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

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.

Clinical reliability and validity of a video-based markerless gait evaluation method

Objective

To explore the reliability and validity of gait parameters obtained from gait assessment system software employing a human posture estimation algorithm based on markerless videos of children walking in clinical practice.

Methods

Eighteen typical developmental (TD) children and ten children with developmental dysplasia of the hip (DDH) were recruited to walk along a designated sidewalk at a comfortable walking speed. A 3-dimensional gait analysis (3D GA) and a 2-dimensional markerless (2D ML) gait evaluation system were used to extract the gait kinematics parameters twice at an interval of 2 h.

Results

The two measurements of the children's kinematic gait parameters revealed no significant differences (P > 0.05). Intra-class correlation coefficients (ICC) were generally high (ICC >0.7), showing moderate to good relative reliability. The standard error of measurement (SEM) values of all gait parameters measured by the two walks were 1.26°-2.91°. The system software had good to excellent validity compared to the 3D GA, with ICC values between 0.835 and 0.957 and SEM values of 0.87°-1.71° for the gait parameters measured by both methods. The Bland-Altman plot analysis indicated no significant systematic errors.

Conclusions

The feasibility of the markerless gait assessment method using the human posture estimation-based algorithm may provide reliable and valid gait analysis results for practical clinical applications.

Adjuvant Therapy for Attention in Children with ADHD Using Game-Type Digital Therapy

Children diagnosed with attention deficit hyperactivity disorder (ADHD) require early intervention and sustained treatment. This study used a game-based digital treatment planning NeuroWorld DTx to analyze the impact on attention and cognitive function in children with ADHD. Thirty children diagnosed with ADHD were recruited and subjected to a four-week NeuroWorld DTx digital treatment. To assess the impact of the digital therapeutic therapy on children's attention, we used the comprehension attention test (CAT) and Korean ADHD Rating Scale (K-ARS). Clinical global impression (CGI) and the Korean-child behavior checklist (K-CBCL 6-18) were used to examine the degree of improvement in ADHD. After four weeks, significant differences in the sensitivity and response style indices were noted, as compared with the baseline in the CAT test; in the case of K-ARS and CGI, a moderate decrease in ADHD was confirmed. The study achieved better results for the "total behavior problems" belonging to the K-CBCL assessment. Game-based digital therapy intervention can be a treatment method that elicits interest and satisfaction in children with ADHD and can be used as an adjunct to drug therapy to improve the quality of life and strengthen attention in children with symptoms of ADHD.

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.

Design of a Diagnostic System for Patient Recovery Based on Deep Learning Image Processing: For the Prevention of Bedsores and Leg Rehabilitation

Worldwide COVID-19 infections have caused various problems throughout different countries. In the case of Korea, problems related to the demand for medical care concerning wards and doctors are serious, which were already slowly worsening problems in Korea before the COVID-19 pandemic. In this paper, we propose the direction of developing a system by combining artificial intelligence technology with limited areas that do not require high expertise in the rehabilitation medical field that should be improved in Korea through the prevention of bedsores and leg rehabilitation methods. Regarding the introduction of artificial intelligence technology, medical and related laws and regulations were quite limited, so the actual needs of domestic rehabilitation doctors and advice on the hospital environment were obtained. Satisfaction with the test content was high, the degree of provision of important medical data was 95%, and the angular error was within 5 degrees and suitable for recovery confirmation.