Movement Patterns in Students Diagnosed with ADHD, Objective Measurement in a Natural Learning Environment

Objective and automatic assessment approach for diagnosing attention-deficit/hyperactivity disorder based on skeleton detection and classification analysis in outpatient videos

BACKGROUND

Attention-deficit/hyperactivity disorder (ADHD) is diagnosed in accordance with Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition criteria by using subjective observations and information provided by parents and teachers. However, subjective analysis often leads to overdiagnosis or underdiagnosis. There are two types of motor abnormalities in patients with ADHD. First, hyperactivity with fidgeting and restlessness is the major diagnostic criterium for ADHD. Second, developmental coordination disorder characterized by deficits in the acquisition and execution of coordinated motor skills is not the major criterium for ADHD. In this study, a machine learning-based approach was proposed to evaluate and classify 96 patients into ADHD (48 patients, 26 males and 22 females, with mean age: 7y6m) and non-ADHD (48 patients, 26 males and 22 females, with mean age: 7y8m) objectively and automatically by quantifying their movements and evaluating the restlessness scales.

METHODS

This approach is mainly based on movement quantization through analysis of variance in patients' skeletons detected in outpatient videos. The patients' skeleton sequence in the video was detected using OpenPose and then characterized using 11 values of feature descriptors. A classification analysis based on six machine learning classifiers was performed to evaluate and compare the discriminating power of different feature combinations.

RESULTS

The results revealed that compared with the non-ADHD group, the ADHD group had significantly larger means in all cases of single feature descriptors. The single feature descriptor "thigh angle", with the values of 157.89 ± 32.81 and 15.37 ± 6.62 in ADHD and non-ADHD groups (p < 0.0001), achieved the best result (optimal cutoff, 42.39; accuracy, 91.03%; sensitivity, 90.25%; specificity, 91.86%; and AUC, 94.00%).

CONCLUSIONS

The proposed approach can be used to evaluate and classify patients into ADHD and non-ADHD objectively and automatically and can assist physicians in diagnosing ADHD.

Assessment of ADHD Subtypes Using Motion Tracking Recognition Based on Stroop Color-Word Tests

Attention-Deficit/Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder known for its significant heterogeneity and varied symptom presentation. Describing the different subtypes as predominantly inattentive (ADHD-I), combined (ADHD-C), and hyperactive-impulsive (ADHD-H) relies primarily on clinical observations, which can be subjective. To address the need for more objective diagnostic methods, this pilot study implemented a Microsoft Kinect-based Stroop Color-Word Test (KSWCT) with the objective of investigating the potential differences in executive function and motor control between different subtypes in a group of children and adolescents with ADHD. A series of linear mixture modeling were used to encompass the performance accuracy, reaction times, and extraneous movements during the tests. Our findings suggested that age plays a critical role, and older subjects showed improvements in KSWCT performance; however, no significant divergence in activity level between the subtypes (ADHD-I and ADHD-H/C) was established. Patients with ADHD-H/C showed tendencies toward deficits in motor planning and executive control, exhibited by shorter reaction times for incorrect responses and more difficulty suppressing erroneous responses. This study provides preliminary evidence of unique executive characteristics among ADHD subtypes, advances our understanding of the heterogeneity of the disorder, and lays the foundation for the development of refined and objective diagnostic tools for ADHD.

Structural differences among children, adolescents, and adults with attention-deficit/hyperactivity disorder and abnormal Granger causality of the right pallidum and whole-brain

Attention-deficit/hyperactivity disorder (ADHD) is a childhood mental health disorder that often persists to adulthood and is characterized by inattentive, hyperactive, or impulsive behaviors. This study investigated structural and effective connectivity differences through voxel-based morphometry (VBM) and Granger causality analysis (GCA) across child, adolescent, and adult ADHD patients. Structural and functional MRI data consisting of 35 children (8.64 ± 0.81 years), 40 adolescents (14.11 ± 1.83 years), and 39 adults (31.59 ± 10.13 years) was obtained from New York University Child Study Center for the ADHD-200 and UCLA dataset. Structural differences in the bilateral pallidum, bilateral thalamus, bilateral insula, superior temporal cortex, and the right cerebellum were observed among the three ADHD groups. The right pallidum was positively correlated with disease severity. The right pallidum as a seed precedes and granger causes the right middle occipital cortex, bilateral fusiform, left postcentral gyrus, left paracentral lobule, left amygdala, and right cerebellum. Also, the anterior cingulate cortex, prefrontal cortex, left cerebellum, left putamen, left caudate, bilateral superior temporal pole, middle cingulate cortex, right precentral gyrus, and the left supplementary motor area demonstrated causal effects on the seed region. In general, this study showed the structural differences and the effective connectivity of the right pallidum amongst the three ADHD age groups. Our work also highlights the evidence of the frontal-striatal-cerebellar circuits in ADHD and provides new insights into the effective connectivity of the right pallidum and the pathophysiology of ADHD. Our results further demonstrated that GCA could effectively explore the interregional causal relationship between abnormal regions in ADHD.

