研究者詳細 - 井上　創造

論文 - 井上　創造

分割表示226 件中 1 - 20 件目／全件表示 >>

Optimizing Forecasted Activity Notifications with Reinforcement Learning 査読有り国際誌

Muhammad Fikry, Sozo Inoue

Sensors ( MDPI ) 23 ( 14 ) 6510 2023年07月

A Bayesian Approach for Quantifying Data Scarcity when Modeling Human Behavior via Inverse Reinforcement Learning 査読有り国際誌

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ACM Transactions on Computer-Human Interaction ( ACM ) 30 ( 1-8 ) 1 - 27 2023年03月

Improving Noise Robustness of Single Sensor Data in Human Activity Recognition With UMAP and Additional Data 査読有り国際誌

Quynh Nguyen Phuong VU, Paula Lago, Sozo Inoue

ISWC '22: Proceedings of the 2022 ACM International Symposium on Wearable Computers ( ACM ) 109 - 111 2022年12月

Handling Class Imbalance in Forecasting Parkinson's Disease Wearing-off with Fitness Tracker Dataset 査読有り国際誌

John Noel Victorino, Sozo Inoue, Tomohiro Shibata

International Conference on Neural Information Processing (ICONIP2023) 564 - 578 2023年11月

Psychological Analysis in Human-Robot Collaboration from Workplace Stress Factors: A Review 査読有り国際誌

Nazmun Nahid, Xinyi Min, Md Atiqur Rahman Ahad, Sozo Inoue

Activity and Behavior Computing (to appear) ( CRC press ) 2023年06月

Summary of the Fourth Nurse Care Activity Recognition Challenge - Predicting Future Activities 査読有り国際誌

Defry Hamdhana, Chrisitina A Garcia, Nazmun Nahid, Haru Kaneko, Sayeda Shamma, Tahera Hossain, Sozo Inoue

Activity and Behavior Computing (to appear) ( CRC press ) 2023年06月

Improving Complex Nurse Care Activity Recognition Using Barometric Pressure Sensors 査読有り国際誌

Muhammad Fikry, Chrisitina A Garcia, Quynh N Phuong Vu, Shintaro Oyama, Keiko Yamashita, Yuji Sakamoto, Yoshinori Ideno, Sozo Inoue

Activity and Behavior Computing (to appear) ( CRC press ) 2023年06月

Challenges and Opportunities of Activity Recognition in Clinical Pathways 査読有り国際誌

Chrisitina A Garcia, Sozo Inoue

Activity and Behavior Computing (to appear) ( CRC press ) 2023年06月

Analysis of Care Records for Predicting Urination Times 査読有り国際誌

Masato Uchimura, Haru Kaneko, Sozo Inoue

Activity and Behavior Computing (to appear) ( CRC press ) 2023年06月

Forecasting Parkinson's Disease Patients' Wearing-Off using Wrist-Worn Fitness Tracker and Smartphone Dataset 査読有り国際誌

John Noel C Victorino, Yuko Shibata, Sozo Inoue, Tomohiro Shibata

Activity and Behavior Computing (to appear) ( CRC press ) 2023年06月

Understanding Mental Health Using Ubiquitous Sensors and Machine Learning: Challenges Ahead 査読有り国際誌

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Activity and Behavior Computing (to appear) ( CRC press ) 2023年06月

Summary of the Third Nurse Care Activity Recognition Challenge - Can We Do from the Field Data? 査読有り国際誌

Sayeda Shamma Alia, 安達康平, Tahera Hossain, Le Nhat Tan, 金子晴, Paula Lago, 大北剛, 井上創造

Ubicomp Workshop on Human Activity Sensing Corpus and Applications (HASCA) 2021年09月

Transition-points-based Segmentation and Hierarchical Classification for Locomotion and Transportation recognitionon Radio-data 査読有り国際誌

Le Nhat Tan, Nazmun Nahid, 井上創造

Ubicomp Workshop on Human Activity Sensing Corpus and Applications (HASCA) 2021年09月

Analysis of Feature Importances for Automatic Generation of Care Records 査読有り国際誌

金子晴, Tahera Hossain, 井上創造

Ubicomp Workshop on Human Activity Sensing Corpus and Applications (HASCA) 2021年09月

PerMML: A Performance Metric for Multi-layer Dataset 査読有り国際誌

Sayeda Shamma Alia, Tahera Hossain, 井上創造

Ubicomp Workshop on Human Activity Sensing Corpus and Applications (HASCA) 2021年09月

Integrating A Spoken Dialogue System, Nursing Records, and Activity Data Collection based on Smartphones 査読有り国際誌

ティッタヤ　マイリッタ, Nattaya Mairittha, 井上創造

Computer Methods and Programs in Biomedicine, ISSN 0169-2607 106364 2021年08月

記述言語：英語掲載種別：研究論文（学術雑誌）

Background and Objective: This study describes the integration of a spoken dialogue system and nursing records on an Android smartphone application intending to help nurses reduce documentation time and improve the overall experience of a healthcare setting. The application also incorporates with collecting personal sensor data and activity labels for activity recognition. Methods: We developed a joint model based on a bidirectional long-short term memory and conditional random fields (Bi-LSTM-CRF) to identify user intention and extract record details from user utterances. Then, we transformed unstructured data into record inputs on the smartphone application. Results: The joint model achieved the highest F1-score at 96.79%. Moreover, we conducted an experiment to demonstrate the proposed models capability and feasibility in recording in realistic settings. Our preliminary evaluation results indicate that when using the dialogue-based, we could increase the percentage of documentation speed to 58.13% compared to the traditional keyboard-based. Conclusions: Based on our findings, we highlight critical and promising future research directions regarding the design of the efficient spoken dialogue system and nursing records.

