 |
International Journal for Research Trends and Innovation
International Peer Reviewed & Refereed Journals, Open Access Journal
ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.14 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)
|
Issue: December 2025
Volume 10 | Issue 12
Review Result and Publication of Paper within : 2-3 days
Click Here For more DetailsFor Authors
Forms / Download
Published Issue Details
Editorial Board
Other IMP Links
Facts & Figure
Impact Factor : 8.14
Issue per Year : 12
Volume Published : 10
Issue Published : 115
Article Submitted : 19444
Article Published : 8041
Total Authors : 21252
Total Reviewer : 769
Total Countries : 144
Indexing Partner
Licence
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
|
Published Paper Details
|
|
| Paper Title: | Integrating Multimodal Sensing and Hybrid Deep Learning for Enhanced Human Activity Recognition |
| Authors Name: | Akash Ghosh , Sakshi Ganesh Dighe , Rahul Sarkar , Abhay Gupta , Pranjal Gupta |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJRTI_188739
|
| Published Paper Id: | IJRTI2312067 |
| Published In: | Volume 8 Issue 12, December-2023 |
| DOI: | |
| Abstract: | Recent advancements in Human Activity Recognition (HAR) have seen significant progress with the implementation of deep learning techniques. Traditional methods heavily depend on handcrafted features extracted from single sensing modalities, creating challenges in achieving precise activity recognition. This paper presents a novel approach that combines the advantages of multimodal sensing devices and a hybrid deep learning model to overcome these challenges. The proposed model utilizes a multi-channel architecture, integrating Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (BLSTM). The CNN layers enable direct mapping and abstract representation of raw sensor inputs at various resolutions, while the BLSTM layer harnesses both forward and backward sequences to enhance feature extraction for superior activity recognition. To evaluate the model's effectiveness, experiments were conducted on two publicly available datasets. The results demonstrate a substantial performance improvement compared to baseline models and alternative approaches using the same datasets. Furthermore, the adaptability of the proposed model to multimodal sensing devices is highlighted, affirming its suitability for advanced human activity recognition. In recognition of the limited availability of publicly accessible datasets for physical activities, this research contributes to the literature by creating a new and challenging dataset. Collected from 25 participants using the Sensor V2 sensor, this dataset encompasses 14 distinct classes of human physical activities. An extensive ablation study is performed, evaluating various traditional machine learning and deep learning models. The CNN-LSTM technique achieves an accuracy of 92.32%, underscoring the effectiveness of the hybrid model for real-world, long-term Human Activity Recognition applications. |
| Keywords: | Human activity recognition, deep learning, multimodal sensing, convolutional neural network, long short-term memory, skeleton data. |
| Cite Article: | "Integrating Multimodal Sensing and Hybrid Deep Learning for Enhanced Human Activity Recognition", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.8, Issue 12, page no.469 - 476, December-2023, Available :http://www.ijrti.org/papers/IJRTI2312067.pdf |
| Downloads: | 000205178 |
| ISSN: |
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
| Publication Details: |
Published Paper ID: IJRTI2312067
Registration ID:188739
Published In: Volume 8 Issue 12, December-2023
DOI (Digital Object Identifier):
Page No: 469 - 476 Country: Delhi, Delhi, India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2312067 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2312067 |
| Share Article: | |
|
Click Here to Download This Article |
|
| Article Preview | |
|
|
|
Major Indexing from www.ijrti.org
| Google Scholar | ResearcherID Thomson Reuters | Mendeley : reference manager | Academia.edu |
| arXiv.org : cornell university library | Research Gate | CiteSeerX | DOAJ : Directory of Open Access Journals |
| DRJI | Index Copernicus International | Scribd | DocStoc |
ISSN Details
 |
 |
|
ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
DOI (A digital object identifier)
| Providing A digital object identifier by DOI.ONE How to Get DOI? |
Conference
Open Access License Policy
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Important Details
Join RMS/Earn 300
WhatsApp
Click Here
Click Here
Indexing Partner |
|||
| Copyright © 2025 - All Rights Reserved - IJRTI | |||