and running shoe gait. In addition, by wearing the running shoes, the center of mass and lower limb kinematics changed. (4) Conclusion: The improvement in balance and control was clearly indicated, and the change in gait on the entire lower limb influenced normalizing the stresses of the foot with running shoes. These valuable results can be used for rehabilitation programs.