Browsing by Author "Papandrea, M"

Now showing 1 - 4 of 4

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Auto-encoder Based Data Clustering for Typical and Atypical Repetitive Child Hand Movement Pattern Identification
(SLIIT, Faculty of Engineering, 2024-10) Wedasingha, N; Samarasinhe, P; Seneviratne, L; Papandrea, M; Puiatti, A
This study is dedicated to the important task of identifying unique repetitive hand movement patterns in children, with the aim of facilitating early anomaly detection. The current body of literature lacks a comprehensive model capable of effectively discerning distinctive patterns in child repetitive hand movements. To address this gap, our innovative approach employs autoencoders to efficiently compress intricate data and extract latent features from a dataset with inherent limitations. By utilizing clustering techniques, we analyze these features to reveal distinct behaviors associated with child hand movements. Despite the challenges posed by binary annotated datasets, our model demonstrates outstanding performance in categorizing movements into four distinct types, thereby providing valuable insights into the intricate landscape of child hand movement patterns. Statistical assessments further underscore the superiority of our autoencoder, achieving a mean Bayesian value of 0.112, outperforming state-of-the-art algorithms in this domain. Subsequent in-depth analysis exposes notable inter-cluster patterns, elucidating transitions from typical to atypical behavior in child hand movements. This research constitutes a significant advancement in the field of child hand movement pattern analysis, offering a powerful and sophisticated tool for healthcare professionals and researchers alike. The automation capabilities embedded in our model empower these professionals to address childhood behavioral disorders more effectively and efficiently. In essence, our research not only contributes to the enhancement of early anomaly detection systems but also serves as a valuable resource for professionals engaged in child healthcare and behavioral research, facilitating a deeper understanding of these nuanced patterns.
Embargo
Autoencoder based data clustering for identifying anomalous repetitive hand movements, and behavioral transition patterns in children
(Springer Science and Business Media Deutschland GmbH, 2025-01-21) Wedasingha, N; Samarasinghe, P; Senevirathna, L; Papandrea, M; Puiatti, A
The analysis of repetitive hand movements and behavioral transition patterns holds particular significance in detecting atypical behaviors in early child development. Early recognition of these behaviors holds immense promise for timely interventions, which can profoundly impact a child’s well-being and future prospects. However, the scarcity of specialized medical professionals and limited facilities has made detecting these behaviors and unique patterns challenging using traditional manual methods. This highlights the necessity for automated tools to identify anomalous repetitive hand movements and behavioral transition patterns in children. Our study aimed to develop an automated model for the early identification of anomalous repetitive hand movements and the detection of unique behavioral patterns. Utilizing autoencoders, self-similarity matrices, and unsupervised clustering algorithms, we analyzed skeleton and image-based features, repetition count, and frequency of repetitive child hand movements. This approach aimed to distinguish between typical and atypical repetitive hand movements of varying speeds, addressing data limitations through dimension reduction. Additionally, we aimed to categorize behaviors into clusters beyond binary classification. Through experimentation on three datasets (Hand Movements in Wild, Updated Self-Stimulatory Behaviours, Autism Spectrum Disorder), our model effectively differentiated between typical and atypical hand movements, providing insights into behavioral transitional patterns. This aids the medical community in understanding the evolving behaviors in children. In conclusion, our research addresses the need for early detection of atypical behaviors through an automated model capable of discerning repetitive hand movement patterns. This innovation contributes to early intervention strategies for neurological conditions
Embargo
Child Head Gesture Classification through Transformers
(Institute of Electrical and Electronics Engineers Inc., 2022-11-04) Wedasingha, N; Samarasinghe, P; Singarathnam, D; Papandrea, M; Puiatti, A; Seneviratne, L
This paper proposes a transformer network for head pose classification (HPC) which outperforms the existing SoA for HPC. This robust model is then extended to overcome the limited child data challenge by applying transfer learning resulting in an accuracy of 95.34% for child HPC in the wild.
Embargo
Skeleton Based Periodicity Analysis of Repetitive Actions
(IEEE, 2022-04-07) Wedasingha, N; Samarasinghe, P; Seneviratne, L; Puiatti, A; Papandrea, M; Dhanayaka, D
This paper investigates the problem of detecting and recognizing repetitive actions performed by a human. Repetitive action analysis play a major role in detecting many behavioral disorders. In this work, we present a robust framework for detecting and recognizing repetitive actions performed by a human subject based on periodic and aperiodic action analysis. Our framework uses focal joints in the human skeleton for the analysis of repetitive actions which are substantiated by the principles of human anatomy and physiology. Using Non-deterministic Finite Automata (NFA) techniques, in this paper, we introduce a novel model to transform repetitive action count to differentiate the periodicity in human action. Experimental results on a dataset consisting of 371 video clips show that our algorithm outperforms the state-of-art (RepNet) [1] in simultaneous multiple repetitive action counts. Further, while the proposed model and RepNet give comparable results in counting periodic repetitive actions, our model performance surpass RepNet significantly on analysing non-periodic repetitive behavior.