Semi-supervised machine learning techniques for infant motility classification
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Journal Title
Journal ISSN
Volume Title
Perustieteiden korkeakoulu |
Master's thesis
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Author
Date
2021-10-18
Department
Major/Subject
Computer Science
Mcode
SCI3042
Degree programme
Master’s Programme in Computer, Communication and Information Sciences
Language
en
Pages
83 + 4
Series
Abstract
Activity recognition (AR) is an emerging field due to its direct applications in various areas including fitness and health sector. AR involves the classification of human activities and movements into different categories. Specifically, infant activity recognition (IAR) assists in diagnosing motor disorders, such as cerebral palsy and hemiplegia and early diagnosis opens avenue for improved care and treatment. IAR can be performed by applying machine learning (ML) techniques. Most studies on infant motility classification employ supervised ML methods that require data to be manually annotated. However, manual annotation of infant motility data to different categories is extremely laborious, expensive, and prone to ambiguity. Therefore, in order to reduce the heavy reliance on manually annotated data, the aim of this thesis is to evaluate the feasibility of using semi-supervised machine learning techniques for classifying infant movement data. The infant data used for this study was acquired with inertial measurement unit (IMU) sensors attached to a wearable Maiju jumpsuit. The semi-supervised learning methods applied here first utilize unannotated data for representation learning with unsupervised learning algorithms such as autoencoder(AE) and contrastive predictive coding (CPC). These learned representations are then used to perform movement classification on annotated data using supervised learning algorithms. The optimal semi-supervised model is determined by fine-tuning the hyper-parameters based on the unweighted average F1-Score (UWAF) metric. The results of this study indicate that the UWAF scores obtained from the optimal semi-supervised models are better in comparison to end-to-end supervised models, especially for lower amounts of available annotated data. Therefore, semi-supervised learning by employing unsupervised pre-training for representation learning followed by supervised learning of the movement classes on the learned representation provides a viable and cost-effective methodology for IAR in future.Description
Supervisor
Jung, AlexThesis advisor
Airaksinen, ManuKeywords
machine learning, infant activity recognition, semi-supervised learning, deep learning, contrastive predictive coding, representation learning
