Dynamic Hand Gesture-Based Person Identification Using Leap Motion and Machine Learning Approaches

Person identification is one of the most vital tasks for network security. People are more concerned about theirsecurity due to traditional passwords becoming weaker or leaking in various attacks. In recent decades, fingerprintsand faces have been widely used for person identification, which has the risk of information leakage as a resultof reproducing fingers or faces by taking a snapshot. Recently, people have focused on creating an identifiablepattern, which will not be reproducible falsely by capturing psychological and behavioral information of a personusing vision and sensor-based techniques. In existing studies, most of the researchers used very complex patternsin this direction, which need special training and attention to remember the patterns and failed to capturethe psychological and behavioral information of a person properly. To overcome these problems, this researchdevised a novel dynamic hand gesture-based person identification system using a Leap Motion sensor. Thisstudy developed two hand gesture-based pattern datasets for performing the experiments, which contained morethan 500 samples, collected from 25 subjects. Various static and dynamic features were extracted from the handgeometry. Randomforest was used to measure feature importance using the Gini Index. Finally, the support vectormachinewas implemented for person identification and evaluate its performance using identification accuracy. Theexperimental results showed that the proposed system produced an identification accuracy of 99.8% for arbitraryhand gesture-based patterns and 99.6% for the same dynamic hand gesture-based patterns. This result indicatedthat the proposed system can be used for person identification in the field of security.

Efficient Feature Selection and Machine Learning Based ADHD Detection Using EEG Signal

Attention deficit hyperactivity disorder(ADHD)is one of the most common psychiatric and neurobehavioral disorders in children,affecting 11%of children worldwide.This study aimed to propose a machine learning(ML)-based algorithm for discriminating ADHD from healthy children using their electroencephalography(EEG)signals.The study included 61 children with ADHD and 60 healthy children aged 7–12 years.Different morphological and time-domain features were extracted from EEG signals.The t-test(p-value<0.05)and least absolute shrinkage and selection operator(LASSO)were used to select potential features of children with ADHD and enhance the classification accuracy.The selected potential features were used in four ML-based algorithms,including support vector machine(SVM),k-nearest neighbors,multilayer perceptron(MLP),and logistic regression,to classify ADHD and healthy children.The overall prevalence of boys and girls with ADHD was 48.9%and 56.5%,respectively.The average age of children with ADHD was 9.6±1.8 years.Our results illustrated that the combination of LASSO with SVM classifier achieved the highest accuracy of 94.2%,sensitivity of 93.3%,F1-score of 91.9%,and AUC of 0.964.Our results also illustrated that MLP was the second-best ML-based classifier,which gave 93.4%accuracy,91.7%sensitivity,91.1%F1-score,and 0.960 AUC.The findings indicated that the combination of the LASSO-based feature selection method and SVM classifier can be a useful tool for selecting reliable/potential features and classifying ADHD and healthy children.Our proposed ML-based algorithms could be useful for the early diagnosis of children with ADHD.