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}
{"title":"Vehicle Road Lane Extraction Using Millimeter-Wave Radar Imagery for Self-Driving Applications","authors":"Weixue Liu;Yuexia Wang;Jiajia Shi;Quan Shi;Zhihuo Xu","doi":"10.1109/LSENS.2024.3456120","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3456120","url":null,"abstract":"Millimeter-wave (MMW) radar imaging technology has advanced significantly, providing high-resolution images crucial for various self-driving applications. This letter presents a novel approach for extracting road surfaces within a vehicle's lane using MMW radar imagery. First, the zonal connected area detection algorithm with sliding windows effectively detects feature points in the radar images. Second, the feature point classification algorithm, utilizing horizontal offset values, preliminarily identifies the feature points for the vehicle's lane boundary. Finally, the feature points are refined based on horizontal density, followed by boundary fitting to extract the road surface accurately. Experiments were conducted on three different scenarios and three distinct datasets to verify the effectiveness and generalization ability of the algorithm.","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":"142235827","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}
{"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}
{"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}
{"title":"Evaluation of Jenks Natural Breaks Clustering Algorithm for Changepoint Identification in Streaming Sensor Data","authors":"Mahdi Saleh","doi":"10.1109/LSENS.2024.3456292","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3456292","url":null,"abstract":"This letter evaluates the performance of a nonsupervised clustering method for identifying abrupt changepoints in streaming sensor data. The proposed method utilizes the Jenks natural breaks (JNB) algorithm, applied in near real time using sliding temporal windows to analyze sections of sensor data and identify instances of significant phase changes. It is suitable for sensing applications that rely on detecting instantaneous changes in the sensed data for fast decisions, such as fire alarms, fault detection, and activity recognition. The method was applied to a custom dataset from 12 electrodes transitioning among different materials. Performance was evaluated based on detection accuracy and delay comparisons. Results demonstrate that applying JNB in a sliding window with a step size of half its length achieves the highest detection accuracy and the lowest error delay compared to nonoverlapping windows.","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":"142313083","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}
Koundinya Varma;Brahad Kokad;Anis Fatema;Aftab M. Hussain
{"title":"Surface Characterization by Plantar Pressure Analysis Using Low-Cost in-Shoe Sensor Array","authors":"Koundinya Varma;Brahad Kokad;Anis Fatema;Aftab M. Hussain","doi":"10.1109/LSENS.2024.3455429","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3455429","url":null,"abstract":"Analysis of foot pressure, also known as plantar pressure analysis, plays a pivotal role in biomedical assessments related to posture and gait analysis. Extensive research has been conducted on leveraging this technique for clinical purposes, leading to the development of flexible pressure sensors. In this letter, we present the use of in-shoe flexible pressure sensor array for determining the nature of the walking surface. The sensor system is fabricated using eight low-cost and robust, in-shoe pressure sensors that leverage the piezoresistivity of velostat. The sensor array was characterized for four different surface types. Random Forest (RF) algorithm was used to classify the surfaces with 86% accuracy. Based on this analysis, we propose a novel method for analyzing various surfaces based on their attributes such as firmness, rigidity, and penetrability. Such a device can be used for ascertaining surface characteristics after construction, or playing surfaces in a stadium.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142235693","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}
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}
{"title":"A Miniature pH Sensor in a Subcutaneous Injection Needle for Biofluid Sensing","authors":"Khengdauliu Chawang;Sen Bing;Jon Stellar;J.-C. Chiao","doi":"10.1109/LSENS.2024.3454486","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3454486","url":null,"abstract":"The pH value in bodily fluids is a crucial diagnostic marker. Conventional glass-rod pH sensors display reliability in aqueous solutions, but the pH-sensitive glass membrane makes them prone to inaccuracies in viscous solutions due to elevated junction potentials and bulky design hinders miniaturization. To overcome this issue, this work introduces a new pH sensor design and fabrication that enables miniaturization and reliability in aqueous and viscous solutions and facilitates insertion into a needle for in vivo monitoring. Utilizing a printing technique for the application of iridium oxide (IrOx) and silver/silver chloride coating on a single flexible polyimide substrate offers cost-effectiveness and production scalability. The sensor then is tailored with a sharp blade to a narrow strip that fits into a 20-gauge needle. The electrochemical measurements demonstrate that electrodes produced through this method demonstrate an accuracy of up to 0.1 pH within a narrow pH range (7.35–7.45) in buffer solutions and real human serum tests.","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":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10664516","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142169586","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Coarse-to-Fine Sparse 3-D Reconstruction in THz Light Field Imaging","authors":"Abdulraouf Kutaish;Miguel Heredia Conde;Ullrich Pfeiffer","doi":"10.1109/LSENS.2024.3454567","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3454567","url":null,"abstract":"Terahertz (THz) light field imaging inherently allows capturing the 3-D geometry of a target but at the cost of an increased data volume. Compressive sensing techniques are instrumental in minimizing data acquisition requirements. However, they often rely on computationally expensive sparse reconstruction approaches with high memory footprint. This research introduces an advanced coarse-to-fine (CTF) sparse 3-D reconstruction strategy aimed at enhancing the precision of reconstructed images while significantly reducing computational load and memory footprint. By employing a sequence of sensing matrices of increasing resolution, our approach avoids falling into an ill-conditioned inversion and strikes a balance between reconstruction quality and computational efficiency. We demonstrate the effectiveness of this CTF strategy through its integration with several established algorithms, including basis pursuit (BP), fast iterative shrinkage-threshold algorithm (FISTA), and others. The results showcase the potential of the CTF approach to improve 3-D image reconstruction accuracy and processing speed in THz light field imaging.","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":"142313082","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}
{"title":"Efficient Cooperative Spectrum Sensing in UAV-Assisted Cognitive Wireless Sensor Networks","authors":"Haoyu Liang;Jun Wu;Tianle Liu;Hao Wang;Weiwei Cao","doi":"10.1109/LSENS.2024.3454718","DOIUrl":"https://doi.org/10.1109/LSENS.2024.3454718","url":null,"abstract":"In order to meet the frequency requirements of unmanned aerial vehicles (UAVs), sensors assist UAVs in cooperative spectrum sensing (CSS) to identify available spectrum resources and opportunistically access the channel being underutilized by the primary user (PU). However, in such a UAV-assisted cognitive wireless sensor network (CWSN), the cooperative mode among multiple UAVs with built-in sensors may incur high overhead costs, resulting in the spectrum sensing performance degradation. Therefore, we introduce a differential sequential 1, which incorporates a differential mechanism and leverages the sequential idea based on the classical voting rule to enhance the CSS performance and efficiency. In view of this, we formulate three scenarios to characterize the PU activity and introduce a multislot cooperative mode within a single UAV with built-in sensor to realize cooperative gain. Finally, simulation results demonstrate that the superiority of our proposal with respect to the detection performance and sample size is evident.","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":"142235826","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}