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INTELLIGENT FABRIC ECOSYSTEM ADAPTIVE TEMPERATURE REGULATION THROUGH IOT AND MACHINE LEARNING FOR PE

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INTELLIGENT FABRIC ECOSYSTEM ADAPTIVE TEMPERATURE REGULATION THROUGH IOT AND MACHINE LEARNING FOR PE

ORDINARY APPLICATION

Published

date

Filed on 6 November 2024

Abstract

The intelligent fabric ecosystem for adaptive tempeniture management using loT and machine learning is a new wearable technology paradigm. Creating dynamic fabrics that adapt in real time to environmentaf circumstances and individual comfort preferences is the goal. The fabric's loT 5 sensors monitor temperature, humidity, and physiological characteristics. Machine learning algorithms analyze real-time data to forecast the wearer's coin fort preferences based on activity levels, previous data, and external variables. This clever device dynamically adjusts cloth thermal qualities like insulation and ventilation. The innovation is personalized learning. As customer input and preferences change, the machine learning model improves its predictions. 10 Throug~ a simple interface, users can manually alter and establish comfort settings. As wearers move through different settings and activities, the Intelligent Fabric Ecosystem adjusts temperature. To avoid overheating, the fabric dynamically changes to preserve comfort as the user transitions from a chilly interior to a warmer outdoor environment. Energy efficiency is a system focus. It uses predictive modeling to forecast environmental and user activity changes to 15 ... 20 25 optimize power usage by activating temperature regulation only when needt:d. This makes wearable fabrics more energy-efficient and comfortable. Finally, loT ~d machine learning in textiles usher in a new wearable technology age. The Intelligent Fabric Ecosystem delivers individualized comfort and advances contemporary, sustainable, energy-efficient, and responsive apparel:

Patent Information

Application ID202441085018
Invention FieldCOMPUTER SCIENCE
Date of Application06/11/2024
Publication Number46/2024

Inventors

NameAddressCountryNationality
E SWARNALATHAAssistant Professor, DEPARTMENT OF ELECTRONICS AND COMMUNICATION ENGINEERING GURU NANAK INSTITUTE OF TECHNOLOGY IBRAHIMPATNAM R. R DISTRICT HYDERABAD 501506 TELANGANAIndiaIndia
Dr. N. MOHANKUMARProfessor, Symbiosis institute of Technology, Nagpur Campus, Symbiosis International (Deemed University), Pune , Gat. No.l67,168,169, Village Mauje-Wathoda I Bhandewadi , Nagpur , Maharashtra-440 008IndiaIndia
BHARAT TIDKEAssistant Professor, Symbiosis institute of Technology, Nagpur Campus, Symbiosis International (Deemed University), Pune , Gat. No.l67,168,169, Village Mauje-Wathoda I Bhandewadi , Nagpur , Maharashtra-440 008IndiaIndia
PRAMOD K PANDEY.Assistant Professor, Symbiosis institute of Technology, Nagpur Campus, Symbiosis International (Deemed University), Pune , Gat. No.l67,168,169, Village Mauje-Wathoda I Bhandewadi , Nagpur , Maharashtra-440 008IndiaIndia
S.MURUGANAdjunct Professor, department of Biomedical Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Saveetha Nagar, Thandalam, Chennai, Tamil Nadu-602 105.IndiaIndia
C. SRINIVASANAdjunct Professor, department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha institute of Medical and Technical Sciences, Saveetha University , Saveetha Nagar, Thandalam, Chennai, TamilNadu-602105IndiaIndia

