Papers - INUZUKA Nobuhiro
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Evaluation of Employees by Generating Meeting Networks Using Entry and Exit Data Reviewed
Atsuko Mutoh, Kazuma Morikida, Koichi Moriyama, Nobuhiro Inuzuka
Proceedings of 2023 IEEE International Conference on Consumer Electronics (ICCE) 1 - 4 2023.01
Language：English Publishing type：Research paper (international conference proceedings)
Data related to various human movements are available due to the recent development of IoT devices and communication networks, and research on the use of historical data of human movements is being actively performed. In this study, we use log data on employee movement obtained from an entry/exit management system, which has been implemented in many businesses. We propose a method for creating a social network based on employee entry/exit history and observing employee performance. We focus on meeting room-related data from entry/exit data and evaluate employees using meeting network centrality indexes. According to the experimental results, the meeting network's closeness centrality best represents job position, while the meeting network's betweenness centrality best represents employee performance.
DOI： 10.1109/ICCE56470.2023.10043416
Other Link： https://ieeexplore.ieee.org/abstract/document/10043416
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Evaluation System for Martial Arts Demonstration from Smartphone Sensor Data Using Deep Neural Networks on Noisy Labels Reviewed International journal
Shohei Yamanaka, Kosuke Shima, Atsuko Mutoh, Koichi Moriyama, Tohgoroh Matsui, Nobuhiro Inuzuka
Proceedings of 2023 IEEE International Conference on Consumer Electronics (ICCE) 1 - 5 2023.01
Authorship：Last author Language：English Publishing type：Research paper (international conference proceedings)
Smartphones and smartwatches are widespread. Devices have with sensors. Research activities to obtain data from sensors to recognize sports motion and find modifications are on the rise. These studies make it possible to provide movement feedback without a coach. However, a few studies have focused on the correctness of the movement, or which parts of the movement are correct. Herein, we propose a method to evaluate the movements of a martial arts demonstration based on sensor data obtained from a smartphone. In the demonstration, several movements, such as punching and kicking, are performed. The coach considers each movement and evaluates the overall movement comprehensively. Regarding the coach evaluation, we evaluate sensor data by dividing the data and using machine learning. This method was applied to the acceleration data of taekwondo demonstrations. It was shown to reproduce the instructor's score with high accuracy.
DOI： 10.1109/ICCE56470.2023.10043511
Other Link： https://ieeexplore.ieee.org/abstract/document/10043511
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Extraction of Behavioral Patterns by Recombining Non-Negative Multiple Matrix Factorization and Clustering Results Reviewed International journal
Shogo Yasui, Atsuko Mutoh, Koichi Moriyama, Tohgoroh Matsui, and Nobuhiro Inuzuka
Proceedings of Proceedings of 2021 IEEE 10th Global Conference on Consumer Electronics (GCCE 2021) 2021.10
Language：English Publishing type：Research paper (international conference proceedings) Publisher：IEEE
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Evaluation of Martial Arts Demonstration Focusing on Peak Timing Using Acceleration Data Reviewed International journal
Shohei Yamanaka, Kosuke Shima, Atsuko Mutoh, Koichi Moriyama, Tohgoroh Matsui, and Nobuhiro Inuzuka
Proceedings of Proceedings of 2021 IEEE 10th Global Conference on Consumer Electronics (GCCE 2021) 2021.10
Language：English Publishing type：Research paper (international conference proceedings) Publisher：IEEE
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Classification of Buzzwords by Focusing on Time Trends Using Twitter Data Reviewed International journal
Juno Hashimoto, Atsuko Mutoh, Koichi Moriyama, Azusa Yokogoshi, Eiko Yoshida, Tohgoroh Matsui, and Nobuhiro Inuzuka
Proceedings of Proceedings of 2021 IEEE 10th Global Conference on Consumer Electronics (GCCE 2021) 2021.10
Language：English Publishing type：Research paper (international conference proceedings) Publisher：IEEE
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Human motion analysis using expressions of non-separated accelerometer values as character strings Reviewed International journal
Kosuke Shima, Atsuko Mutoh, Koichi Moriyama, Nobuhiro Inuzuka
Artificial Life and Robotics 26 ( 2 ) 202 - 209 2021.05
Language：English Publishing type：Research paper (scientific journal) Publisher：Springer Japan
