Hardware-Accelerated Non-Contact System for Sleep Disorder Monitoring and Analysis

This study analyzes human sleep disorders using non-contact approaches. The proposed approach analyzes periodic limb movement disorder (PLMD) under sleep conditions. This was conceptualized as data capture using a non-contact approach with ultrasonic sensors. The model was designed to estimate PLMD...

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Main Authors: Mangali Sravanthi, Sravan Kumar Gunturi, Mangali Chinna Chinnaiah, G. Divya Vani, Mudasar Basha, Narambhatla Janardhan, Dodde Hari Krishna, Sanjay Dubey
Format: Article
Language:English
Published: MDPI AG 2025-04-01
Series:Sensors
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Online Access:https://www.mdpi.com/1424-8220/25/9/2747
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author Mangali Sravanthi
Sravan Kumar Gunturi
Mangali Chinna Chinnaiah
G. Divya Vani
Mudasar Basha
Narambhatla Janardhan
Dodde Hari Krishna
Sanjay Dubey
author_facet Mangali Sravanthi
Sravan Kumar Gunturi
Mangali Chinna Chinnaiah
G. Divya Vani
Mudasar Basha
Narambhatla Janardhan
Dodde Hari Krishna
Sanjay Dubey
author_sort Mangali Sravanthi
collection DOAJ
description This study analyzes human sleep disorders using non-contact approaches. The proposed approach analyzes periodic limb movement disorder (PLMD) under sleep conditions. This was conceptualized as data capture using a non-contact approach with ultrasonic sensors. The model was designed to estimate PLMD and classify it using real-time sleep data and a machine learning-based random forest classifier. Hardware schemes play a vital role in capturing sleep data in real time using ultrasonic sensors. A field-programmable gate array (FPGA)-based accelerator for a random forest classifier was designed to analyze PLMD. This is a novel approach that aids subjects in taking further medications. Verilog HDL was used for PLMD estimation using a Xilinx Vivado 2021.1 simulation and synthesis. The proposed method was validated using a Xilinx Zynq-7000 Zed board XC7Z020-CLG484.
format Article
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issn 1424-8220
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publishDate 2025-04-01
publisher MDPI AG
record_format Article
series Sensors
spelling doaj-art-719d94257a7f4f78afd21bc232bceca92025-08-20T02:58:44ZengMDPI AGSensors1424-82202025-04-01259274710.3390/s25092747Hardware-Accelerated Non-Contact System for Sleep Disorder Monitoring and AnalysisMangali Sravanthi0Sravan Kumar Gunturi1Mangali Chinna Chinnaiah2G. Divya Vani3Mudasar Basha4Narambhatla Janardhan5Dodde Hari Krishna6Sanjay Dubey7Department of Electronics and Communication Engineering, Koneru Lakshmaiah Education Foundation, Aziz Nagar, Hyderabad 500075, Telangana, IndiaDepartment of Electronics and Communication Engineering, Koneru Lakshmaiah Education Foundation, Aziz Nagar, Hyderabad 500075, Telangana, IndiaDepartment of Electronics and Communications Engineering, B V Raju Institute of Technology, Medak (Dist), Narsapur 502313, Telangana, IndiaDepartment of Electronics and Communications Engineering, B V Raju Institute of Technology, Medak (Dist), Narsapur 502313, Telangana, IndiaDepartment of Electronics and Communications Engineering, B V Raju Institute of Technology, Medak (Dist), Narsapur 502313, Telangana, IndiaDepartment of Mechanical Engineering, Chaitanya Bharati Institute of Technology, Gandipet, Hyderabad 500075, Telangana, IndiaDepartment of Electronics and Communications Engineering, B V Raju Institute of Technology, Medak (Dist), Narsapur 502313, Telangana, IndiaDepartment of Electronics and Communications Engineering, B V Raju Institute of Technology, Medak (Dist), Narsapur 502313, Telangana, IndiaThis study analyzes human sleep disorders using non-contact approaches. The proposed approach analyzes periodic limb movement disorder (PLMD) under sleep conditions. This was conceptualized as data capture using a non-contact approach with ultrasonic sensors. The model was designed to estimate PLMD and classify it using real-time sleep data and a machine learning-based random forest classifier. Hardware schemes play a vital role in capturing sleep data in real time using ultrasonic sensors. A field-programmable gate array (FPGA)-based accelerator for a random forest classifier was designed to analyze PLMD. This is a novel approach that aids subjects in taking further medications. Verilog HDL was used for PLMD estimation using a Xilinx Vivado 2021.1 simulation and synthesis. The proposed method was validated using a Xilinx Zynq-7000 Zed board XC7Z020-CLG484.https://www.mdpi.com/1424-8220/25/9/2747sleep monitoringperiodic limb movement disorder (PLMD)random forest classifierFPGA-based accelerators
spellingShingle Mangali Sravanthi
Sravan Kumar Gunturi
Mangali Chinna Chinnaiah
G. Divya Vani
Mudasar Basha
Narambhatla Janardhan
Dodde Hari Krishna
Sanjay Dubey
Hardware-Accelerated Non-Contact System for Sleep Disorder Monitoring and Analysis
Sensors
sleep monitoring
periodic limb movement disorder (PLMD)
random forest classifier
FPGA-based accelerators
title Hardware-Accelerated Non-Contact System for Sleep Disorder Monitoring and Analysis
title_full Hardware-Accelerated Non-Contact System for Sleep Disorder Monitoring and Analysis
title_fullStr Hardware-Accelerated Non-Contact System for Sleep Disorder Monitoring and Analysis
title_full_unstemmed Hardware-Accelerated Non-Contact System for Sleep Disorder Monitoring and Analysis
title_short Hardware-Accelerated Non-Contact System for Sleep Disorder Monitoring and Analysis
title_sort hardware accelerated non contact system for sleep disorder monitoring and analysis
topic sleep monitoring
periodic limb movement disorder (PLMD)
random forest classifier
FPGA-based accelerators
url https://www.mdpi.com/1424-8220/25/9/2747
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