Search Results for author:
Found 33 papers, 10 papers with code
Engagement Measurement Based on Facial Landmarks and Spatial-Temporal Graph Convolutional Networks
It uses facial landmarks, which carry no personally identifiable information, extracted from videos via the MediaPipe deep learning solution.
Rehabilitation Exercise Quality Assessment through Supervised Contrastive Learning with Hard and Soft Negatives
Exercise-based rehabilitation programs have proven to be effective in enhancing the quality of life and reducing mortality and rehospitalization rates.
Domain-Specific Deep Learning Feature Extractor for Diabetic Foot Ulcer Detection
Diabetic Foot Ulcer (DFU) is a condition requiring constant monitoring and evaluations for treatment.
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.
Synthesizing Diabetic Foot Ulcer Images with Diffusion Model
The findings highlight the potential of diffusion models for generating synthetic DFU images and their impact on medical training programs and research in wound detection and classification.
Cross-Modal Video to Body-joints Augmentation for Rehabilitation Exercise Quality Assessment
This paper introduces a novel approach to assessing the quality of rehabilitation exercises using RGB video.
Supervised and Unsupervised Deep Learning Approaches for EEG Seizure Prediction
We further develop novel unsupervised deep learning approaches to train the models on only normal EEG, and detecting pre-seizure EEG as an anomalous event.
Rehabilitation Exercise Repetition Segmentation and Counting using Skeletal Body Joints
This paper presents a novel approach for segmenting and counting the repetitions of rehabilitation exercises performed by patients, based on their skeletal body joints.
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.
Bag of States: A Non-sequential Approach to Video-based Engagement Measurement
Compared to the existing sequential and spatiotemporal approaches for engagement measurement, the proposed non-sequential approach improves the state-of-the-art results.
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.
Step Counting with Attention-based LSTM
One measure of physical activity, the step count, is well known as a predictor of long-term morbidity and mortality.
Detecting Disengagement in Virtual Learning as an Anomaly using Temporal Convolutional Network Autoencoder
Student engagement is an important factor in meeting the goals of virtual learning programs.
MAISON -- Multimodal AI-based Sensor platform for Older Individuals
In this paper, we propose MAISON, a scalable cloud-based platform of commercially available smart devices capable of collecting desired multimodal sensor data from older adults and patients living in their own homes.
Inconsistencies in the Definition and Annotation of Student Engagement in Virtual Learning Datasets: A Critical Review
The reviewed SE datasets used very diverse and inconsistent definitions and annotation protocols.
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.
Supervised Contrastive Learning for Detecting Anomalous Driving Behaviours from Multimodal Videos
We showed our results on a driver anomaly detection dataset that contains 783 minutes of video recordings of normal and anomalous driving behaviors of 31 drivers from the various top and front cameras (both depth and infrared).
Improving state-of-the-art in Detecting Student Engagement with Resnet and TCN Hybrid Network
The 2D ResNet extracts spatial features from consecutive video frames, and the TCN analyzes the temporal changes in video frames to detect the level of engagement.
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.
FedSL: Federated Split Learning on Distributed Sequential Data in Recurrent Neural Networks
Since the proposed framework is privacy-preserving, segments of multiple-segment sequential data cannot be shared between clients or between clients and server.
Anomaly Detection Approach to Identify Early Cases in a Pandemic using Chest X-rays
Therefore, we formulate the problem of identifying early cases in a pandemic as an anomaly detection problem, in which the data for healthy patients is abundantly available, whereas no training data is present for the class of interest (COVID-19 in our case).
Motion and Region Aware Adversarial Learning for Fall Detection with Thermal Imaging
A larger reconstruction error indicates the occurrence of a fall.
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.
initKmix -- A Novel Initial Partition Generation Algorithm for Clustering Mixed Data using k-means-based Clustering
Generally, these algorithms use random partition as a starting point, which tends to produce different clustering results for different runs.
Survey of state-of-the-art mixed data clustering algorithms
Mixed data comprises both numeric and categorical features, and mixed datasets occur frequently in many domains, such as health, finance, and marketing.
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.
Detecting Unseen Falls from Wearable Devices using Channel-wise Ensemble of Autoencoders
We propose two methods for automatic tightening of reconstruction error from only the normal activities for better identification of unseen falls.
Review of Fall Detection Techniques: A Data Availability Perspective
In this paper, we present a taxonomy for the study of fall detection from the perspective of availability of fall data.
Relationship between Variants of One-Class Nearest Neighbours and Creating their Accurate Ensembles
In one-class classification problems, only the data for the target class is available, whereas the data for the non-target class may be completely absent.
Detecting Falls with X-Factor Hidden Markov Models
This paper proposes an approach for the identification of falls using a wearable device in the absence of training data for falls but with plentiful data for normal ADL.
One-Class Classification: Taxonomy of Study and Review of Techniques
In this paper we present a unified view of the general problem of OCC by presenting a taxonomy of study for OCC problems, which is based on the availability of training data, algorithms used and the application domains applied.
