2022 15th International Conference on Human System Interaction (HSI)最新文献

筛选

英文中文

Predicting Human Performance in Vertical Hierarchical Menu Selection in Immersive AR Using Hand-gesture and Head-gaze 使用手势和头凝视来预测沉浸式AR中垂直分层菜单选择中的人类表现

2022 15th International Conference on Human System Interaction (HSI) Pub Date : 2022-06-19 DOI: 10.1109/HSI55341.2022.9869495

Majid Pourmemar, Yashas Joshi, Charalambos (Charis) Poullis

{"title":"Predicting Human Performance in Vertical Hierarchical Menu Selection in Immersive AR Using Hand-gesture and Head-gaze","authors":"Majid Pourmemar, Yashas Joshi, Charalambos (Charis) Poullis","doi":"10.1109/HSI55341.2022.9869495","DOIUrl":"https://doi.org/10.1109/HSI55341.2022.9869495","url":null,"abstract":"There are currently limited guidelines on designing user interfaces (UI) for immersive augmented reality (AR) applications. Designers must reflect on their experience designing UI for desktop and mobile applications and conjecture how a UI will influence AR users’ performance. In this work, we introduce a predictive model for determining users’ performance for a target UI without the subsequent involvement of participants in user studies. The model is trained on participants’ responses to objective performance measures such as consumed endurance (CE) and pointing time (PT) using hierarchical drop-down menus. Large variability in the depth and context of the menus is ensured by randomly and dynamically creating the hierarchical drop-down menus and associated user tasks from words contained in the lexical database WordNet. Subjective performance bias is reduced by incorporating the users’ non-verbal standard performance WAIS-IV during the model training. The semantic information of the menu is encoded using the Universal Sentence Encoder. We present the results of a user study that demonstrates that the proposed predictive model achieves high accuracy in predicting the CE on hierarchical menus of users with various cognitive abilities. To the best of our knowledge, this is the first work on predicting CE in designing UI for immersive AR applications.","PeriodicalId":282607,"journal":{"name":"2022 15th International Conference on Human System Interaction (HSI)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123791085","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

Comparison Study of Inertial Sensor Signal Combination for Human Activity Recognition based on Convolutional Neural Networks 基于卷积神经网络的惯性传感器信号组合人体活动识别比较研究

2022 15th International Conference on Human System Interaction (HSI) Pub Date : 2022-06-09 DOI: 10.1109/HSI55341.2022.9869457

Farhad Nazari, N. Mohajer, D. Nahavandi, A. Khosravi, S. Nahavandi

{"title":"Comparison Study of Inertial Sensor Signal Combination for Human Activity Recognition based on Convolutional Neural Networks","authors":"Farhad Nazari, N. Mohajer, D. Nahavandi, A. Khosravi, S. Nahavandi","doi":"10.1109/HSI55341.2022.9869457","DOIUrl":"https://doi.org/10.1109/HSI55341.2022.9869457","url":null,"abstract":"Human Activity Recognition (HAR) is one of the essential building blocks of so many applications like security, monitoring, the internet of things and human-robot interaction. The research community has developed various methodologies to detect human activity based on various input types. However, most of the research in the field has been focused on applications other than human-in-the-centre applications. This paper focused on optimising the input signals to maximise the HAR performance from wearable sensors. A model based on Convolutional Neural Networks (CNN) has been proposed and trained on different signal combinations of three Inertial Measurement Units (IMU) that exhibit the movements of the dominant hand, leg and chest of the subject. The results demonstrate k-fold cross-validation accuracy between 99.77 and 99.98% for signals with the modality of 12 or higher. The performance of lower dimension signals, except signals containing information from both chest and ankle, was far inferior, showing between 73 and 85% accuracy.","PeriodicalId":282607,"journal":{"name":"2022 15th International Conference on Human System Interaction (HSI)","volume":"142 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134448069","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: 5

An Analysis of the Features Considerable for NFT Recommendations NFT推荐的重要特征分析

2022 15th International Conference on Human System Interaction (HSI) Pub Date : 2022-05-01 DOI: 10.1109/HSI55341.2022.9869497

D. Piyadigama, Guhanathan Poravi

引用次数: 9

首页上一页