2018 11th International Conference on Human System Interaction (HSI)最新文献

Digits Recognition with Quadrant Photodiode and Convolutional Neural Network 基于象限光电二极管和卷积神经网络的数字识别

2018 11th International Conference on Human System Interaction (HSI) Pub Date : 2018-07-04 DOI: 10.1109/HSI.2018.8431246

Kamil Janczyk, Krzysztof Czuszyński, J. Rumiński

{"title":"Digits Recognition with Quadrant Photodiode and Convolutional Neural Network","authors":"Kamil Janczyk, Krzysztof Czuszyński, J. Rumiński","doi":"10.1109/HSI.2018.8431246","DOIUrl":"https://doi.org/10.1109/HSI.2018.8431246","url":null,"abstract":"In this paper we have investigated the capabilities of a quadrant photodiode based gesture sensor in the recognition of digits drawn in the air. The sensor consisting of 4 active elements, 4 LEDs and a pinhole was considered as input interface for both discrete and continuous gestures. Index finger and a round pointer were used as navigating mediums for the sensor. Experiments performed with 5 volunteers allowed to record 300 examples of each digit from 0 to 9, which were drawn in the air. Digits were converted from a list of recorded coordinates into images processed as in the MNIST database. Three approaches for recognition of digits recorded by quadrant photodiode were considered: convolutional neural network trained only on examples from the MNIST database, network trained on mixed data of MNIST with examples recorded using quadrant photodiode (4/1 proportions) and trained on the MNIST with examples recorded using the elaborated sensor but after the arbitral rejection of 20% of worst quality data (4/1 proportions preserved). The application of the third approach in comparison to the first one allowed to increase the overall accuracy of digits classification from 34.4% to 86% for testing data recorded with the use of the pointer and from 32% to 81.2% for data recorded with the use of a finger (for 50Hz sampling frequency).","PeriodicalId":441117,"journal":{"name":"2018 11th International Conference on Human System Interaction (HSI)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123868699","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Integration in Multichannel Emotion Recognition 多通道情感识别的集成

2018 11th International Conference on Human System Interaction (HSI) Pub Date : 2018-07-01 DOI: 10.1109/HSI.2018.8431343

Grzegorz Brodny, A. Landowska

引用次数: 3

Concept Verification of Antagonistic Pneumatic Driven and Inflatable Arm Joint 对抗性气动驱动和充气臂关节的概念验证

2018 11th International Conference on Human System Interaction (HSI) Pub Date : 2018-07-01 DOI: 10.1109/HSI.2018.8431215

Hirofusa Ogasawara, S. Yokota, A. Matsumoto, D. Chugo, H. Hashimoto

引用次数: 0

Design Rules, Implementation and Testing of User Interfaces for Mixed Reality Applications 混合现实应用中用户界面的设计规则、实现和测试

2018 11th International Conference on Human System Interaction (HSI) Pub Date : 2018-07-01 DOI: 10.1109/HSI.2018.8431347

Agata Lis-Marciniak, Jan Tomiakowski, Paweł Kapusta

引用次数: 0

Classifying Cognitive Workload Based on Brain Waves Signal in the Arithmetic Tasks' Study 基于脑电波信号的算术任务认知负荷分类研究

2018 11th International Conference on Human System Interaction (HSI) Pub Date : 2018-07-01 DOI: 10.1109/HSI.2018.8431105

M. Plechawska-Wójcik, M. Borys, Mikhail Tokovarov, Monika Kaczorowska, Kinga Wesolowska, Martyna Wawrzyk

{"title":"Classifying Cognitive Workload Based on Brain Waves Signal in the Arithmetic Tasks' Study","authors":"M. Plechawska-Wójcik, M. Borys, Mikhail Tokovarov, Monika Kaczorowska, Kinga Wesolowska, Martyna Wawrzyk","doi":"10.1109/HSI.2018.8431105","DOIUrl":"https://doi.org/10.1109/HSI.2018.8431105","url":null,"abstract":"Cognitive workload is a quantitative usage measure of the limited amount of working memory. Its measuring is of great importance for understanding human mental effort processing, evaluating information systems or supporting diagnosis and treatment of patients. The paper presents the results of cognitive workload classification of electroencephalographic (EEG) data. The performed study covered arithmetic tasks realised in several intervals with the increasing difficulty level. Brain waves data in the form of EEG signal were gathered and processed in the form of frequency spectra. The paper discusses the process of features selection performed with several methods including ranking methods (K-Fisher), Feature Selection By Eigenvector Centrality (ECFS) and Mitinffs mutual information-based approach. What is more, the paper presents results of participant cognitive workload classification based on such methods as Support Vector Machines (SVM), boosted trees and k-nearest neighbours (KNN) algorithm. The paper discusses the efficiency of features selection methods and accuracy of applied classification methods.","PeriodicalId":441117,"journal":{"name":"2018 11th International Conference on Human System Interaction (HSI)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124055289","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

ANFIS Post-Processing for Real Time Gait Detection and Classification 实时步态检测与分类的ANFIS后处理

2018 11th International Conference on Human System Interaction (HSI) Pub Date : 2018-07-01 DOI: 10.1109/HSI.2018.8431095

Jakub Dabros, M. Iwaniec, M. Patyk, Xavier Sulkowski, Jacek Wesol

引用次数: 0

The Study of Driver's Brain Activity and Behavior Using fNIRS During Actual Car Driving 汽车实际驾驶过程中驾驶员脑活动与行为的fNIRS研究

2018 11th International Conference on Human System Interaction (HSI) Pub Date : 2018-07-01 DOI: 10.1109/HSI.2018.8431026

