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Found 16 papers, 5 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.
RFID-Assisted Indoor Localization Using Hybrid Wireless Data Fusion
In this paper, a hybrid section-based indoor localization method using a developed Radio Frequency Identification (RFID) tracking device and multiple IoT wireless technologies is proposed.
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.
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.
Multi-modal reward for visual relationships-based image captioning
To take advantage of visual relationships in caption generation, this paper proposes a deep neural network architecture for image captioning based on fusing the visual relationships information extracted from an image's scene graph with the spatial feature maps of the image.
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.
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.
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).
Affect-driven Ordinal Engagement Measurement from Video
We evaluated the performance of the proposed method on the only two publicly available video engagement measurement datasets, DAiSEE and EmotiW-EW, containing videos of students in online learning programs.
A new soft computing method for integration of expert's knowledge in reinforcement learn-ing problems
This paper proposes a novel fuzzy action selection method to leverage human knowledge in reinforcement learning problems.
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.
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.