People generally perform various activities, such as walking and running. They perform these activities with different motions. For example, walking can be performed with or without swinging shoulders, as well as staggering and swinging arms. We assume that such differences occur based on physical and mental characteristics of humans. To analyze relations between the motions and the characteristics/conditions, it is useful to group humans according to these differences. In a previous work, we proposed a method that successfully grouped humans by analyzing accelerometer data of their bodies in a specific activity with fixed timing and duration. In this study, we tackle with a problem of grouping human in generic, variable-length activities, such as walking and running. We propose a method that detects same motions from the accelerometer data with sliding windows and merges continuous same motions into a motion. The method is robust regarding the difference in timing and duration of the motion. In our conducted experiments, the proposed method classified humans into groups appropriately, the groups which are acquired by the previous method with the same data but without assuming fixed timing and fixed duration, which are assumed in the previous method. The proposed method is robust against temporally noised data generated from the data.
Other Link： https://link.springer.com/article/10.1007/s10015-020-00668-6
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Exploration Improvement by Sequential Intrinsic Reward Generator in Deep Reinforcement Learning Reviewed
Kazuhiro Murakami, Koichi Moriyama, Tohgoroh Matsui, Atsuko Mutoh, Nobuhiro Inuzuka
14 ( 1 ) 1 - 11 2021.01
Language：Japanese Publishing type：Research paper (scientific journal) Publisher：Information Processing Society of Japan
Deep reinforcement learning is working well in the environment with high dimensional states. However, it is difficult for a reinforcement learning agent to learn an optimal policy in the environment where it hardly obtain rewards. Curiosity-driven exploration is a solution that gives intrinsic rewards to the agent in unfamiliar states to encourage it for visiting various states. This work proposes Sequential Intrinsic Reward Generator (SRG), which extends curiosity-driven exploration to a sequence of states and gives the agent intrinsic rewards for unfamiliar state transitions. Due to this sequential property, SRG is promising to work well also in partially observable Markov decision processes. The result of experiments shows that SRG worked better than other methods in such environments.
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属性情報取得範囲を考慮した友人関係生成モデル Reviewed
泉優多, 武藤敦子, 森山甲一,松井藤五郎, 犬塚信博
電気学会論文誌Ｃ電子情報システム部門誌 140 ( 12 ) 2020.12
Language：Japanese Publishing type：Research paper (scientific journal) Publisher：電気学会
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Evolution of Mate-choice Copying Adapted to Environmental Changes Reviewed International journal
Kosuke Ozeki, Atsuko Mutoh, Koichi Moriyama, Tohgoroh Matsui, and Nobuhiro Inuzuka
Proc. 2020 IEEE 9th Global Conference on Consumer Electronics (GCCE 2020) 2020.10
Language：English Publishing type：Research paper (international conference proceedings) Publisher：IEEE
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A Study on Attributes Based on Heterogeneity of Egos and Surrounding Nodes for Structural Features in Social Networks
Minoru Natsume, Hayato Ishigure, Atsuko Mutoh, Koichi Moriyama, Nobuhiro Inuzuka
37 ( 3 ) 1 - 7 2020.03
Language：Japanese Publishing type：Research paper (conference, symposium, etc.)
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A Model for Activating Community Activities by Changing Leaders Using Graph Clustering
Kosuke Hamajima, Atsuko Mutoh, Tohgoroh Matsui, Koichi Moriyama, Nobuhiro Inuzuka
37 ( 4 ) 1 - 8 2020.03
Language：Japanese Publishing type：Research paper (scientific journal)
Activate of community activities plays an important role in solving regional and national problems. In communities such as neighborhood associations, community activities sometimes carry out to enrich lives of members and improve their environment. Participation in community activities is supported by members having rights and responsibilities. However, there are few people who actually participate. That's because nonparticipants can obtain the same benefit with participants. Therefore, nomination of a leader who always participates in the activities and encourages the norm consciousness of the surrounding people is important to activate activities. In this research, we propose a community activity model introduced changes of a leader. We make community activity be activated by changing a leader.
