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Dwi Sulisworo
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Journal of Novel Engineering Science and Technology
Published by Indonesian Institute of Science and Technology Research
ISSN : 29618916     EISSN : 29618738     DOI : https://doi.org/10.56741/jnest.v1i02
Core Subject : Science, Social, Engineering,
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Environmental Science Mechanical Engineering
Journal of Novel Engineering Science and Technology is a multi-disciplinary international open-access journal dedicated to natural science, technology, and engineering, as well as its derived applications in various fields. JNEST publishes high-quality original research articles and reviews in all of the disciplines mentioned above. All papers submitted will go through a rapid peer-review process to ensure their quality. Submissions must contain original research and contributions to their field. The manuscript must adhere to the author’s guidelines and have never been published before. All accepted manuscripts will be indexed in DOAJ, EBSCO, and Google Scholar. The indexation in SINTA, Scopus, and WoS will be provided in the future to provide maximum exposure to the articles.
Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 62 Documents
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Multimodal Gait Analysis Using IMU and EMG Sensors with HMM Classification to Differentiate Obese and Normal Body Types Setiyadi, Suto; Muhammad Ridho Rosa; Nigel Bryan Tang; Muhammad Sabiq Al Muttaqin; Muhammad Rafi Haykal Gumelar
Journal of Novel Engineering Science and Technology Vol. 4 No. 03 (2025): Journal of Novel Engineering Science and Technology
Publisher : The Indonesian Institute of Science and Technology Research

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56741/jnest.v4i03.1272

Abstract

Gait analysis is essential for diagnosing movement disorders and monitoring rehabilitation progress; conventional methods are often costly and complex. This study aims to differentiate gait characteristics between individuals with obesity and those with normal body composition using a multimodal approach that integrates Inertial Measurement Unit (IMU) and electromyography (EMG) sensors. Data were collected from ten male participants (five classified as obese and five with normal body composition). IMU sensors were used to measure acceleration, angular velocity, and step count, while EMG sensors recorded muscle activity from the tibialis anterior and gastrocnemius muscles. We developed a real-time acquisition using ESP32 microcontrollers and Bluetooth Low Energy (BLE), and gait phase classification was performed using the Hidden Markov Model (HMM). Using heel-mounted sensors, the average step detection error ranged from 2.5% to 3.6%. IMU signals from obese participants indicated a shift in dominant gait phase from Initial Contact during slow walking to Loading Response during fast walking, with relative errors up to 27%. In contrast, participants with normal body composition exhibited more diverse and accurate phase distributions. EMG-based analysis provided more precise segmentation (with error rates as low as 0.47%). It revealed distinct muscle activation patterns: gastrocnemius activity was dominant during the Midswing or Midstance phases, while tibialis anterior activity peaked during Initial Contact, Initial Swing, or Loading Response. These findings suggest body composition significantly affects gait stability, phase transitions, and muscle activation patterns. Future work should explore advanced machine learning algorithms such as Long Short-Term Memory (LSTM) or Convolutional Neural Networks (CNN), integrate pressure sensors, and validate the system in real-world environments to enhance accuracy and reliability.
Integration of ECDHE Curve25519, RSASSA-PSS, and AES-256 for Enhanced PrivateDH Key Exchange Protocol in End-to-End Communication Ardi Saputra; Ronsen Purba
Journal of Novel Engineering Science and Technology Vol. 4 No. 03 (2025): Journal of Novel Engineering Science and Technology
Publisher : The Indonesian Institute of Science and Technology Research

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56741/jnest.v4i03.1275

Abstract

The growing demand for secure digital communication calls for cryptographic protocols that are not only efficient but also capable of ensuring message confidentiality, integrity, and authenticity. PrivateDH is one such protocol that combines Diffie-Hellman, RSA, and AES; however, it still exhibits key weaknesses, including the absence of user authentication and reliance on classical Diffie-Hellman algorithms, which are computationally intensive and do not support forward secrecy. This study proposes an enhanced version of the PrivateDH protocol by integrating ECDHE Curve25519 as a replacement for classic DH, and RSASSA-PSS as a robust digital signature mechanism for user authentication. The methodology involves implementing and testing the proposed protocol within a peer-to-peer communication scenario, with performance evaluations based on handshake duration, CPU and memory usage, as well as security assessments including digital signature validation and forward secrecy. The results demonstrate that the enhanced protocol effectively accelerates key exchange, maintains resource efficiency, and provides reliable user authentication. In conclusion, this protocol contributes meaningfully to the advancement of more secure and efficient end-to-end communication systems, aligning with the demands of modern digital environments.

Page 7 of 7 | Total Record : 62

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