IEEE Sensors Letters最新文献_第4页

Multiclass Gait Phase Classification From the Temporal Convolutional Network of Wireless Surface Electromyography Measurements 通过无线表面肌电图测量的时空卷积网络进行多分类步态相位分类

IF 2.2

IEEE Sensors Letters Pub Date : 2024-09-10 DOI: 10.1109/LSENS.2024.3453558

V. Mallikarjuna Reddy M;P. S. Pandian;Karthick P A

{"title":"Multiclass Gait Phase Classification From the Temporal Convolutional Network of Wireless Surface Electromyography Measurements","authors":"V. Mallikarjuna Reddy M;P. S. Pandian;Karthick P A","doi":"10.1109/LSENS.2024.3453558","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3453558","url":null,"abstract":"Recent advancements and developments in the field of rehabi- litation lead to the invention of myoelectric control interfaces for patients with disabilities. However, decoding the motion intent from the surface electromyography (sEMG) signals of hamstrings and quadriceps is challenging due to its complex mechanics associated with weight bearing joints and stochastic, nonstationary, and multicomponent behavior of signals. In this letter, a novel approach is proposed for multiclass gait phase classification during level walking using temporal convolutional network (TCN) of sEMG signals. For this purpose, sEMG and inertial measurement unit (IMU) data were recorded concurrently from 20 healthy participants during level walking on treadmill at a speed of 2.5 km/h. sEMG were collected from the muscles, namely, rectus femoris (RF), vastus lateralis (VL), biceps femoris (BF), and semitendinosus (SEM). The IMU measurements of knee flexion/extension data are utilized for labeling the four phases of gait cycle. The root mean square of sEMG epochs is used to design the TCN framework. The results show that the proposed framework has the ability to differentiate the four classes of gait with a maximum accuracy of 86.00% using the myoelectric activity from all the four muscles. The information from the muscle pairs SEM and VL, and RF and BF, yielded the correct detection rate of 83.00% and 84.00%, respectively. In addition, the accuracy is also improved by 6% with TCN when we compare accuracy obtained through convolutional neural network architecture. The findings suggest that the proposed approach is effective in decoding the motion intent of lower limb muscles, which may lead to the development of precise movement control of lower limb prosthesis.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142274963","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Vehicle Road Lane Extraction Using Millimeter-Wave Radar Imagery for Self-Driving Applications 利用毫米波雷达图像提取自动驾驶应用中的车辆道路车道

IF 2.2

IEEE Sensors Letters Pub Date : 2024-09-09 DOI: 10.1109/LSENS.2024.3456120

Weixue Liu;Yuexia Wang;Jiajia Shi;Quan Shi;Zhihuo Xu

引用次数: 0

Enhancing Bluetooth Channel Sounding Performance in Complex Indoor Environments 增强复杂室内环境中的蓝牙信道探测性能

IF 2.2

IEEE Sensors Letters Pub Date : 2024-09-09 DOI: 10.1109/LSENS.2024.3456002

Avik Santra;Igor Kravets;Nazarii Kotliar;Ashutosh Pandey

{"title":"Enhancing Bluetooth Channel Sounding Performance in Complex Indoor Environments","authors":"Avik Santra;Igor Kravets;Nazarii Kotliar;Ashutosh Pandey","doi":"10.1109/LSENS.2024.3456002","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3456002","url":null,"abstract":"The Internet of Things (IoT) relies on accurate distance estimation between devices, crucial for localization in various applications. While received signal strength indicator (RSSI)-based ranging lacks precision and time-of-flight narrow band systems perform poorly, phase-based ranging emerges as the preferred choice for Bluetooth Low Energy (BLE). Infineon's BLE prototype and its performance with a novel processing pipeline based on the minimum variance distortionless response (MVDR) algorithm are presented in this letter. Our pipeline comprises subalgorithms for preprocessing, scene identification, feature selection, feature engineering, and postprocessing. Preprocessing includes zero distance calibration, low-pass filtering, and time history averaging. Scene identification adapts parameters to environmental conditions. MVDR algorithms enable high-resolution feature transformation to project the residual phase correction term to the range domain. Postprocessing includes a tracker and data-dependent adaptation. Postprocessing in conjunction with feature selection tracks the line of sight path, minimizing distance jitter. Our proposed pipeline achieves a \u0000<inline-formula><tex-math>$text{90}{%}$</tex-math></inline-formula>\u0000 peak error of \u0000<inline-formula><tex-math>$leq$</tex-math></inline-formula>\u0000<inline-formula><tex-math>$text{1.6} ,text{m}$</tex-math></inline-formula>\u0000 without data-dependent adaptation and \u0000<inline-formula><tex-math>$leq$</tex-math></inline-formula>\u0000<inline-formula><tex-math>$text{1.2} ,text{m}$</tex-math></inline-formula>\u0000 with data-dependent adaptation and tracking, outperforming existing methods in the literature. This work demonstrates the potential of Infineon's BLE channel sounding for accurate range estimation in IoT applications.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142320443","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Self-Powered Standalone Performance of Thermoelectric Generator for Body Heat Harvesting 用于人体热量收集的热电发生器的自供电独立性能

