research
∙
06/15/2023
Cross-Modal Video to Body-joints Augmentation for Rehabilitation Exercise Quality Assessment
Exercise-based rehabilitation programs have been shown to enhance qualit...
share
research
∙
04/24/2023
Supervised and Unsupervised Deep Learning Approaches for EEG Seizure Prediction
Epilepsy affects more than 50 million people worldwide, making it one of...
research
∙
04/19/2023
Rehabilitation Exercise Repetition Segmentation and Counting using Skeletal Body Joints
Physical exercise is an essential component of rehabilitation programs t...
research
∙
02/07/2023
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 ...
research
∙
01/17/2023
Bag of States: A Non-sequential Approach to Video-based Engagement Measurement
Automatic measurement of student engagement provides helpful information...
research
∙
12/31/2022
Skeletal Video Anomaly Detection using Deep Learning: Survey, Challenges and Future Directions
The existing methods for video anomaly detection mostly utilize videos c...
research
∙
12/20/2022
Privacy-Protecting Behaviours of Risk Detection in People with Dementia using Videos
People living with dementia often exhibit behavioural and psychological ...
research
∙
11/13/2022
Detecting Disengagement in Virtual Learning as an Anomaly
Student engagement is an important factor in meeting the goals of virtua...
research
∙
11/07/2022
MAISON – Multimodal AI-based Sensor platform for Older Individuals
There is a global aging population requiring the need for the right tool...
research
∙
08/09/2022
Inconsistencies in Measuring Student Engagement in Virtual Learning – A Critical Review
In recent years, virtual learning has emerged as an alternative to tradi...
research
∙
06/25/2022
Multi Visual Modality Fall Detection Dataset
Falls are one of the leading cause of injury-related deaths among the el...
research
∙
09/09/2021
Modified Supervised Contrastive Learning for Detecting Anomalous Driving Behaviours
Detecting distracted driving behaviours is important to reduce millions ...
research
∙
04/20/2021
Improving state-of-the-art in Detecting Student Engagement with Resnet and TCN Hybrid Network
Automatic detection of students' engagement in online learning settings ...
research
∙
11/06/2020
FedSL: Federated Split Learning on Distributed Sequential Data in Recurrent Neural Networks
Federated Learning (FL) and Split Learning (SL) are privacy-preserving M...
research
∙
10/06/2020
COVIDomaly: A Deep Convolutional Autoencoder Approach for Detecting Early Cases of COVID-19
As of September 2020, the COVID-19 pandemic continues to devastate the h...
research
∙
05/19/2019
Spatio-Temporal Adversarial Learning for Detecting Unseen Falls
Fall detection is an important problem from both the health and machine ...
research
∙
01/31/2019
A Novel Initial Clusters Generation Method for K-means-based Clustering Algorithms for Mixed Datasets
Mixed datasets consist of numeric and categorical attributes. Various K-...
research
∙
11/11/2018
A Survey of Mixed Data Clustering Algorithms
Most of the datasets normally contain either numeric or categorical feat...
research
∙
08/30/2018
DeepFall -- Non-invasive Fall Detection with Deep Spatio-Temporal Convolutional Autoencoders
Human falls rarely occur; however, detecting falls is very important fro...
research
∙
02/01/2018
Bootstrapping and Multiple Imputation Ensemble Approaches for Missing Data
Presence of missing values in a dataset can adversely affect the perform...
research
∙
10/12/2016
Detecting Unseen Falls from Wearable Devices using Channel-wise Ensemble of Autoencoders
A fall is an abnormal activity that occurs rarely, so it is hard to coll...
research
∙
04/08/2015
Detecting Falls with X-Factor Hidden Markov Models
Identification of falls while performing normal activities of daily livi...
research
∙
11/30/2013