Applicants

NameAddressCountryNationality
E SWARNALATHAAssistant Professor, DEPARTMENT OF ELECTRONICS AND COMMUNICATION ENGINEERING GURU NANAK INSTITUTE OF TECHNOLOGY IBRAHIMPATNAM R. R DISTRICT HYDERABAD 501506 TELANGANAIndiaIndia
Dr. N. MOHANKUMARProfessor, Symbiosis institute of Technology, Nagpur Campus, Symbiosis International (Deemed University), Pune , Gat. No.l67,168,169, Village Mauje-Wathoda I Bhandewadi , Nagpur , Maharashtra-440 008IndiaIndia
BHARAT TIDKEAssistant Professor, Symbiosis institute of Technology, Nagpur Campus, Symbiosis International (Deemed University), Pune , Gat. No.l67,168,169, Village Mauje-Wathoda I Bhandewadi , Nagpur , Maharashtra-440008IndiaIndia
PRAMOD K PANDEYAssistant Professor, Symbiosis institute of Technology, Nagpur Campus, Symbiosis International (Deemed University), Pune , Gat. No.l67,168,169, Village Mauje-Wathoda I Bhandewadi , Nagpur , Maharashtra-440 008IndiaIndia
S.MURUGANAdjunct Professor, department of Biomedical Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Saveetha Nagar, Thandalam, Chennai, Tamil Nadu-602 105.IndiaIndia
C. SRINIVASANAdjunct Professor, department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha institute of Medical and Technical Sciences, Saveetha University , Saveetha Nagar, Thandalam, Chennai, TamilNadu-602105IndiaIndia

