Search Results for author:
Found 15 papers, 3 papers with code
Temporal Shift -- Multi-Objective Loss Function for Improved Anomaly Fall Detection
Anomaly detection frameworks using autoencoders and their variants can be used for fall detection due to the data imbalance that arises from the rarity and diversity of falls.
StairNet: Visual Recognition of Stairs for Human-Robot Locomotion
This motivated us to create the StairNet initiative to support the development of new deep learning models for visual sensing and recognition of stairs, with an emphasis on lightweight and efficient neural networks for onboard real-time inference.
Undersampling and Cumulative Class Re-decision Methods to Improve Detection of Agitation in People with Dementia
Agitation is one of the most prevalent symptoms in people with dementia (PwD) that can place themselves and the caregiver's safety at risk.
Skeletal Video Anomaly Detection using Deep Learning: Survey, Challenges and Future Directions
Appearance-based features can also be sensitive to pixel-based noise, straining the anomaly detection methods to model the changes in the background and making it difficult to focus on the actions of humans in the foreground.
Privacy-Protecting Behaviours of Risk Detection in People with Dementia using Videos
Our work differs from most existing approaches for video anomaly detection that focus on appearance-based features, which can put the privacy of a person at risk and is also susceptible to pixel-based noise, including illumination and viewing direction.
AI-powered Language Assessment Tools for Dementia
The main objective of this paper is to propose an approach for developing an Artificial Intelligence (AI)-powered Language Assessment (LA) tool.
Multi Visual Modality Fall Detection Dataset
From a machine learning perspective, developing an effective fall detection system is challenging because of the rarity and variability of falls.
Tracking agitation in people living with dementia in a care environment
Agitation is a symptom that communicates distress in people living with dementia (PwD), and that can place them and others at risk.
Spatio-Temporal Adversarial Learning for Detecting Unseen Falls
In this paper, we take an alternate philosophy to detect falls in the absence of their training data, by training the classifier on only the normal activities (that are available in abundance) and identifying a fall as an anomaly.
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.
DeepFall -- Non-invasive Fall Detection with Deep Spatio-Temporal Convolutional Autoencoders
Human falls rarely occur; however, detecting falls is very important from the health and safety perspective.
Bootstrapping and Multiple Imputation Ensemble Approaches for Missing Data
In this paper, we present several variants of combining single and multiple imputation with bootstrapping to create ensembles that can model uncertainty and diversity in the data, and that are robust to high missingness in the data.
Depth image hand tracking from an overhead perspective using partially labeled, unbalanced data: Development and real-world testing
We present the development and evaluation of a hand tracking algorithm based on single depth images captured from an overhead perspective for use in the COACH prompting system.
