Automated system to measure Tandem Gait to assess executive functions in children

• URL: http://arxiv.org/abs/2012.08662v2
• Date: Mon, 28 Dec 2020 18:40:25 GMT
• Title: Automated system to measure Tandem Gait to assess executive functions in children
• Authors: Mohammad Zaki Zadeh, Ashwin Ramesh Babu, Ashish Jaiswal, Maria Kyrarini, Morris Bell, Fillia Makedon
• Abstract summary: This work focuses on assessing motor function in children by analyzing their gait movements. We have devised a computer vision-based assessment system that only requires a camera which makes it easier to employ in school or home environments. The results highlight the efficacy of proposed work for automating the assessment of children's performances by achieving 76.61% classification accuracy.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As mobile technologies have become ubiquitous in recent years, computer-based cognitive tests have become more popular and efficient. In this work, we focus on assessing motor function in children by analyzing their gait movements. Although there has been a lot of research on designing automated assessment systems for gait analysis, most of these efforts use obtrusive wearable sensors for measuring body movements. We have devised a computer vision-based assessment system that only requires a camera which makes it easier to employ in school or home environments. A dataset has been created with 27 children performing the test. Furthermore in order to improve the accuracy of the system, a deep learning based model was pre-trained on NTU-RGB+D 120 dataset and then it was fine-tuned on our gait dataset. The results highlight the efficacy of proposed work for automating the assessment of children's performances by achieving 76.61% classification accuracy.

Related papers

• A Data-Centric Revisit of Pre-Trained Vision Models for Robot Learning [67.72413262980272]
  Pre-trained vision models (PVMs) are fundamental to modern robotics, yet their optimal configuration remains unclear. We develop SlotMIM, a method that induces object-centric representations by introducing a semantic bottleneck. Our approach achieves significant improvements over prior work in image recognition, scene understanding, and robot learning evaluations.
  arXiv  Detail & Related papers  (2025-03-10T06:18:31Z)
• Exploring Machine Learning Engineering for Object Detection and Tracking by Unmanned Aerial Vehicle (UAV) [3.600782980481468]
  This research effort focuses on the development of a machine learning pipeline emphasizing the inclusion of assurance methods with increasing automation. A new dataset was created by collecting videos of moving object such as Roomba vacuum cleaner, emulating search and rescue (SAR) for indoor environment. After the refinement of the dataset it was trained on a second YOLOv4 and a Mask R-CNN model, which is deployed on a Parrot Mambo drone to perform real-time object detection and tracking.
  arXiv  Detail & Related papers  (2024-12-19T19:27:31Z)
• Rubric-Specific Approach to Automated Essay Scoring with Augmentation Training [0.1227734309612871]
  We propose a series of data augmentation operations that train and test an automated scoring model to learn features and functions overlooked by previous works. We achieve state-of-the-art performance in the Automated Student Assessment Prize dataset.
  arXiv  Detail & Related papers  (2023-09-06T05:51:19Z)
• Evaluation of self-supervised pre-training for automatic infant movement classification using wearable movement sensors [2.995873287514728]
  The infant wearable MAIJU provides a means to automatically evaluate infants' motor performance in out-of-hospital settings. We investigated how self-supervised pre-training improves performance of the classifiers used for analyzing MAIJU recordings.
  arXiv  Detail & Related papers  (2023-05-16T11:46:16Z)
• Self-Improving Robots: End-to-End Autonomous Visuomotor Reinforcement Learning [54.636562516974884]
  In imitation and reinforcement learning, the cost of human supervision limits the amount of data that robots can be trained on. In this work, we propose MEDAL++, a novel design for self-improving robotic systems. The robot autonomously practices the task by learning to both do and undo the task, simultaneously inferring the reward function from the demonstrations.
  arXiv  Detail & Related papers  (2023-03-02T18:51:38Z)
• Graph Neural Network based Child Activity Recognition [6.423239719448169]
  This paper presents an implementation on child activity recognition (CAR) with a graph convolution network (GCN) based deep learning model. With feature extraction and fine-tuning methods, accuracy was improved by 20%-30% with the highest accuracy being 82.24%.
  arXiv  Detail & Related papers  (2022-12-18T05:07:11Z)
• BabyNet: A Lightweight Network for Infant Reaching Action Recognition in Unconstrained Environments to Support Future Pediatric Rehabilitation Applications [5.4771139749266435]
  Action recognition is an important component to improve autonomy of physical rehabilitation devices, such as wearable robotic exoskeletons. In this paper, we introduce BabyNet, a light-weight (in terms of trainable parameters) network structure to recognize infant reaching action from off-body stationary cameras.
  arXiv  Detail & Related papers  (2022-08-09T07:38:36Z)
• ALBench: A Framework for Evaluating Active Learning in Object Detection [102.81795062493536]
  This paper contributes an active learning benchmark framework named as ALBench for evaluating active learning in object detection. Developed on an automatic deep model training system, this ALBench framework is easy-to-use, compatible with different active learning algorithms, and ensures the same training and testing protocols.
  arXiv  Detail & Related papers  (2022-07-27T07:46:23Z)
• Cross Your Body: A Cognitive Assessment System for Children [5.279475826661643]
  We created a system called Cross-Your-Body and recorded data, which is imprecise in several aspects. The videos capture real-world usage, as they record children performing tasks during real-world assessment by psychologists. It is our goal that this system will be useful in advancing research in cognitive assessment of kids.
  arXiv  Detail & Related papers  (2021-11-24T22:38:07Z)
• ALT-MAS: A Data-Efficient Framework for Active Testing of Machine Learning Algorithms [58.684954492439424]
  We propose a novel framework to efficiently test a machine learning model using only a small amount of labeled test data. The idea is to estimate the metrics of interest for a model-under-test using Bayesian neural network (BNN)
  arXiv  Detail & Related papers  (2021-04-11T12:14:04Z)
• Evaluation Toolkit For Robustness Testing Of Automatic Essay Scoring Systems [64.4896118325552]
  We evaluate the current state-of-the-art AES models using a model adversarial evaluation scheme and associated metrics. We find that AES models are highly overstable. Even heavy modifications(as much as 25%) with content unrelated to the topic of the questions do not decrease the score produced by the models.
  arXiv  Detail & Related papers  (2020-07-14T03:49:43Z)
• Automatic Gesture Recognition in Robot-assisted Surgery with Reinforcement Learning and Tree Search [63.07088785532908]
  We propose a framework based on reinforcement learning and tree search for joint surgical gesture segmentation and classification. Our framework consistently outperforms the existing methods on the suturing task of JIGSAWS dataset in terms of accuracy, edit score and F1 score.
  arXiv  Detail & Related papers  (2020-02-20T13:12:38Z)
• GraspNet: A Large-Scale Clustered and Densely Annotated Dataset for Object Grasping [49.777649953381676]
  We contribute a large-scale grasp pose detection dataset with a unified evaluation system. Our dataset contains 87,040 RGBD images with over 370 million grasp poses.
  arXiv  Detail & Related papers  (2019-12-31T18:15:11Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.