2022/08/02 更新

写真a

セオ チャンジン
瀬尾 燦振
Scopus 論文情報  
論文数: 0  Citation: 0  h-index: 2

Citation Countは当該年に発表した論文の被引用数

所属
理工学術院 創造理工学部
職名
助教
 

論文

  • Quantitative method for evaluating the coordination between sprinting motions using joint coordinates obtained from the videos and cross-correlations

    Masato Sabanai, Chanjin Seo, Hiroyuki Ogata, Jun Ohya

    ICPRAM 2021 - Proceedings of the 10th International Conference on Pattern Recognition Applications and Methods     531 - 539  2021年

     概要を見る

    This paper proposes a method for quantitatively evaluating sprinting motions using the videos of runners. Specifically, this paper explores the coordination between physical motions, which has been recognized as very important in sprinting. After detecting and normalizing the joint coordinates from sprinting videos, the cross-correlations of two windowed time-series data are calculated using the windowing cross-correlation function, and the coordination between the motions of the two joints is quantified. Experiments that use 20 subjects are conducted. As a result of classifying the cross-correlation obtained from the subjects' data into two clusters using k-means clustering, conditions in which the obtained cluster includes a high percentage of inexperienced sprinters are found. To verify whether the motions corresponding to these conditions are valid as the evaluation criterion of sprinting, Spearman's rank correlation coefficients between cross-correlations and 30-m time records are calculated. The results show a weak correlation with respect to the coordination between the elbow and knee motions. Therefore, it can be said that the cross-correlation corresponding to the coordination can be used as a quantitative criterion in sprinting.

  • Developing thermal endoscope for endoscopic photothermal therapy for peritoneal dissemination

    Mutsuki Ohara, Sohta Sanpei, Chanjin Seo, Jun Ohya, Ken Masamune, Hiroshi Nagahashi, Yuji Morimoto, Manabu Harada

    IEEE International Conference on Intelligent Robots and Systems     3040 - 3047  2020年10月

     概要を見る

    As a novel therapy for peritoneal dissemination, it is desired to actualize an endoscopic photothermal therapy, which is minimally invasive and is highly therapeutically effective. However, since the endoscopic tumor temperature control has not been actualized, conventional therapies could damage healthy tissues by overhearing. In this paper, we develop a thermal endoscope system that controls the tumor temperature so that the heated tumor gets necrotic. In fact, our thermal endoscope contains a thermal image sensor, a visible light endoscope and a laser fiber. Concerning the thermal image sensor, the conventional thermal endoscope has the problem that the diameter is too large, because the conventional endoscope loads a large thermal image sensor with high-resolution. Therefore, this paper uses a small thermal image sensor with low resolution, because the diameter of the thermal endoscope needs to be smaller than 15mm in order to be inserted into the trocar. However, this thermal image sensor is contaminated by much noise. Thus, we develop a tumor temperature control system using a feedback control and tumor temperature estimation based on Gaussian function, so that the noisy, small thermal image sensor can be used. As experimental results of the proposed endoscopic photothermal therapy for the hepatophyma carcinoma model of rats, it turns out that the tumor temperature by which the heated tumor gets necrotic can be kept stable. It can be said that our endoscopic photothermal therapy achieves a certain degree of therapy effect.

    DOI

    Scopus

    2
    被引用数
    (Scopus)
  • Classification of Aortic Stenosis Using ECG by Deep Learning and its Analysis Using Grad-CAM

    Erika Hata, Chanjin Seo, Masafumi Nakayama, Kiyotaka Iwasaki, Takaaki Ohkawauchi, Jun Ohya

    Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS   2020-July   1548 - 1551  2020年07月

     概要を見る

    This paper proposes an automatic method for classifying Aortic valvular stenosis (AS) using ECG (Electrocardiogram) images by the deep learning whose training ECG images are annotated by the diagnoses given by the medical doctor who observes the echocardiograms. Besides, it explores the relationship between the trained deep learning network and its determinations, using the Grad-CAM.In this study, one-beat ECG images for 12-leads and 4-leads are generated from ECG's and train CNN's (Convolutional neural network). By applying the Grad-CAM to the trained CNN's, feature areas are detected in the early time range of the one-beat ECG image. Also, by limiting the time range of the ECG image to that of the feature area, the CNN for the 4-lead achieves the best classification performance, which is close to expert medical doctors' diagnoses.Clinical Relevance - This paper achieves as high AS classification performance as medical doctors' diagnoses based on echocardiograms by proposing an automatic method for detecting AS only using ECG.