Specification

At the crossroads of fashion, technology, and materials sc•ence, intelligent textiles have the
potential to completely transform the way we see imd engage with clothing. From simple textiles,
we are seeing the evolution of complex systems that can adapt in real-time to our surroundings
5 and our preferences, providing a level of customization and comfort that has never been seen
before. This emerging field is called "Intelligent Textiles," and it uses innovative technologies
like the Internet of Things (loT) and machine learning to make clothes that are dynamic,
responsive, and environmentally friendly. Constant investigation into the potential of intelligent
fabrics is expanding the frontiers of this rapidly evolving area of innovation. The possibilities are
10 endless, ranging from athletic performance wear to casual wear, and they point to a future when
our clothes contribute to our health and awareness of the environment. An exciting new chapter
in the history of wearable technology has begun with intelligent fabrics, which combine form
with function, aesthetics, and sustainability
The innovation stems from the rising demand for improved wearable technology that improves
comfort and adjusts to changing environmental conditions. Traditional fabrics typically fail to
regulate heat, causing discomfort in various settings. loT and machine learning in clothing are
5 becoming more popular as smart technology advances. The development· of sensors and data
processing allows textiles to monitor and adapt to physiological and environmental conditions in
real time. In this novel technique, a dynamic system customizes thermal characteristics
depending on individual preferences and situational settings to overcome traditional textile
limits. Intelligent textiles can optimize electricity utilization and provide functional advantage·s,
10 according to research. Sustainable and responsive clothing solutions are in demand as society
grows more health-conscious and ecologically conscientious. This idea advances wearable
technology by combining fashion and utility to build an ecosystem that improves user
experience, comfort, and sustainability. The intelligent fabric ecosystem uses cutting-edge
technology to reinvent personal clothing. The Intelligent Fabric Ecosystem, which uses loT and machine learning to regulate temperature,
is a breakthrough in wearable technology. This unique fabric uses smart sensors to continually
monitortemperature, humidity, and physiological data to adapt to the wearer's comfort demands.
5 Machine learning algorithms anticipate individual preferences based on historical activities and
external circumstances, allowing the fabric to modif'y its thennal properties-such as insulation
and ventilation--accordingly. This tailored strategy optimizes power usage and comfort in
different activities and situations using predictive modeling. The technology regulates
temperature just when needed, saving electricity. An easy interface lets users change their
10 comfort settings for a personalized experience. The Intelligent Fabric Ecosystem enables a new
age of smart clothing that combines beauty and utility, changing how people use their clothes:
15
This technology improves comfort and well-being and meets current society's desire for
sustainable and responsive textiles Innovative wearable technology like the Intelligent Fabric Ecosystem revolutionizes human
comfort via adaptive temperature management. This technology uses powerful JoT sensors and
machine learning algorithms in the fabric to dynamically adapt to environmental changes and
5 comfort preferences. A network of sensors in the fabric monitors temperature, humidity, skin
temperature, and heart rate. Real-time data gathering provides a complete picture of the wearer's
comfort and surroundings. Data analysis relies on the ecosystem's machine learning component.
The fabric can predict comfort preferences depending on activity levels and environmental
circumstances using algorithms that learn from user behavior and historical data. To avoid
10 overheaiing during exercise, the fabric may boost ventilation, while in colder situations, it may
increase insulation. This predictive feature keeps users comfortable regardless of activity or
surroundings.
Energy efficiency is crucial to the Intelligent Fabric Ecosystem. The system uses predictive
modeling -to engage temperature regulating systems only when needed; saving energy and -
15 electricity. An intuitive interface lets users manually alter comfort settings for a tailored
experience. This technology might affect ordinary clothes, workplace, and medicinal textiles as
well as sports and fitness gear. The Intelligent Fabric Ecosystem advances sustainable,
responsive, and healthy clothing. This innovation improves user experience and personalizes
textile interactions by combining aesthetics, utility, and technology, opening the road for
20 intelligent clothes
Detailed Description of Drawings
(I) Figure (i) shows the Block Diagram ·
(2) Figure (ii) shows the Power Adapter
The Raspberry Pi power adapter usually offers enough power for reliable operation. Depending
5 on the Raspberry Pi model, it has micro-USB or USB-C. The adapter converts AC mains power
to DC ·voltage, typically -5V output with various current ratings depending on model and
peripherals. A basic Raspberry Pi adapter may produce 5V at 2A, but the Raspberry Pi 4 needs a
more robust adapter with 5V at 3A to accommodate power-hungry components and peripherals.
(3) Figure (iii) shows the Raspberry pi 4·
10 The Raspberry Pi 4 Model B is a flexible single-board computer with several advancements over
its predecessors. Its 1.5 GHz Broadcom BCM2711 quad-core ARM Cortex-A 72 CPU boosts
performance for demanding workloads. Multitasking and complicated programs are supported by
the board's 2GB, 4GB, or 8GB LPDDR4 RAM. The Raspberry Pi 4 has two USB 3.0 ports, two -Cll USB 2.0 ports, and a Gigabit Ethernet connector for fast network access. Dual micro-HOM!
C)
· ~ 15 connections accommodate up to 4K monitors. The board has a 40-pin GPIO header for
Cll connecting sensors, modules, and other devices.
(4) Figure (iv) shows DHTI I module
The DHTI I is a digital temperature and humidity sensor compatible with Raspberry Pi. Tt
provides accurate readings of environmental conditions, essential for the Intelligent Fabric
20 Ecosystem. The sensor operates with a simple one-wire interface, making it easy to integrate into
various projects.
(5) Figure (v) shows temp module
The LM35 ambient temperature sensor offers precise temperature measurements and is ideal for
monitoring fabric temperature changes. Its linear output makes it compatible with the Raspberry
25 Pi's ADC, facilitating real-time data processing.
The BH--I750 ambient light sensor measures light intensity in lux, assisting the ecosystem in
adjusting fabric properties based on lighting conditions. Its J2C interface ensures seamless
communication with the Raspberry Pi, enhancing the overall adaptability of the intelligent fabric
system.
We Claim
The above invention Intelligent Fabric Ecosystem Adaptive Temperature Regulation through JoT
and Machine Learning for Personalized Comfort in Wearable Textiles comprises of:
I. A wearable textile with loT sensors that detect ambient variables and modifY thermal
characteristics in real time.
2. A fabric-integrated machine learning algorithm that predicts and adjusts temperature
based on user behavior and preferences.
3. A user interface that lets people manually adjust comfort settings to their liking.
4. When needed, an energy-efficient system triggers temperature regulating systems to
10 · optimize power usage for sustainable wearable textiles.
15
25
5. Adaptive temperature management for tailored comfort m sporting wear, outdoor
garments, smart home textiles, and medical wearables

Documents

NameDate
202441085018-Form 1-061124.pdf08/11/2024
202441085018-Form 2(Title Page)-061124.pdf08/11/2024

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