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All Journal Journal of Electronics, Electromedical Engineering, and Medical Informatics Journal of Prosthetics Orthotics and Science Technology (JPOST)
Kitagawa, Kodai
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Health Professions Materials Science & Nanotechnology Nursing Public Health

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Foot Clearance Prediction using Wrist Acceleration and Gait Speed Kitagawa, Kodai; Wada, Chikamune; Toya, Nobuyuki
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 6 No 1 (2024): January
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v6i1.345

Abstract

Elderly individuals experience fall accidents due to tripping because recognizing foot clearance during walking is difficult for them. To prevent fall accidents, foot clearance should be measured and informed in daily life. Foot clearance is commonly measured using vision-based systems, such as optical motion capture systems. However, problem of these vision-based systems is that these systems cannot measure foot clearance in daily life because they have limitations due to obstacles and field of view. Based on this problem, we developed a wearable fall prevention system using smart devices, such as smartphones and smartwatches. This study aimed to evaluate the proposed prediction method for foot clearance using sensor data obtained from wearable smart devices which can be used in daily life. The proposed method will contribute to measure foot clearance in daily life. This method predicts foot clearance from wrist acceleration and gait speed using a machine learning-based regression model. The proposed method was tested in a computational simulation with a public gait dataset obtained using an optical motion capture system. The results showed that the correlations between the predicted and actual foot clearance were at least 0.65. In conclusion, this study indicates the possibility that the proposed method can be used to measure foot clearance and thus can be used in wearable fall prevention systems.
A Comparative Study of Improved Ensemble Learning Algorithms for Patient Severity Condition Classification Edi Ismanto; Abdul Fadlil; Anton Yudhana; Kitagawa, Kodai
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 6 No 3 (2024): July
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v6i3.452

Abstract

The evolution of Electronic Health Records (EHR) has facilitated comprehensive patient record-keeping, enhancing healthcare delivery and decision-making processes. Despite these advancements, analyzing EHR data using ensemble machine learning methods poses unique challenges. These challenges include data dimensionality, imbalanced class distributions, and the need for effective hyperparameter tuning to optimize model performance. The study conducted a thorough comparative analysis of various ensemble machine learning (EML) models using Electronic Health Record (EHR) datasets. After addressing data imbalance and reducing dimensionality, the accuracy of the EML models showed significant improvement. Notably, the Gradient Boosting Machine (GBM) and CatBoost models exhibited superior performance with an accuracy of 73%, achieved through experiments involving dimensionality reduction and handling of imbalanced data. Furthermore, optimization techniques such as Grid Search and Random Search were employed to enhance the EML models. The results of model optimization revealed that the GBM + Random Search model performed the best, achieving an accuracy of 74%, followed by the XGBoost + Grid Search model with an accuracy of 73%. The GBM model also excelled in distinguishing between positive and negative classes, boasting the highest Area under Curve (AUC) value of 0.78, indicative of its superior classification capabilities compared to other models. This study emphasizes the significance of incorporating cutting-edge EML techniques into clinical workflows and emphasizes the revolutionary potential of GBM in classification modeling for patient severity conditions. Future research should focus on deep learning (DL) applications and the integration of these models.
Effect of Soft Knee Brace on Shank Movement in Running Kitagawa, Kodai; Murakami, Tomoya; Wada, Chikamune; Yamamoto, Hiroaki
Journal of Prosthetics Orthotics and Science Technology Vol. 3 No. 2 (2024): Journal of Prosthetics Orthotics and Science Technology (JPOST)
Publisher : Poltekkes Kemenkes Jakarta I

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36082/jpost.v3i2.1945

Abstract

Background: Soft knee braces are used for the protection and treatment of knee injuries in light-intensity activities of daily life. Soft knee braces have several functions, such as stabilizing and supporting the lower limb posture. However, previous studies did not investigate the effects of a soft knee brace on the lower limbs during vigorous-intensity activities. Aims: The objective of this study was to investigate the effect of a soft knee brace on the physical load and stability of the lower limb during running as a vigorous-intensity activity. Methods: Participants were asked to run with or without a soft knee brace, and lower-limb movements during running were measured using an inertial sensor on the shank. Results: The result showed that soft knee brace significantly reduced the magnitude and mediolateral standard deviation of shank acceleration. Conclusion: The results of this study indicate the possibility that wearing a soft knee brace can improve the physical load and stability of the lower limbs during running.  
Co-Authors Abdul Fadlil Anton Yudhana Edi Ismanto Murakami, Tomoya Toya, Nobuyuki Wada, Chikamune Yamamoto, Hiroaki