Search Results for author:
Found 15 papers, 6 papers with code
MotionAGFormer: Enhancing 3D Human Pose Estimation with a Transformer-GCNFormer Network
Our proposed GCNFormer module exploits the local relationship between adjacent joints, outputting a new representation that is complementary to the transformer output.
Ranked #1 on
3D Human Pose Estimation
on MPI-INF-3DHP
Pose2Gait: Extracting Gait Features from Monocular Video of Individuals with Dementia
In this work we train a deep neural network to map from a two dimensional pose sequence, extracted from a video of an individual walking down a hallway toward a wall-mounted camera, to a set of three-dimensional spatiotemporal gait features averaged over the walking sequence.
Large Language Models are Fixated by Red Herrings: Exploring Creative Problem Solving and Einstellung Effect using the Only Connect Wall Dataset
In this paper we present the novel Only Connect Wall (OCW) dataset and report results from our evaluation of selected pre-trained language models and LLMs on creative problem solving tasks like grouping clue words by heterogeneous connections, and identifying correct open knowledge domain connections in respective groups.
Ranked #1 on
Only Connect Walls Dataset Task 1 (Grouping)
on OCW
(# Correct Groups metric, using extra
training data)
Only Connect Walls Dataset Task 1 (Grouping)
Only Connect Walls Dataset Task 2 (Connections)
Automated Temporal Segmentation of Orofacial Assessment Videos
Computer vision techniques can help automate or partially automate clinical examination of orofacial impairments to provide accurate and objective assessments.
Estimating Parkinsonism Severity in Natural Gait Videos of Older Adults with Dementia
This work leverages novel spatial-temporal graph convolutional network (ST-GCN) architectures and training procedures to predict clinical scores of parkinsonism in gait from video of individuals with dementia.
Unobtrusive Pain Monitoring in Older Adults with Dementia using Pairwise and Contrastive Training
However, existing computer vision techniques for pain detection are not validated on faces of older adults or people with dementia, and this population is not represented in existing facial expression datasets of pain.
Ranked #1 on
Pain Intensity Regression
on UNBC-McMaster ShoulderPain dataset
(Pearson Correlation Coefficient metric)
Estimation of Orofacial Kinematics in Parkinson's Disease: Comparison of 2D and 3D Markerless Systems for Motion Tracking
We are developing an automated system for assessment of orofacial function in PD that can be used in-home or in-clinic and can provide useful and objective clinical information that informs disease management.
Toward an Automatic System for Computer-Aided Assessment in Facial Palsy
Objective: To develop an ML algorithm for accurate facial landmarks localization in photographs of facial palsy patients, and use it as part of an automated computer-aided diagnosis system.
Limitations and Biases in Facial Landmark Detection -- An Empirical Study on Older Adults with Dementia
We perform our evaluation not only on frontal faces but also on profile faces and in various regions of the face.
A Hybrid Instance-based Transfer Learning Method
Transfer learning can help overcome this issue by transferring the knowledge from readily available datasets (source) to a new dataset (target).
Learning to Unlearn: Building Immunity to Dataset Bias in Medical Imaging Studies
As a result, machine learning models do not generalize even when trained on imaging datasets that were captured to study the same variable of interest.
Subspace Selection to Suppress Confounding Source Domain Information in AAM Transfer Learning
We propose a subspace transfer learning method, in which we select a subspace from the source that best describes the target space.
Vision-Based Assessment of Parkinsonism and Levodopa-Induced Dyskinesia with Deep Learning Pose Estimation
Conclusion: This paper presents the first application of deep learning for vision-based assessment of parkinsonism and LID and demonstrates promising performance for the future translation of deep learning to PD clinical practices.
Video Analysis of "YouTube Funnies" to Aid the Study of Human Gait and Falls - Preliminary Results and Proof of Concept
The analysis explores: computing spatiotemporal parameters of gait in a video captured from an arbitrary viewpoint; the relationship between parameters of gait from the last few steps before the obstacle and falling vs. not falling; and the predictive capacity of a multivariate model in predicting a fall in the presence of an unexpected obstacle.
Detecting Unseen Falls from Wearable Devices using Channel-wise Ensemble of Autoencoders
We propose two methods for automatic tightening of reconstruction error from only the normal activities for better identification of unseen falls.
