Gesture and Voice-Based Automation System for Home Surveillance

Gesture and voice-based automation innovations are being examined because of the developing interest for reliable and smart home security frameworks. In this study, a MATLAB-based home reconnaissance automation framework in light of voice and motion is created and reproduced. To perceive, interpret, and answer user developments and vocal orders, the proposed framework utilizes modern sign handling and AI calculations. Although nonverbal communication through gestures has been around for a while, gesture-based human-computer interaction (HCI) is growing in popularity in the present day. The conventional interface uses a number of tools, including markers, physical controllers, and gloves, to increase the recognition rate. This work offers a novel marker less method for capturing gestures without the use of expensive gear or any obstacles. In order to enable disabled individuals to operate household appliances using their own speech and gesture commands, we present in this paper a speech and Gesture Driven automation system for homes with a central controller. The framework joins programmed reactions with continuous observation highlights to further develop security, openness, and user solace. Framework reenactment, including control rationale, voice order handling, and motion identification, is finished with MATLAB. The accuracy, reactivity, and common sense of the innovation are shown by the recreation results.

Authors

Institutions

Publication Details

Published
2025-02-21
DOI
https://doi.org/10.1109/ce2ct64011.2025.10941253
Citations
2
Primary Topic
IoT-based Smart Home Systems
Type
article
Field-Weighted Citation Impact
1.46
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Gesture and Voice-Based Automation System for Home Surveillance

C Jayanth, Prithiv Raj, Sanjay Oli, B Santhosh et al.
2 citations
IoT-based Smart Home Systems
1.46
article

Gesture and Voice-Based Automation System for Home Surveillance

C Jayanth, Prithiv Raj, Sanjay Oli, B Santhosh, Ankit Kandpal
article en
2 citations

Abstract

Gesture and voice-based automation innovations are being examined because of the developing interest for reliable and smart home security frameworks. In this study, a MATLAB-based home reconnaissance automation framework in light of voice and motion is created and reproduced. To perceive, interpret, and answer user developments and vocal orders, the proposed framework utilizes modern sign handling and AI calculations. Although nonverbal communication through gestures has been around for a while, gesture-based human-computer interaction (HCI) is growing in popularity in the present day. The conventional interface uses a number of tools, including markers, physical controllers, and gloves, to increase the recognition rate. This work offers a novel marker less method for capturing gestures without the use of expensive gear or any obstacles. In order to enable disabled individuals to operate household appliances using their own speech and gesture commands, we present in this paper a speech and Gesture Driven automation system for homes with a central controller. The framework joins programmed reactions with continuous observation highlights to further develop security, openness, and user solace. Framework reenactment, including control rationale, voice order handling, and motion identification, is finished with MATLAB. The accuracy, reactivity, and common sense of the innovation are shown by the recreation results.

Dr. Hari Singh Gour University (IN)
Openalex Percentile: Top 18%
IoT-based Smart Home Systems
1.46
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.