Effectiveness of an automatic tracking software in underwater motion analysis

Tracking of markers placed on anatomical landmarks is a common practice in sports science to perform the kinematic analysis that interests both athletes and coaches. Although different software programs have been developed to automatically track markers and/or features, none of them was specifically designed to analyze underwater motion. Hence, this study aimed to evaluate the effectiveness of a software developed for automatic tracking of underwater movements (DVP), based on the Kanade-Lucas-Tomasi feature tracker. Twenty-one video recordings of different aquatic exercises (n = 2940 markers' positions) were manually tracked to determine the markers' center coordinates. Then, the videos were automatically tracked using DVP and a commercially available software (COM). Since tracking techniques may produce false targets, an operator was instructed to stop the automatic procedure and to correct the position of the cursor when the distance between the calculated marker's coordinate and the reference one was higher than 4 pixels. The proportion of manual interventions required by the software was used as a measure of the degree of automation. Overall, manual interventions were 10.4% lower for DVP (7.4%) than for COM (17.8%). Moreover, when examining the different exercise modes separately, the percentage of manual interventions was 5.6% to 29.3% lower for DVP than for COM. Similar results were observed when analyzing the type of marker rather than the type of exercise, with 9.9% less manual interventions for DVP than for COM. In conclusion, based on these results, the developed automatic tracking software presented can be used as a valid and useful tool for underwater motion analysis. Key PointsThe availability of effective software for automatic tracking would represent a significant advance for the practical use of kinematic analysis in swimming and other aquatic sports.An important feature of automatic tracking software is to require limited human interventions and supervision, thus allowing short processing time.When tracking underwater movements, the degree of automation of the tracking procedure is influenced by the capability of the algorithm to overcome difficulties linked to the small target size, the low image quality and the presence of background clutters.The newly developed feature-tracking algorithm has shown a good automatic tracking effectiveness in underwater motion analysis with significantly smaller percentage of required manual interventions when compared to a commercial software

MARKERLESS ANALYSIS OF SWIMMERS’ MOTION: A PILOT STUDY

Regular laboratory-based motion analysis with skin surface markers is not always feasible. In particular, when studying swimmers kinematics, traditional motion capture techniques cannot be adopted. Although video recordings from swimmers often exist, current methods for biomechanical analysis of these are inadequate. They usually rely on manual digitization of joints’ position on a single sagittal view of the subject. Therefore, in this study a method for three dimensional (3D) markerless motion capture of swimmers is presented. The method adopts the markerless motion capture system developed at Stanford University

Quantitative Evaluation of Hypomimia in Parkinson's Disease: A Face Tracking Approach.

Parkinson's disease (PD) is a neurological disorder that mainly affects the motor system. Among other symptoms, hypomimia is considered one of the clinical hallmarks of the disease. Despite its great impact on patients' quality of life, it remains still under-investigated. The aim of this work is to provide a quantitative index for hypomimia that can distinguish pathological and healthy subjects and that can be used in the classification of emotions. A face tracking algorithm was implemented based on the Facial Action Coding System. A new easy-to-interpret metric (face mobility index, FMI) was defined considering distances between pairs of geometric features and a classification based on this metric was proposed. Comparison was also provided between healthy controls and PD patients. Results of the study suggest that this index can quantify the degree of impairment in PD and can be used in the classification of emotions. Statistically significant differences were observed for all emotions when distances were taken into account, and for happiness and anger when FMI was considered. The best classification results were obtained with Random Forest and kNN according to the AUC metric

Gait abnormalities in people with Dravet syndrome: A cross-sectional multi-center study

Objective: To quantify gait abnormalities in people with Dravet syndrome (DS). Methods: Individuals with a confirmed diagnosis of DS were enrolled, and stratified according to knee flexion at initial contact (IC) and range of motion (ROM) during stance (atypical crouch: knee flexion >20\ub0 at IC and knee ROM >15\ub0 during stance; straight: knee flexion <20\ub0 at IC). A 1D ANOVA (\u3b1 = 0.05) was used to test statistical differences among the joint kinematics and spatio\u2013temporal parameters of the cohort and an age-matched control group. Clinical (neurological and orthopaedic evaluation) and anamnestic data (seizure type, drugs, genetic mutation) were collected; distribution between the two gait phenotypes was assessed with the Fisher exact test and, for mutation, with the chi-squared test (p < 0.05). Linear regression between maximum knee flexion and normalised walking speed was calculated. Results: Seventy-one subjects were enrolled and evaluated with instrumented gait analysis. Fifty-two were included in final analysis (mean age 13.8 \ub1 7.3; M 26). Two gait patterns were detected: an atypical crouch gait (34.6%) with increased ankle, knee and hip flexion during stance, and reduced walking speed and stride length not associated with muscle-tendon retractions; and a pattern resembling those of healthy age-matched controls, but still showing reduced walking speed and stride length. No differences in clinical or anamnestic data emerged between the two groups. Significance: Objectively quantified gait in DS shows two gait patterns with no clear-cut relation to clinical data. Kinematics abnormalities may be related to stabilization issues. These findings may guide rehabilitative and preventive measures