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All Journal International Journal of Advances in Intelligent Informatics Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI)
Mohamad Yani
Department Of Computer Engineering, Telkom University, Surabaya Campus, Surabaya, Indonesia
Computer Science & IT Education Engineering

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An efficient activity recognition for homecare robots from multi-modal communication dataset Mohamad Yani; Yamada Nao; Chyan Zheng Siow; Kubota Naoyuki
International Journal of Advances in Intelligent Informatics Vol 9, No 1 (2023): March 2023
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v9i1.903

Abstract

Human environments are designed and managed by humans for humans. Thus, adding robots to interact with humans and perform specific tasks appropriately is an essential topic in robotics research. In recent decades, object recognition, human skeletal, and face recognition frameworks have been implemented to support the tasks of robots. However, recognition of activities and interactions between humans and surrounding objects is an ongoing and more challenging problem. Therefore, this study proposed a graph neural network (GNN) approach to directly recognize human activity at home using vision and speech teaching data. Focus was given to the problem of classifying three activities, namely, eating, working, and reading, where these activities were conducted in the same environment. From the experiments, observations, and analyses, this proved to be quite a challenging problem to solve using only traditional convolutional neural networks (CNN) and video datasets. In the proposed method, an activity classification was learned from a 3D detected object corresponding to the human position. Next, human utterances were used to label the activity from the collected human and object 3D positions. The experiment, involving data collection and learning, was demonstrated by using human-robot communication. It was shown that the proposed method had the shortest training time of 100.346 seconds with 6000 positions from the dataset and was able to recognize the three activities more accurately than the deep layer aggregation (DLA) and X3D networks with video datasets.
Static Sign Language Classification Using RGB, Skeleton Image, and Fusion Representations: A Cross-Dataset Evaluation Moch. Iskandar Riansyah; Mohamad Yani; Ubaidillah Umar; Helmy Widyantara
Jurnal Nasional Pendidikan Teknik Informatika: JANAPATI Vol. 15 No. 2 (2026)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v15i2.114587

Abstract

This study investigates static sign language classification by examining three image-based input representations, namely RGB images, skeleton images, and RGB+skeleton fusion, using two datasets with different visual characteristics. The first dataset reflects a relatively controlled acquisition setting, whereas the second dataset contains more complex background and visual variations. As an initial baseline, eight pretrained convolutional neural network (CNN) architectures were evaluated, and two representative models were subsequently selected for more detailed analysis. The baseline evaluation indicates that ResNet50 and EfficientNetB0 achieve the most competitive performance when RGB images are used as input. Further analysis of input representations shows that skeleton images are highly effective, particularly on the more challenging dataset, while RGB+skeleton fusion does not consistently improve classification performance. The cross-dataset evaluation further reveals a considerable performance drop across all configurations, suggesting the presence of a strong domain shift between the two datasets. In the A-B scenario, EfficientNetB0 with RGB input yields the best results, while in the B-A scenario, EfficientNetB0 with skeleton input shows the most stable performance. These findings indicate that the most effective input representation depends on the direction of domain transfer and that high intra-dataset performance does not necessarily reflect good generalization capability.
Co-Authors Chyan Zheng Siow Helmy Widyantara Kubota Naoyuki Moch. Iskandar Riansyah Ubaidillah Umar Yamada Nao