no code implementations • 6 Nov 2023 • Stefan Denkovski, Shehroz S. Khan, Alex Mihailidis
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.
no code implementations • 31 Oct 2023 • Andrew Garrett Kurbis, Dmytro Kuzmenko, Bogdan Ivanyuk-Skulskiy, Alex Mihailidis, Brokoslaw Laschowski
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.
no code implementations • 7 Feb 2023 • Zhidong Meng, Andrea Iaboni, Bing Ye, Kristine Newman, Alex Mihailidis, Zhihong Deng, Shehroz S. Khan
Agitation is one of the most prevalent symptoms in people with dementia (PwD) that can place themselves and the caregiver's safety at risk.
no code implementations • 31 Dec 2022 • Pratik K. Mishra, Alex Mihailidis, Shehroz S. Khan
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.
no code implementations • 20 Dec 2022 • Pratik K. Mishra, Andrea Iaboni, Bing Ye, Kristine Newman, Alex Mihailidis, Shehroz S. Khan
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.
no code implementations • 13 Sep 2022 • Mahboobeh Parsapoor, Muhammad Raisul Alam, Alex Mihailidis
The main objective of this paper is to propose an approach for developing an Artificial Intelligence (AI)-powered Language Assessment (LA) tool.
1 code implementation • 25 Jun 2022 • Stefan Denkovski, Shehroz S. Khan, Brandon Malamis, Sae Young Moon, Bing Ye, Alex Mihailidis
From a machine learning perspective, developing an effective fall detection system is challenging because of the rarity and variability of falls.
no code implementations • 15 Apr 2021 • Shehroz S. Khan, Thaejaesh Sooriyakumaran, Katherine Rich, Sofija Spasojevic, Bing Ye, Kristine Newman, Andrea Iaboni, Alex Mihailidis
Agitation is a symptom that communicates distress in people living with dementia (PwD), and that can place them and others at risk.
no code implementations • 19 May 2019 • Shehroz S. Khan, Jacob Nogas, Alex Mihailidis
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.
no code implementations • 17 May 2019 • Azin Asgarian, Shun Zhao, Ahmed B. Ashraf, M. Erin Browne, Kenneth M. Prkachin, Alex Mihailidis, Thomas Hadjistavropoulos, Babak Taati
We perform our evaluation not only on frontal faces but also on profile faces and in various regions of the face.
1 code implementation • 30 Aug 2018 • Jacob Nogas, Shehroz S. Khan, Alex Mihailidis
Human falls rarely occur; however, detecting falls is very important from the health and safety perspective.
1 code implementation • 1 Feb 2018 • Shehroz S. Khan, Amir Ahmad, Alex Mihailidis
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.
no code implementations • 6 Sep 2014 • Stephen Czarnuch, Alex Mihailidis
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.