Mv
M.J.F. van Oort
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2 records found
1
In-Ear Human-Computer Interaction
Using EarMag to Detect Head and Jaw Movements
This thesis explores the use of in-ear magnetosensing (EarMag) as a novel sensing technique for detecting jaw and head movements in the context of human-computer interaction. While prior work has explored the use of acoustic, inertial, and visual sensing, the potential of EarMag remains to be explored. As a proof of concept, 17 orofacial physiotherapy-related exercises were collected from 21 participants using EarMag-enabled earables. A soft voting ensemble of support vector machine and random forest achieved 76% accuracy for five exercises on an unseen test set of ten users. While individual anatomical differences pose challenges for generalization, this work highlights the potential of EarMag for applications such as assistive technologies, silent speech interfaces, and biosignal tracking.
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This thesis explores the use of in-ear magnetosensing (EarMag) as a novel sensing technique for detecting jaw and head movements in the context of human-computer interaction. While prior work has explored the use of acoustic, inertial, and visual sensing, the potential of EarMag remains to be explored. As a proof of concept, 17 orofacial physiotherapy-related exercises were collected from 21 participants using EarMag-enabled earables. A soft voting ensemble of support vector machine and random forest achieved 76% accuracy for five exercises on an unseen test set of ten users. While individual anatomical differences pose challenges for generalization, this work highlights the potential of EarMag for applications such as assistive technologies, silent speech interfaces, and biosignal tracking.
Image-Based Video Search Engine
Feature Extraction
Bachelor thesis
(2022)
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M.J.F. van Oort, K.A. Hoogeveen, J.H.G. Dauwels, L.S. Pakula, N. Llombart Juan
One of the main problems with Instance-Level Image Retrieval in video data is that for query videos with multiple objects of the same instance, extracting features from keyframes of this query video is time consuming. This thesis aims to solve this problem by implementing a Convolutional Neural Network based approach, which significantly reduces the extraction time and increases the accuracy. After analysing multiple methods, Second-Order Loss and Attention for image Retrieval (SOLAR) was found to be the most promising method. SOLAR will be tested based on three performance metrics: the mean average precision, the recall and the extraction time per image. The performance will be evaluated based on a selection of videos and images from a dataset provided by Dr. Andrea Nanetti from the Engineering Historical Memory project. For this dataset SOLAR achieved a mean average precision of 83 %, a recall of 85 % and an extraction time of 0.73 seconds per image. To conclude, SOLAR is specialised in detecting and describing landmarks due to its pre-trained model, but for a more general case the backbone model should be trained differently which will increase the accuracy. Future work could also include speed improvement by looking at object detection methods.
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One of the main problems with Instance-Level Image Retrieval in video data is that for query videos with multiple objects of the same instance, extracting features from keyframes of this query video is time consuming. This thesis aims to solve this problem by implementing a Convolutional Neural Network based approach, which significantly reduces the extraction time and increases the accuracy. After analysing multiple methods, Second-Order Loss and Attention for image Retrieval (SOLAR) was found to be the most promising method. SOLAR will be tested based on three performance metrics: the mean average precision, the recall and the extraction time per image. The performance will be evaluated based on a selection of videos and images from a dataset provided by Dr. Andrea Nanetti from the Engineering Historical Memory project. For this dataset SOLAR achieved a mean average precision of 83 %, a recall of 85 % and an extraction time of 0.73 seconds per image. To conclude, SOLAR is specialised in detecting and describing landmarks due to its pre-trained model, but for a more general case the backbone model should be trained differently which will increase the accuracy. Future work could also include speed improvement by looking at object detection methods.