Kouji Yamamoto, Hideki Takahashi, T. Sugimachi, Kimihiko Nakano, Y. Suda, Toshinori Kato

{"title":"The Study of Driver's Brain Activity and Behavior Using fNIRS During Actual Car Driving","authors":"Kouji Yamamoto, Hideki Takahashi, T. Sugimachi, Kimihiko Nakano, Y. Suda, Toshinori Kato","doi":"10.1109/HSI.2018.8431026","DOIUrl":"https://doi.org/10.1109/HSI.2018.8431026","url":null,"abstract":"In this study, based on the measurement of brain activity and the change of accelerator and brake stroke, we tried to grasp the interaction between the driver's reaction and the driver's following behavior at the time when driver watched Variable Message Sign on actual car driving. Specifically, using fNIRS, we analyzed same driver's brain activity and driving behavior during actual car driving, and then we evaluated the interaction of both items. As a result, we confirmed that parietal association cortex and prefrontal area activated in the case of driving with recognition and judgment for the information which a driver collected from the environment during driving. Then it was suggested that it was needed to expand parietal association cortex in order to measure the brain activity. Furthermore, it was suggested that both on and off accelerator stroke were connected with the activity of prefrontal area. As a result, it was suggested that it was valid to confirm the driver's reaction at every steps, such as “recognition”, “judgment”, “behavior”, by means of being approached from a neuroscience.","PeriodicalId":441117,"journal":{"name":"2018 11th International Conference on Human System Interaction (HSI)","volume":"124 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125629372","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Interpretable Data-Driven Modeling in Biomass Preprocessing 生物质预处理中的可解释数据驱动建模

2018 11th International Conference on Human System Interaction (HSI) Pub Date : 2018-07-01 DOI: 10.1109/HSI.2018.8431156

Daniel L. Marino, Matthew Anderson, K. Kenney, M. Manic

引用次数: 3

Convolutional Neural Network Based Femur Stabilization for X-Ray Image Sequences 基于卷积神经网络的x射线图像序列股骨稳定

2018 11th International Conference on Human System Interaction (HSI) Pub Date : 2018-07-01 DOI: 10.1109/HSI.2018.8430840

Marta Drążkowska, Tomasz Gawron, K. Kozlowski

{"title":"Convolutional Neural Network Based Femur Stabilization for X-Ray Image Sequences","authors":"Marta Drążkowska, Tomasz Gawron, K. Kozlowski","doi":"10.1109/HSI.2018.8430840","DOIUrl":"https://doi.org/10.1109/HSI.2018.8430840","url":null,"abstract":"Sequence stabilization of medical images is an important aspect of diagnosis, therapy, joint movement kinematic analysis, and cancer detection. Typically, when image frames are recorded, the body is not rigidly fixed as a result of e.g. respiration, thus the position of its segments may vary. Simple image analysis methods (e.g. gradient based, scale-space based) tend to have problems with discerning the key-points in this specific task, due to large diversity of bone structure and highly visible soft tissue. In this paper, we propose a specialized algorithm for stabilization of femur in a sequence of single plane fluoroscopic images. The method estimates the positions of several easily-detectable femur key-points using gradient-based image analysis methods. For other key-points, which are located in the regions of bone with saliency prohibiting effective detection, we use feedforward Convolutional Neural Network as a position estimator. All the key-point positions are used in a stabilization process performed with the ICP (Iterative Closest Point) algorithm. The overall stabilization accuracy is evaluated for two uncorrelated X-ray image sequences, where manual stabilization (i.e., the results for image alignment performed by a human operator without access to key-points) constitutes the ground truth.","PeriodicalId":441117,"journal":{"name":"2018 11th International Conference on Human System Interaction (HSI)","volume":"183 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134151942","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

GymApp: A Real Time Physical Activity Trainner on Wearable Devices GymApp:可穿戴设备上的实时体育锻炼训练器

2018 11th International Conference on Human System Interaction (HSI) Pub Date : 2018-07-01 DOI: 10.1109/HSI.2018.8431358

Paula Viana, Tiago Ferreira, L. Castro, Márcio Soares, José P. Pinto, M. T. Andrade, Pedro Carvalho

{"title":"GymApp: A Real Time Physical Activity Trainner on Wearable Devices","authors":"Paula Viana, Tiago Ferreira, L. Castro, Márcio Soares, José P. Pinto, M. T. Andrade, Pedro Carvalho","doi":"10.1109/HSI.2018.8431358","DOIUrl":"https://doi.org/10.1109/HSI.2018.8431358","url":null,"abstract":"Technological advances are pushing into the mass market innovative wearable devices featuring increasing processing and sensing capacity, non-intrusiveness and ubiquitous use. Sensors built-in those devices, enable acquiring different types of data and by taking advantage of the available processing power, it is possible to run intelligent applications that process the sensed data to offer added-value to the user in multiple domains. Although not new to the modern society, it is unquestionable that the present exercise boom is rapidly spreading across all age groups. However, in a great majority of cases, people perform their physical activity on their own, either due to time or budget constraints and may easily get discouraged if they do not see results or perform exercises inadequately. This paper presents an application, running on a wearable device, aiming at operating as a personal trainer that validates a set of proposed exercises in a sports session. The developed solution uses inertial sensors of an Android Wear smartwatch and, based on a set of pattern recognition algorithms, detects the rate of success in the execution of a planned workout. The fact that all processing can be executed on the device is a differentiator factor to other existing solutions.","PeriodicalId":441117,"journal":{"name":"2018 11th International Conference on Human System Interaction (HSI)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114833000","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 8