1 code implementation • 28 May 2024 • Vida Adeli, Soroush Mehraban, Irene Ballester, Yasamin Zarghami, Andrea Sabo, Andrea Iaboni, Babak Taati
Lastly, we establish a benchmark for the analysis of skeleton-based motion encoder models in clinical settings.
1 code implementation • 22 Aug 2023 • Caroline Malin-Mayor, Vida Adeli, Andrea Sabo, Sergey Noritsyn, Carolina Gorodetsky, Alfonso Fasano, Andrea Iaboni, Babak Taati
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.
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 • 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.
2 code implementations • 7 May 2021 • Andrea Sabo, Sina Mehdizadeh, Andrea Iaboni, Babak Taati
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.
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.