IF 2.2

IEEE Sensors Letters Pub Date : 2024-09-09 DOI: 10.1109/LSENS.2024.3456289

Anshu Panbude;Pandiyarasan Veluswamy

{"title":"Self-Powered Standalone Performance of Thermoelectric Generator for Body Heat Harvesting","authors":"Anshu Panbude;Pandiyarasan Veluswamy","doi":"10.1109/LSENS.2024.3456289","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3456289","url":null,"abstract":"In this letter, we propose a self-powered thermoelectric generator (TEG) to map out the thermal energy to electricity conversion. The wearable flexible thermoelectric generator (FTEG) could generate electric potential from the human skin and environment. The FTEG comes into consideration as an auxiliary supply/passive sensor for power generation to self-charge mode. In this letter, we study the reliability of the FTEG to resist chemicals, water, and moisture. For long-term reliability of the wearable FTEGs, the electrical, mechanical, and thermal performances are significant. The 8-leg FTEG in outdoor conditions at merely 2 °C temperature gradient between human skin and the environment generates an output potential of 0.63 mV to display its sensitivity to temperature variations. The simple fabrication of the TEG performance is stable under water to demonstrate the weathering protection and can withstand 1300 bending cycles. In addition, the interfacial microstructures are investigated to understand the effects of mechanical stress on the thermoelectric leg and bonding material. The mechanical strength to bend and withstand the electrical parameters without significant changes makes it an outstanding candidate for wearable applications.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142450922","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Evaluation of Jenks Natural Breaks Clustering Algorithm for Changepoint Identification in Streaming Sensor Data 评估用于流式传感器数据中变化点识别的詹克斯自然断裂聚类算法

IF 2.2

IEEE Sensors Letters Pub Date : 2024-09-09 DOI: 10.1109/LSENS.2024.3456292

Mahdi Saleh

引用次数: 0

Surface Characterization by Plantar Pressure Analysis Using Low-Cost in-Shoe Sensor Array 利用低成本鞋内传感器阵列通过足底压力分析进行表面特征描述

IF 2.2

IEEE Sensors Letters Pub Date : 2024-09-06 DOI: 10.1109/LSENS.2024.3455429

Koundinya Varma;Brahad Kokad;Anis Fatema;Aftab M. Hussain

引用次数: 0

Photoacoustic Sensing System for Noninvasive and Real-Time Measurement of Paint's Viscosity in Flowing Conditions 用于在流动条件下非侵入式实时测量涂料粘度的光声传感系统

IF 2.2

IEEE Sensors Letters Pub Date : 2024-09-04 DOI: 10.1109/LSENS.2024.3454764

Abhijeet Gorey;Rajat Das;Chirabrata Bhaumik;Tapas Chakravarty;Arpan Pal

{"title":"Photoacoustic Sensing System for Noninvasive and Real-Time Measurement of Paint's Viscosity in Flowing Conditions","authors":"Abhijeet Gorey;Rajat Das;Chirabrata Bhaumik;Tapas Chakravarty;Arpan Pal","doi":"10.1109/LSENS.2024.3454764","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3454764","url":null,"abstract":"Inline measurement of paint viscosity in the flowing conditions is extremely important for the paint manufacturing industry. This study proposes a noninvasive, cost-effective, inline method to measure paint's viscosity using frequency domain photoacoustic (PA) sensing. Through a PA signal, three different frequency and time domain features, namely, spectral amplitude ratio, acoustic attenuation, and acoustic wave velocity, are extracted. Due to the lower accuracy (<90%) of the aforementioned features, a novel statistical feature, i.e., the harmonic mean is derived from the existing features to enhance the accuracy of the measurement. To mitigate the experimental challenges, the viscosity model is trained from the PA data under static condition and tested for the paint under flowing condition. In the flowing conditions, the accuracy in the measurement is found to be less than 93%. Hence, a correction factor is introduced, which considers the Doppler shift in the PA wave velocity due to the paint flow. With this correction factor, the accuracy of the viscosity measurement is found to be greater than 95%. The developed viscosity model is validated through the fourfold cross-validation and the results are confirmed for their repeatability and tested with different paint samples.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142328402","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Miniature pH Sensor in a Subcutaneous Injection Needle for Biofluid Sensing 用于生物流体传感的皮下注射针头微型 pH 传感器

IF 2.2

IEEE Sensors Letters Pub Date : 2024-09-04 DOI: 10.1109/LSENS.2024.3454486

Khengdauliu Chawang;Sen Bing;Jon Stellar;J.-C. Chiao