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Analysis of Changes in Behavior Patterns of Workers Using Non-Negative Multiple Matrix Factorization Reviewed International journal
Seidai Kojima, Kosuke Shima, Atsuko Mutoh, Koichi Moriyama, and Nobuhiro Inuzuka
proc. The Twenty-Fifth International Symposium on Artificial Life and Robotics 2020 (AROB 25th 2020) 2020.01
Language：English Publishing type：Research paper (scientific journal)
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Human Motion Analysis Using Expressions of Non-Separated Accelerometer Values as Character Strings Reviewed International journal
Kosuke Shima, Atsuko Mutoh, Koichi Moriyama, and Nobuhiro Inuzuka
Proc. The Twenty-Fifth International Symposium on Artificial Life and Robotics 2020 (AROB 25th 2020) 2020.01
Language：English Publishing type：Research paper (international conference proceedings)
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Simplification of Concept Lattice by Decomposition using Degree of Attribute Relevance Reviewed
2019.12
Language：Japanese Publishing type：Research paper (scientific journal)
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An Analysis of the Relationship between Users' Behavior Patterns and Their Attributes Information Using Non-negative Multiple Matrix Factorization and Decision Tree Learning Reviewed
2019.12
Language：Japanese Publishing type：Research paper (scientific journal)
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Evaluation of Decomposition Methods for Concept Lattices Based on Implications Reviewed
2019.12
Language：Japanese Publishing type：Research paper (scientific journal)
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Evolutionary model of mating time for I. senegalensis by intrasexual competition Reviewed
2019.12
Language：Japanese Publishing type：Research paper (scientific journal)
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Safe Reinforcement Learning in Continuous State Spaces Reviewed
Takumi Umemoto, Tohgoroh Matsui, Atsuko Mutoh, Koichi Moriyama, Nobuhiro Inuzuka
2019 IEEE 8th Global Conference on Consumer Electronics (GCCE 2019) 2019.10
Language：English Publishing type：Research paper (international conference proceedings)
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Running Reinforcement Learning Agents on GPU for Many Simulations of Two-Person Simultaneous Games Reviewed International journal
Koichi Moriyama, Yoshiya Kuroki, Atsuko Mutoh, Tohgoroh Matsui, and Nobuhiro Inuzuka
2019.10
Language：English Publishing type：Research paper (international conference proceedings)
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Multi-Objective Safe Reinforcement Learning -The Relationship Between Multi-Objective Reinforcement Learning and Safe Reinforcement Learning Reviewed International journal
Naoto Horie, Tohgoroh Matsui, Koichi Moriyama, Atsuko Mutoh, Nobuhiro Inuzuka
24 ( 3 ) 352 - 359 2019.09
Language：English Publishing type：Research paper (scientific journal) Publisher：Springer
Reinforcement learning (RL) is a learning method that learns actions based on trial and error. Recently, multi-objective reinforcement learning (MORL) and safe reinforcement learning (SafeRL) have been studied. The objective of conventional RL is to maximize the expected rewards; however, this may cause a fatal state because safety is not considered. Therefore, RL methods that consider safety during or after learning have been proposed. SafeRL is similar to MORL because it considers two objectives, i.e., maximizing expected rewards and satisfying safety constraints. However, to the best of our knowledge, no study has investigated the relationship between MORL and SafeRL to demonstrate that the SafeRL method can be applied to MORL tasks. This paper combines MORL with SafeRL and proposes a method for Multi-Objective SafeRL (MOSafeRL). We applied the proposed method to resource gathering task, which is a standard task used in MORL test cases.
