Faculty Profiles - INOUE Sozo

Papers - INOUE Sozo

Division display1 - 20 of about 226／ All the affair displays >>

Optimizing Forecasted Activity Notifications with Reinforcement Learning Reviewed International journal

Muhammad Fikry, Sozo Inoue

Sensors ( MDPI ) 23 ( 14 ) 6510 2023.07

A Bayesian Approach for Quantifying Data Scarcity when Modeling Human Behavior via Inverse Reinforcement Learning Reviewed International journal

Tahera Hossain, Wanggang Shen, Anindya Antar, Snehal Prabhudesai, Sozo Inoue, Xun Huan, Nikola Banovic

ACM Transactions on Computer-Human Interaction ( ACM ) 30 ( 1-8 ) 1 - 27 2023.03

Improving Noise Robustness of Single Sensor Data in Human Activity Recognition With UMAP and Additional Data Reviewed International journal

Quynh Nguyen Phuong VU, Paula Lago, Sozo Inoue

ISWC '22: Proceedings of the 2022 ACM International Symposium on Wearable Computers ( ACM ) 109 - 111 2022.12

Handling Class Imbalance in Forecasting Parkinson's Disease Wearing-off with Fitness Tracker Dataset Reviewed International journal

John Noel Victorino, Sozo Inoue, Tomohiro Shibata

International Conference on Neural Information Processing (ICONIP2023) 564 - 578 2023.11

Psychological Analysis in Human-Robot Collaboration from Workplace Stress Factors: A Review Reviewed International journal

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

Activity and Behavior Computing (to appear) ( CRC press ) 2023.06

Summary of the Fourth Nurse Care Activity Recognition Challenge - Predicting Future Activities Reviewed International journal

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

Activity and Behavior Computing (to appear) ( CRC press ) 2023.06

Improving Complex Nurse Care Activity Recognition Using Barometric Pressure Sensors Reviewed International journal

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 ) 2023.06

Challenges and Opportunities of Activity Recognition in Clinical Pathways Reviewed International journal

Chrisitina A Garcia, Sozo Inoue

Activity and Behavior Computing (to appear) ( CRC press ) 2023.06

Analysis of Care Records for Predicting Urination Times Reviewed International journal

Masato Uchimura, Haru Kaneko, Sozo Inoue

Activity and Behavior Computing (to appear) ( CRC press ) 2023.06

Forecasting Parkinson's Disease Patients' Wearing-Off using Wrist-Worn Fitness Tracker and Smartphone Dataset Reviewed International journal

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

Activity and Behavior Computing (to appear) ( CRC press ) 2023.06

Understanding Mental Health Using Ubiquitous Sensors and Machine Learning: Challenges Ahead Reviewed International journal

Tahia Tazin, Tahera Hossain, Shahera Hossain, Sozo Inoue

Activity and Behavior Computing (to appear) ( CRC press ) 2023.06

Summary of the Third Nurse Care Activity Recognition Challenge - Can We Do from the Field Data? Reviewed International journal

Sayeda Shamma Alia, Kohei Adachi, Tahera Hossain, Le Nhat Tan, Haru Kaneko, Paula Lago, Tsuyoshi Okita, Sozo Inoue

Ubicomp Workshop on Human Activity Sensing Corpus and Applications (HASCA) 2021.09

Transition-points-based Segmentation and Hierarchical Classification for Locomotion and Transportation recognitionon Radio-data Reviewed International journal

Le Nhat Tan, Nazmun Nahid, Sozo Inoue

Ubicomp Workshop on Human Activity Sensing Corpus and Applications (HASCA) 2021.09

Analysis of Feature Importances for Automatic Generation of Care Records Reviewed International journal

Haru Kaneko, Tahera Hossain, Sozo Inoue

Ubicomp Workshop on Human Activity Sensing Corpus and Applications (HASCA) 2021.09

PerMML: A Performance Metric for Multi-layer Dataset Reviewed International journal

Sayeda Shamma Alia, Tahera Hossain, Sozo Inoue

Ubicomp Workshop on Human Activity Sensing Corpus and Applications (HASCA) 2021.09

Integrating A Spoken Dialogue System, Nursing Records, and Activity Data Collection based on Smartphones Reviewed International journal

Tittaya Mairittha, Nattaya Mairittha, Sozo Inoue

Computer Methods and Programs in Biomedicine, ISSN 0169-2607 106364 2021.08

Language：English Publishing type：Research paper (scientific journal)

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.

Other Link： https://doi.org/10.1016/j.cmpb.2021.106364.

Intracranial Hemorrhage Brain Image Non-rigid Registration from Real-world Dataset to Reference Space Reviewed International journal

Le Nhat Tan, Shoji Kobashi, Koichi Arimura, Koji Iihara, Sozo Inoue

International Conference on Imaging, Vision & Pattern Recognition (IVPR) 7 pages 2021.08

Language：English Publishing type：Research paper (international conference proceedings)

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 Reviewed International journal

John Noel Victorino, Yuko Shibata, Sozo Inoue, Tom Shibata

Applied Sciences, MDPI 11 ( 16 ) 2 - 2 2021.08

Language：English Publishing type：Research paper (scientific journal)

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

Other Link： https://www.mdpi.com/2076-3417/11/16/7354

CrowdAct: Achieving High-Quality Crowdsourced Datasets in Mobile Activity Recognition Reviewed International journal

Nattaya Mairittha, Tittaya Mairittha, Sozo Inoue

Proc. of ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, Article 142, 5 ( 0 ) 3 - 2 2021.03

Language：English Publishing type：Research paper (scientific journal)

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.

Other Link： 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]

Sozo Inoue

36 ( 2 ) 233 - 246 2021.03

　PREV- NEXT 　

1
2
3
4
5
6
7
8
9
10
11
12