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Existing diagnosis of the autism spectrum disorder (ASD) heavily depends on the informant's evaluation of the patient's behavior, which is both time consuming and labor demanding. In order to develop a rapid diagnostic tool with high accuracy, machine learning (ML) approaches have been proposed to explore the feasibility of identifying ASD with a limited number of features extracted from behavioral evaluation, neuroimaging and kinematic data. Though restricted and repetitive behavior (RRB) is one of the cardinal symptoms of ASD, no study has been conducted to investigate whether restricted kinematic features (RKF) could be used to identify ASD. The present study aimed to address this question. Twenty children with high functioning autism and twenty-three children with typical development (TD) were recruited. They were instructed to perform a motor task that required the execution of the utmost variant movement. Entropy and 95% range of the movement amplitude, velocity and acceleration were computed as indices of RKF. Five ML classifiers were trained and tested including support vector machine (SVM), Linear Discriminant Analysis (LDA), Decision tree (DT), Random forest (RF), and K nearest neighbor (KNN). Results showed that the KNN algorithm (k = 1 ) yielded the highest classification accuracy with four kinematic features (accuracy: 88.37%, specificity: 91.3%, sensitivity: 85%, AUC: 0.8815). Our study demonstrated that RKF could help robustly identify ASD. It is inferred that the application of ML on genetic, neuroimaging, psychological and kinematic features might pose a considerable challenge to the current diagnostic criteria of ASD, and might potentially lead to an automated and objective diagnosis of ASD.

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In order to improve action recognition accuracy, the discriminative kinematic descriptor and deep attention-pooled descriptor are proposed. Firstly, the optical flow field is transformed into a set of kinematic fields with more discriminativeness. Subsequently, two kinematic features are constructed, which more accurately depict the dynamic characteristics of action subject from the multi-order divergence and curl fields. Secondly, by introducing both of the tight-loose constraint and anti-confusion constraint, a discriminative fusion method is proposed, which guarantees better within-class compactness and between-class separability, meanwhile reduces the confusion caused by outliers. Furthermore, a discriminative kinematic descriptor is constructed. Thirdly, a prediction-attentional pooling method is proposed, which accurately focuses its attention on the discriminative local regions. On this basis, a deep attention-pooled descriptor (DKD–DAD) is constructed. Finally, a novel framework with discriminative kinematic descriptor and deep attention-pooled descriptor is presented, which comprehensively obtains the discriminative dynamic and static information in a video. Consequently, accuracies are improved. Experiments on two challenging datasets verify the effectiveness of our methods. [ABSTRACT FROM AUTHOR]

The speed and pen-pressure while sketching a spiral are lower among Parkinson’s disease (PD) patients with higher severity of the disease. However, the correlation between these features and the severity level (SL) of PD has been reported to be 0.4. There is a need for identifying parameters with a stronger correlation for considering this for accurate diagnosis of the disease. This study has proposed the use of the Composite Index of Speed and Pen-pressure (CISP) of sketching as a feature for analyzing the severity of PD. A total of 28 control group (CG) and 27 PD patients (total 55 participants) were recruited and assessed for Unified Parkinson’s Disease Rating Scale (UPDRS). They drew guided Archimedean spiral on an A3 sheet. Speed, pen-pressure, and CISP were computed and analyzed to obtain their correlation with severity of the disease. The correlation of speed, pen-pressure, and CISP with the severity of PD was −0.415, −0.584, and −0.641, respectively. Mann–Whitney U test confirmed that CISP was suitable to distinguish between PD and CG, while non-parametric k-sample Kruskal–Wallis test confirmed that it was significantly different for PD SL-1 and PD SL-3. This shows that CISP during spiral sketching may be used to differentiate between CG and PD and between PD SL-1 and PD SL-3 but not SL-2.

The speed and pen-pressure while sketching a spiral are lower among Parkinson's disease (PD) patients with higher severity of the disease. However, the correlation between these features and the severity level (SL) of PD has been reported to be 0.4. There is a need for identifying parameters with a stronger correlation for considering this for accurate diagnosis of the disease. This study has proposed the use of the Composite Index of Speed and Pen-pressure (CISP) of sketching as a feature for analyzing the severity of PD. A total of 28 control group (CG) and 27 PD patients (total 55 participants) were recruited and assessed for Unified Parkinson's Disease Rating Scale (UPDRS). They drew guided Archimedean spiral on an A3 sheet. Speed, pen-pressure, and CISP were computed and analyzed to obtain their correlation with severity of the disease. The correlation of speed, pen-pressure, and CISP with the severity of PD was -0.415, -0.584, and -0.641, respectively. Mann-Whitney U test confirmed that CISP was suitable to distinguish between PD and CG, while non-parametric k-sample Kruskal-Wallis test confirmed that it was significantly different for PD SL-1 and PD SL-3. This shows that CISP during spiral sketching may be used to differentiate between CG and PD and between PD SL-1 and PD SL-3 but not SL-2. [ABSTRACT FROM AUTHOR]

The purpose of this study was to investigate the kinematic features of the elite Chinese wheelchair racers to provide some guiding information for their daily training. Participants were divided into three groups. Group I included seven (five males and two females) elite wheelchair racers from Chinese National Team. Group II included two (male) wheelchair racers from Hong Kong. Group III was comprised of two 10-12 years old youth wheelchair racers from Hong Kong after school wheelchair racing training program. Their all-out speed of wheelchair propulsion on a training roller system was filmed by three Basler cameras at 60 Hz with Fire-I software from the sagittal view, front view and oblique view. A standard wheelchair training roller system was used. A 2-D MaxTRAQ software was employed to digitize the wheelchair propulsive movements automatically. Seven reflective markers were placed on the right ear, acromion (shoulder), lateral epicondyle of humerus, styloid process of Ulna, 3rd metacarpophalangeal joint, wheel axis and pushrim. Film data was digitized and smoothed before the joint angles, and angular and linear velocities of the upper extremity were calculated. The results demonstrated that the Chinese wheelchair racers had a significantly larger initial contact angle as compared to Hong Kong adult wheelchair racers and Hong Kong youth wheelchair racers. Three crucial elements in wheelchair racing training for Hong Kong wheelchair racers include: the large initial contact angle, the greater range of push-angle, and the short drive phase. The angular velocities and linear velocity increased dramatically at the wrist joint point indicated that greater linear velocity transferring from the upper arm to forearm and then the hand during wheelchair propulsion. The Chinese elite wheelchair racers mainly use semi-circular wheelchair propulsion pattern. [ABSTRACT FROM AUTHOR]