    DOI

    Scopus

    7
    被引用数
    (Scopus)
  • Extracting and interpreting unknown factors with classifier for foot strike types in running

    Chanjin Seo, Masato Sabanai, Yuta Goto, Koji Tagami, Hiroyuki Ogata, Kazuyuki Kanosue, Jun Ohya

    Proceedings - International Conference on Pattern Recognition     3217 - 3224  2020年

     概要を見る

    This paper proposes a method that can classify foot strike types using a deep learning model and can extract unknown factors, which enables to evaluate running motions without being influenced by biases of sports experts, using the contribution degree of input values (CDIV). Accelerometers are attached to the runner's body, and when the runner runs, a fixed camera observes the runner and acquires a video sequence synchronously with the accelerometers. To train a deep learning model for classifying foot strikes, we annotate foot strike acceleration data for RFS (Rearfoot strike) or non-RFS objectively by watching the video. To interpret the unknown factors extracted from the learned model, we calculate two CDIVs: the contributions of the resampling time and the accelerometer value to the output (foot strike type). Experiments on classifying unknown runners' foot strikes were conducted. As a common result to sport science, it is confirmed that the CDIVs contribute highly at the time of the right foot strike, and the sensor values corresponding to the right and left tibias contribute highly to classifying the foot strikes. Experimental results show the right tibia is important for classifying foot strikes. This is because many of the training data represent difference between the two foot strikes in the right tibia. As a conclusion, our proposed method could extract unknown factors from the classifier and could interpret the factors that contain similar knowledge to the prior knowledge of experts, as well as new findings that are not included in conventional knowledge.

    DOI

    Scopus

  • Understanding sprinting motion skills using unsupervised learning for stepwise skill improvements of running motion

    Chanjin Seo, Masato Sabanai, Hiroyuki Ogata, Jun Ohya

    ICPRAM 2019 - Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods     467 - 475  2019年

     概要を見る

    To improve running performances, each runner's skill, such as characteristics and habits, needs to be known, and feedback on the performance should be outputted according to the runner's skill level. In this paper, we propose a new coaching system for detecting the skill of a runner and a method of giving feedback using a sprint motion dataset. Our proposed method calculates an extracted feature to detect the skill using an autoencoder whose middle layer is an LSTM layer; we analyse the feature using hierarchical clustering, and we analyse the human joints that affect the skill. As a result of experiments, five clusters are obtained using hierarchical clustering. This paper clarifies how to detect the skill and to output feedback to achieve a level of performance one step higher than the current level.

    DOI

    Scopus

    2
    被引用数
    (Scopus)
  • Investigating relationship between running motions and skills acquired from jump trainings

    Chanjin Seo, Masato Sabanai, Hiroyuki Ogata, Jun Ohya

    icSPORTS 2019 - Proceedings of the 7th International Conference on Sport Sciences Research and Technology Support     198 - 203  2019年

     概要を見る

    To identify the difference in performers' motions, this paper investigates the relationship between running motions and the result of evaluating motions during jump training. To clarify the relationship, two experiments were performed using 17 subjects as follows: i) obtaining sequences of human joints during running to evaluate running motions, and ii) obtaining motions during jump training which could skill up the running motions. According to the result of those experiments, we confirmed that whether a running motion is good or not relies greatly on the number of acquired skills.

    DOI

    Scopus

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Misc

  • Virtual Reality環境における初心者向けスキー滑走学習システムに関する研究

    宮川峻一, 瀬尾燦振, 大橋拓未, 後藤悠太, 中新かれん, 小澤悠, 大谷淳, 彼末一之, 小方博之

    電子情報通信学会技術研究報告(Web)   120 ( 389(IMQ2020 10-35) )  2021年

    J-GLOBAL

  • 深層学習を用いた心電図からの大動脈弁狭窄症の識別法の提案とGrad-CAMを用いた分析

    秦絵里香, 瀬尾燦振, 中山雅文, 岩崎清隆, 大川内隆朗, 大谷淳

    電子情報通信学会技術研究報告   119 ( 399(MI2019 65-123)(Web) )  2020年

    J-GLOBAL

  • Virtual Realityを用いたプルーク滑走スキーの体験のためのスキーシミュレータシステムの構築

    宮川峻一, 瀬尾燦振, 大橋拓未, 後藤悠太, 中新かれん, 小澤悠, 彼末一之, 大谷淳, 小方博之

    日本スキー学会大会講演論文集   30th  2020年

    J-GLOBAL

  • 腹膜播種の光温熱治療のための熱画像の深層学習を用いた高解像度化に関する研究

    三瓶聡太, 尾原睦月, 瀬尾燦振, 正宗賢, 長橋宏, 大谷淳, 守本祐司, 原田学

    電子情報通信学会技術研究報告   119 ( 454(IMQ2019 13-68) )  2020年

    J-GLOBAL

産業財産権

  • 心電信号分析装置及び心電信号分析プログラム

    秦 絵里香, 瀬尾 燦振, 中山 雅文, 岩▲崎▼ 清隆, 大谷 淳

    特許権

    J-GLOBAL