その他リンク： https://doi.org/10.1016/j.cmpb.2021.106364.

Intracranial Hemorrhage Brain Image Non-rigid Registration from Real-world Dataset to Reference Space 査読有り国際誌

Le Nhat Tan, 小橋昌司, 有村公一, 飯原弘二, 井上創造

International Conference on Imaging, Vision & Pattern Recognition (IVPR) 7 pages 2021年08月

記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

Online Online

Intracranial Hemorrhage is a common brain injury that leads to a high mortality rate without prompt recognition. To address these issues, computer-aid diagnosis tools are rapidly being developed along with neural-network-based techniques to provide fast, reliable analysis and achieve accurate diagnosis decisions based on medical images. One of the most interesting applications in computer-aid diagnosis is Image Registration due to its practical features in clinical diagnosis and treatment planning. In this study, we present the non-rigid image registration for the 3D Computed Tomography image dataset of the Intracranial Hemorrhage Brain. By utilizing the affine transformation and a neural network model, we aim to predict the deformation vector field, map the real-world-collected dataset to the reference space and overcome the shifting data problem between the data analysis experiment on standard and real-world medical image analysis. Our test results gave that good registration performance is obtained in a very short time by using a neural network model, and the affine transformation significantly improves the real-world image registration. In addition, according to the distance from the hematoma area change ratio to the brain area change ratio, the characteristics of the major structure are determined to be preserved.

Predicting Wearing-Off of Parkinson’s Disease Patients Using a Wrist-Worn Fitness Tracker and a Smartphone: A Case Study 査読有り国際誌

John Noel Victorino, Yuko Shibata, 井上創造, 柴田智広

Applied Sciences, MDPI 11 ( 16 ) 2 - 2 2021年08月

記述言語：英語掲載種別：研究論文（学術雑誌）

Parkinson’s disease (PD) patients experience varying symptoms related to their illness. Therefore, each patient needs a tailored treatment program from their doctors. One approach is the use of anti-PD medicines. However, a “wearing-off” phenomenon occurs when these medicines lose their effect. As a result, patients start to experience the symptoms again until their next medicine intake. In the long term, the duration of “wearing-off” begins to shorten. Thus, patients and doctors have to work together to manage PD symptoms effectively. This study aims to develop a prediction model that can determine the “wearing-off” of anti-PD medicine. We used fitness tracker data and self-reported symptoms from a smartphone application in a real-world environment. Two participants wore the fitness tracker for a month while reporting any symptoms using the Wearing-Off Questionnaire (WoQ-9) on a smartphone application. Then, we processed and combined the datasets for each participant’s models. Our analysis produced prediction models for each participant. The average balanced accuracy with the best hyperparameters was at 70.071.7% for participant 1 and 76.176.9% for participant 2, suggesting that our approach would be helpful to manage the “wearing-off” of anti-PD medicine, motor fluctuations of PD patients, and customized treatment for PD patients. View Full-Text

その他リンク： https://www.mdpi.com/2076-3417/11/16/7354

CrowdAct: Achieving High-Quality Crowdsourced Datasets in Mobile Activity Recognition 査読有り国際誌

Nattaya Mairittha, ティッタヤ　マイリッタ, 井上創造

Proc. of ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, Article 142, 5 ( 0 ) 3 - 2 2021年03月

記述言語：英語掲載種別：研究論文（学術雑誌）

In this study, we propose novel gamified active learning and inaccuracy detection for crowdsourced data labeling for an activity recognition system using mobile sensing (CrowdAct). First, we exploit active learning to address the lack of accurate information. Second, we present the integration of gamification into active learning to overcome the lack of motivation and sustained engagement. Finally, we introduce an inaccuracy detection algorithm to minimize inaccurate data. To demonstrate the capability and feasibility of the proposed model in realistic settings, we developed and deployed the CrowdAct system to a crowdsourcing platform. For our experimental setup, we recruited 120 diverse workers. Additionally, we gathered 6,549 activity labels from 19 activity classes by using smartphone sensors and user engagement information. We empirically evaluated the quality of CrowdAct by comparing it with a baseline using techniques such as machine learning and descriptive and inferential statistics. Our results indicate that CrowdAct was effective in improving activity accuracy recognition, increasing worker engagement, and reducing inaccurate data in crowdsourced data labeling. Based on our findings, we highlight critical and promising future research directions regarding the design of efficient activity data collection with crowdsourcing.

その他リンク： https://dl.acm.org/doi/abs/10.1145/3432222

私のブックマーク「センサを用いた行動認識」

井上創造

人工知能 36 ( 2 ) 233 - 246 2021年03月

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