Hybrid Convolution based Efficient Net-based Hand Gesture Recognition Framework with Optimized Algorithm DOI
Jency Rubia J,

R. Babitha Lincy,

C. Sherin Shibi

et al.

International Journal of Pattern Recognition and Artificial Intelligence, Journal Year: 2024, Volume and Issue: unknown

Published: June 29, 2024

The difficulties in communication and hearing are an important concern for deaf–dumb people, which stop access to their essential basic needs. Many findings have been made address sign languages even though this challenging problem is not still solved. methods aimed propose vision-based classifiers through identical pattern investigation tasks by obtaining the difficult handcraft feature descriptions of gestures from gathered images. However, efficacy all those models less performing with a huge signbook captured uncontrolled complex background conditions. So, effective Indian Sign Language (ISL) classification method developed advanced deep learning approach. At first, hand gesture images obtained data source. Only image hand, complicated background, extracted image. features using Scale-Invariant Feature Transform (SIFT) Multiscale Vision Transformer (MVT). Then, fed Hybrid Convolution-based EfficientNet (HCEN) model. hyper-parameters HCEN model tuned implemented Adaptive Political Optimizer (APO) algorithm. recognized signs suggested Various experiments conducted determine performance learning-based recognition

Language: Английский

Enhanced user interaction in operating systems through machine learning language models DOI
Chenwei Zhang,

Wenran Lu,

Chunhe Ni

et al.

Published: June 13, 2024

With the large language model showing human-like logical reasoning and understanding ability, whether agents based on can simulate interaction behavior of real users, so as to build a reliable virtual recommendation A/B test scene help application research is an urgent, important economic value problem. The combination design machine learning provide more efficient personalized user experience for products services. This service meet specific needs users improve satisfaction loyalty. Second, interactive system understand user's views product by providing good interface experience, then use algorithms optimize product. iterative optimization process continuously quality performance changing users. At same time, designers need consider how these tools be combined with systems experience. paper explores potential applications models, in operating systems. By integrating technologies, intelligent services provided promote continuous improvement products. great both applications.

Language: Английский

Citations

6
Evaluation of Impact of Image Augmentation Techniques on Two Tasks: Window Detection and Window States Detection DOI Creative Commons
Seunghyeon Wang

Results in Engineering, Journal Year: 2024, Volume and Issue: unknown, P. 103571 - 103571

Published: Nov. 1, 2024

Language: Английский

Citations

6
Wearable device for personalized EMG feedback-based treatments DOI Creative Commons
Mitar Simić, Goran Stojanović

Results in Engineering, Journal Year: 2024, Volume and Issue: 23, P. 102472 - 102472

Published: June 25, 2024

Curative effects of electromyography (EMG) feedback in treatment various conditions and/or recovery after injuries have been earlier reported. However, wider application on EMG is somehow limited due to the overall price such systems and availability outside specialized centers. Development a personalized device for would be great importance home stroke or injuries, achieving better success fitness improving biofeedback-based treatments as urinary incontinence. Despite extensive research signal collection, there lack focus in-situ analysis that considers intensity duration muscle activities. This gap presents motivation our research. In this paper, we present methodology realization wearable, rechargeable battery-powered, small-sized (90 mm × 60 mm) electronic recording two channels (12-bits resolution, sampling frequency up 1.6 kHz) with Bluetooth Low Energy connectivity smartphone. An average current consumption 20.5 mA was experimentally determined, suggesting multiday continuous functionality possible. Advancing state art, propose cross-correlation-based algorithm dynamical computing evaluation activation levels. can determine if follows predefined profile contractions/relaxations (as needed treatment) indicate muscles specific exercise were not engaged proper time intensity. The performed simulation showed proposed approach exhibited shorter processing compared Morlet Wavelet Transform Dynamic Time Warping. Finally, experimental work five human volunteers demonstrated reliability acquisition processing. Therefore, main contribution cost-effective, small-sized, customizable system an efficient collection

Language: Английский

Citations

5
A survey on hand gesture recognition based on surface electromyography: Fundamentals, methods, applications, challenges and future trends DOI

Sike Ni,

Mohammed A. A. Al‐qaness, Ammar Hawbani

et al.

Applied Soft Computing, Journal Year: 2024, Volume and Issue: 166, P. 112235 - 112235

Published: Sept. 11, 2024

Language: Английский

Citations

4
Exploring the synergy of human-robot teaming, digital twins, and machine learning in Industry 5.0: a step towards sustainable manufacturing DOI Creative Commons
Even Falkenberg Langås, Muhammad Hamza Zafar, Filippo Sanfilippo

et al.

Journal of Intelligent Manufacturing, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 19, 2025

Language: Английский

Citations

0
Using machine learning algorithms for grasp strength recognition in rehabilitation planning DOI Creative Commons

Tanin Boka,

Arshia Eskandari,

S. Ali A. Moosavian

et al.

Results in Engineering, Journal Year: 2023, Volume and Issue: 21, P. 101660 - 101660

Published: Dec. 14, 2023

The augmentation of individuals' quality life, particularly those with disabilities, can be achieved through state-of-the-art artificial intelligence solutions. Machine learning algorithms, known for their ability to acquiring knowledge and identify significant characteristics from diverse datasets, play a crucial role. In this investigation, we focused on classifying various weights commonly encountered in daily activities based electromyography (EMG) readings, using multiple distinct machine algorithms. This endeavor involved collection substantial data cohort, wherein participants assumed arm configurations while manipulating three objects (specifically, pen, bottle, weighty object) or no object at all. sample encompassed 50 physically capable healthy participants, an equal distribution 25 males females. muscular activity was measured utilizing the MYO armband, advanced eight-channel EMG device positioned forearm. After preprocessing data, several algorithms has been employed analyze dataset. Notably, outcomes demonstrate that K-Nearest Neighbors (KNN), Random Forest (RF), Decision Tree (DT) emerge as optimal methodologies grip strength estimation, achieving impressive accuracy rates 99.23 %, 99.08 98.62 respectively. experimental supplementary materials are available https://github.com/arshiaeskandari/EMG-Dataset.

Language: Английский

Citations

4
Hybrid Convolution based Efficient Net-based Hand Gesture Recognition Framework with Optimized Algorithm DOI
Jency Rubia J,

R. Babitha Lincy,

C. Sherin Shibi

et al.

International Journal of Pattern Recognition and Artificial Intelligence, Journal Year: 2024, Volume and Issue: unknown

Published: June 29, 2024

The difficulties in communication and hearing are an important concern for deaf–dumb people, which stop access to their essential basic needs. Many findings have been made address sign languages even though this challenging problem is not still solved. methods aimed propose vision-based classifiers through identical pattern investigation tasks by obtaining the difficult handcraft feature descriptions of gestures from gathered images. However, efficacy all those models less performing with a huge signbook captured uncontrolled complex background conditions. So, effective Indian Sign Language (ISL) classification method developed advanced deep learning approach. At first, hand gesture images obtained data source. Only image hand, complicated background, extracted image. features using Scale-Invariant Feature Transform (SIFT) Multiscale Vision Transformer (MVT). Then, fed Hybrid Convolution-based EfficientNet (HCEN) model. hyper-parameters HCEN model tuned implemented Adaptive Political Optimizer (APO) algorithm. recognized signs suggested Various experiments conducted determine performance learning-based recognition

Language: Английский

Citations

0