Prediction of joint moments from kinematics using machine learning in children with congenital talipes equino varus and typically developing peers.

Background: Understanding joint loading and the crucial role of joint moments is essential for developing treatment strategies in gait analysis, which often requires the precise estimation of joint moments through an inverse dynamic approach. This process necessitates the use of a force plate synchronized with a motion capture system. However, effectively capturing ground reaction force in typically developing (TD) children and those with congenital talipes equino varus (CTEV) presents challenges, while the availability and high cost of additional force plates pose additional challenges.

Classification of attention deficit hyperactivity disorder using machine learning on an EEG dataset.

The neurodevelopmental disorder, Attention Deficit Hyperactivity Disorder (ADHD), frequently affecting youngsters, is characterized by persistent patterns of inattention, hyperactivity, and impulsivity, the etiology of which may involve a variety of genetic, environmental, and neurological factors. Electroencephalography (EEG) measures the electrical activity in the brain through neuronal activity, which is a function of cognitive processes. In this study, a previously recorded sample set of 121 children containing unbiased data from both ADHD and control group classes and EEG signals were analyzed to classify the ADHD patients.

Electroencephalogram (EEG) based prediction of attention deficit hyperactivity disorder (ADHD) using machine learning.

"Attention-Deficit Hyperactivity Disorder (ADHD)" is a neuro-developmental disorder in children under 12 years old. Learning deficits, anxiety, depression, sensory processing disorder, and oppositional defiant disorder are the most frequent comorbidities of ADHD. This research focuses on ADHD in children, considering its common occurrence and frequent coexistence with other mental disorders.

Finite element analysis of a healthy knee joint at deep squatting for the study of tibiofemoral and patellofemoral contact.

Background: In non-western countries, deep squatting is a daily activity, and prolonged deep squatting is common among occupational squatters. Household tasks, taking a bath, socializing, using toilets, and performing religious acts are among the activities frequently carried out while squatting by the Asian population. High knee loading is responsible for a knee injury and osteoarthritis.

Estimation and Comparison of Knee Joint Contact Forces During Heel Contact and Heel Rise Deep Squatting.

Background: Increased knee flexion is required for deep squatting in the daily life of the non-western population as well as in many sports activities. The purpose of this study was to estimate as well as to compare knee joint contact forces during heel contact (HC) and heel rise (HR) deep squatting in 10 healthy young Indian participants.

Materials And Methods: Kinematic data were captured using a 12-camera Motion Analysis system.

Implementation of a visual feedback system for motion management during radiation therapy.

Purpose: To describe the details of an in-house video goggles feedback system assembled from several commercially available components. The objective of this paper is to share our experience with this system, provide details on the equipment needed, system assembly, patient set up and user settings on some components.

Materials And Methods: The system consisted of goggles (FPView3DHD, ITV, USA), RJ45(Registered Jack) to Digital Visual Interface (DVI) converter (Tripplite), DVI to HDMI converters, Local Area Network(LAN) cable, HDMI and power extender cables.