Deep-Learning-Based ADHD Classification Using Children's Skeleton Data Acquired through the ADHD Screening Game

The identification of attention deficit hyperactivity disorder (ADHD) in children, which is increasing every year worldwide, is very important for early diagnosis and treatment. However, since ADHD is not a simple disease that can be diagnosed with a simple test, doctors require a large period of time and substantial effort for accurate diagnosis and treatment. Currently, ADHD classification studies using various datasets and machine learning or deep learning algorithms are actively being conducted for the screening diagnosis of ADHD. However, there has been no study of ADHD classification using only skeleton data. It was hypothesized that the main symptoms of ADHD, such as distraction, hyperactivity, and impulsivity, could be differentiated through skeleton data. Thus, we devised a game system for the screening and diagnosis of children's ADHD and acquired children's skeleton data using five Azure Kinect units equipped with depth sensors, while the game was being played. The game for screening diagnosis involves a robot first travelling on a specific path, after which the child must remember the path the robot took and then follow it. The skeleton data used in this study were divided into two categories: standby data, obtained when a child waits while the robot demonstrates the path; and game data, obtained when a child plays the game. The acquired data were classified using the RNN series of GRU, RNN, and LSTM algorithms; a bidirectional layer; and a weighted cross-entropy loss function. Among these, an LSTM algorithm using a bidirectional layer and a weighted cross-entropy loss function obtained a classification accuracy of 97.82%.

A Cross-Sectional Study to Measure Physical Activity with Accelerometry in ADHD Children according to Presentations

(1) Background: Attention deficit hyperactivity disorder (ADHD) is a common mental disorder affecting 5-7% of school-aged children. Previous studies have looked at the effects of physical activity interventions on the symptoms of ADHD, although few have compared the motor behavior of children with ADHD versus those without. This exploratory study provides detailed information on the patterns and intensity of physical activity and sedentary behavior in children with ADHD as measured by Actigraph GT3X accelerometry, as well as the differences in physical activity in the different presentations of ADHD; (2) Methods: A cross-sectional design was used with a sample of 75 children, aged 6 to 12 years, with and without ADHD. The ADHD group had a previous diagnosis, determined by clinical assessment based on DSM-5 criteria; (3) Results: Physical activity levels were higher in children with ADHD compared to children without ADHD, but there was no difference in sedentary time between groups during weekdays or weekends. Physical activity decreased with age, with significant differences in the ADHD group, who exhibited more minutes of moderate Physical activity in 6-7 year-olds than 10-11 year-olds during weekdays and weekends; (4) Conclusions: Sedentary time increased by age in children without ADHD, and there was a decrease in moderate-intensity physical activity time in children with ADHD by age.

A Robust Movement Quantification Algorithm of Hyperactivity Detection for ADHD Children Based on 3D Depth Images

Attention deficit hyperactivity disorder (ADHD) is one of the most common childhood mental disorders. Hyperactivity is a typical symptom of ADHD in children. Clinicians diagnose this symptom by evaluating the children's activities based on subjective rating scales and clinical experience. In this work, an objective system is proposed to quantify the movements of children with ADHD automatically. This system presents a new movement detection and quantification method based on depth images. A novel salient object extraction method is proposed to segment body regions. In movement detection, we explore a new local search algorithm to detect any potential motions of children based on three newly designed evaluation metrics. In the movement quantification, two parameters are investigated to quantify the participation degree and the displacements of each body part in the movements. This system is tested by a depth dataset of children with ADHD. The movement detection results of this dataset mainly range from 91.0% to 95.0%. The movement quantification results of children are consistent with the clinical observations. The public MSR Action 3D dataset is tested to validate the performance of this system.

Automatic Personality Assessment through Movement Analysis

Obtaining accurate and objective assessments of an individual's personality is vital in many areas including education, medicine, sports and management. Currently, most personality assessments are conducted using scales and questionnaires. Unfortunately, it has been observed that both scales and questionnaires present various drawbacks. Their limitations include the lack of veracity in the answers, limitations in the number of times they can be administered, or cultural biases. To solve these problems, several articles have been published in recent years proposing the use of movements that participants make during their evaluation as personality predictors. In this work, a multiple linear regression model was developed to assess the examinee's personality based on their movements. Movements were captured with the low-cost Microsoft Kinect camera, which facilitates its acceptance and implementation. To evaluate the performance of the proposed system, a pilot study was conducted aimed at assessing the personality traits defined by the Big-Five Personality Model. It was observed that the traits that best fit the model are Extroversion and Conscientiousness. In addition, several patterns that characterize the five personality traits were identified. These results show that it is feasible to assess an individual's personality through his or her movements and open up pathways for several research.

Evaluating Therapeutic Effects of ADHD Medication Objectively by Movement Quantification with a Video-Based Skeleton Analysis

Attention-deficit/hyperactivity disorder (ADHD) is the most common neuropsychiatric disorder in children. Several scales are available to evaluate ADHD therapeutic effects, including the Swanson, Nolan, and Pelham (SNAP) questionnaire, the Vanderbilt ADHD Diagnostic Rating Scale, and the visual analog scale. However, these scales are subjective. In the present study, we proposed an objective and automatic approach for evaluating the therapeutic effects of medication in patients with (ADHD). The approach involved using movement quantification of patients’ skeletons detected automatically with OpenPose in outpatient videos. Eleven skeleton parameter series were calculated from the detected skeleton sequence, and the corresponding 33 features were extracted using autocorrelation and variance analysis. This study enrolled 25 patients with ADHD. The outpatient videos were recorded before and after medication treatment. Statistical analysis indicated that four features corresponding to the first autocorrelation coefficients of the original series of four skeleton parameters and 11 features each corresponding to the first autocorrelation coefficients of the differenced series and the averaged variances of the original series of 11 skeleton parameters significantly decreased after the use of methylphenidate, an ADHD medication. The results revealed that the proposed approach can support physicians as an objective and automatic tool for evaluating the therapeutic effects of medication on patients with ADHD.