1 citations · 2 across the 5 of their papers we have counts for
7 papers
Benchmarking Early Agitation Prediction in Community-Dwelling People with Dementia Using Multimodal Sensors and Machine Learning
Ali Abedi, Charlene H. Chu, Shehroz S. Khan
Agitation is one of the most common responsive behaviors in people living with dementia, particularly among those residing in community settings without continuous clinical supervi…
Depth-Weighted Detection of Behaviours of Risk in People with Dementia using Cameras
Pratik K. Mishra, Irene Ballester, Andrea Iaboni +4
The behavioural and psychological symptoms of dementia, such as agitation and aggression, present a significant health and safety risk in residential care settings. Many care facil…
Temporal Shift -- Multi-Objective Loss Function for Improved Anomaly Fall Detection
Stefan Denkovski, Shehroz S. Khan, Alex Mihailidis
Falls are a major cause of injuries and deaths among older adults worldwide. Accurate fall detection can help reduce potential injuries and additional health complications. Differe…
Undersampling and Cumulative Class Re-decision Methods to Improve Detection of Agitation in People with Dementia
Zhidong Meng, Andrea Iaboni, Bing Ye +4
Agitation is one of the most prevalent symptoms in people with dementia (PwD) that can place themselves and the caregiver's safety at risk. Developing objective agitation detection…
Privacy-Protecting Behaviours of Risk Detection in People with Dementia using Videos
Pratik K. Mishra, Andrea Iaboni, Bing Ye +3
People living with dementia often exhibit behavioural and psychological symptoms of dementia that can put their and others' safety at risk. Existing video surveillance systems in l…
Tracking agitation in people living with dementia in a care environment
Shehroz S. Khan, Thaejaesh Sooriyakumaran, Katherine Rich +5
Agitation is a symptom that communicates distress in people living with dementia (PwD), and that can place them and others at risk. In a long term care (LTC) environment, care staf…
DeepFall -- Non-invasive Fall Detection with Deep Spatio-Temporal Convolutional Autoencoders
Jacob Nogas, Shehroz S. Khan, Alex Mihailidis
Human falls rarely occur; however, detecting falls is very important from the health and safety perspective. Due to the rarity of falls, it is difficult to employ supervised classi…