IEEE Sensors Letters最新文献_第8页

Flexible X-Band Sensing Phased Array With Integrated Strain Sensors for Curvature-Compensated Beam Steering 带有集成应变传感器的柔性x波段传感相控阵用于曲率补偿波束转向

IF 2.4

IEEE Sensors Letters Pub Date : 2026-08-01 Epub Date: 2026-07-06 DOI: 10.1109/LSENS.2026.3710260

Jingxiang Wang;Gong Chen;Koen Mouthaan

{"title":"Flexible X-Band Sensing Phased Array With Integrated Strain Sensors for Curvature-Compensated Beam Steering","authors":"Jingxiang Wang;Gong Chen;Koen Mouthaan","doi":"10.1109/LSENS.2026.3710260","DOIUrl":"https://doi.org/10.1109/LSENS.2026.3710260","url":null,"abstract":"A cost-effective, wideband, flexible dipole strip antenna array with integrated curvature sensors for adaptive beamforming is presented for X-band intelligent sensing applications. The antenna elements, realized as printed dipoles on rigid substrate, use a plug-in pin header for connection to the feed network on the flexible substrate. A parasitic dipole is included to increase the bandwidth of the printed dipole elements. Four antenna elements are printed on a strip of Rogers RO4350B to form a 1×4 subarray module. Four of these modules are plugged into a thin and flexible Pyralux substrate. A feed network is fabricated on the back side of the Pyralux substrate, with metalized through holes for the connection with the dipole strip subarrays. Strain sensors are integrated into the flexible feed network on the Pyralux substrate to sense the curvature of the flexible array. Using the curvature information, a phase compensation method is applied to compensate for the phase difference, caused by the curvature of the antenna array, to recover the undistorted antenna pattern. Experimental results show that the undistorted pattern can be effectively recovered.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"10 8","pages":"3504204-3504204"},"PeriodicalIF":2.4,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148627423","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

Analysis of Neck and Shoulder Muscle Fatigue Conditions Using Wavelet Scattering Transform of Wireless Electromyography and Convolutional Neural Network 基于无线肌电小波散射变换和卷积神经网络的颈肩肌肉疲劳状态分析

IF 2.4

IEEE Sensors Letters Pub Date : 2026-08-01 Epub Date: 2026-07-13 DOI: 10.1109/LSENS.2026.3712298

Gobinath Kaliyaperumal;Thomas Renald C J;Divya Bharathi Krishnamani;Karthick P A;Ramakrishnan Swaminathan

{"title":"Analysis of Neck and Shoulder Muscle Fatigue Conditions Using Wavelet Scattering Transform of Wireless Electromyography and Convolutional Neural Network","authors":"Gobinath Kaliyaperumal;Thomas Renald C J;Divya Bharathi Krishnamani;Karthick P A;Ramakrishnan Swaminathan","doi":"10.1109/LSENS.2026.3712298","DOIUrl":"https://doi.org/10.1109/LSENS.2026.3712298","url":null,"abstract":"The widespread use of digital devices and prolonged sedentary work has increased the incidence of neck muscle fatigue, posing a growing occupational health concern. Later, it leads to musculoskeletal disorders (MSD). Muscle fatigue is a significant clinical indicator preceding MSD. Surface electromyography (sEMG) provides a noninvasive means for fatigue assessment; however, its nonlinear and nonstationary characteristics complicate signal analysis. In this study, an attempt has been made to investigate fatigue-induced variations in neck and shoulder sEMG signals using the wavelet scattering transform (WST) combined with convolutional neural network (CNN). Wireless sEMG data were acquired from the sternocleidomastoid (SCM), splenius capitis, and trapezius muscles of 50 healthy subjects following a standardized protocol. The first and last ten-second segments of the sEMG signals are categorized as nonfatigue and fatigue zones, respectively. The signals are preprocessed and analyzed using WST to obtain stable time–frequency representations, which is further subjected to classification using CNN. The proposed CNN model results in the maximum accuracy of 89% for SCM, among the three muscles. Further, the performance is compared with the conventional features, such as slope sign change, zero crossing, median frequency, and mean frequency. The results highlight the potential of integrating wavelet-based multiresolution analysis with deep learning models for effective muscle fatigue monitoring and early MSD prevention.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"10 8","pages":"7003804-7003804"},"PeriodicalIF":2.4,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148627579","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

Heat-Laminated P(VDF-TrFE) Piezoelectric Sensors for Rigid and Textile Substrates 用于刚性和纺织基板的热叠层P（VDF-TrFE）压电传感器

IF 2.4

IEEE Sensors Letters Pub Date : 2026-07-01 Epub Date: 2026-06-15 DOI: 10.1109/LSENS.2026.3703485

Remmi Calvo Guzman;Vilho Ranto;Karem Lozano Montero;Matti Mäntysalo

{"title":"Heat-Laminated P(VDF-TrFE) Piezoelectric Sensors for Rigid and Textile Substrates","authors":"Remmi Calvo Guzman;Vilho Ranto;Karem Lozano Montero;Matti Mäntysalo","doi":"10.1109/LSENS.2026.3703485","DOIUrl":"https://doi.org/10.1109/LSENS.2026.3703485","url":null,"abstract":"The rising interest in wearable technologies over the years has been fueled by the development of innovative electronic devices. However, their integration into human-electronic systems still presents challenges. For example, the integration of sensing elements onto electronic systems requires the optimization of fabrication processes. This study evaluates the integration of flexible piezoelectric sensors onto rigid and soft surfaces. The sensors were fabricated using poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS) as electrodes and polyvinylidene fluoride-trifluoro ethylene [P(VDF-TrFE)] as the active layer. The devices were laminated onto commercial rigid printed circuit boards (PCBs) and fabric to evaluate their application in functional expansion and wearable electronics. The lamination process was performed at temperatures near the curie temperature of P(VDF-TrFE) (100 °C). The results showed a reduction in ferroelectric and piezoelectric properties. Samples laminated on PCB showed a reduction of 44% in remnant polarization (<italic>Pr), while samples laminated on fabric showed a reduction of 33%. Laminated samples exhibited piezoelectric response; however, sensitivity (<italic>d33) measurements showed significant increase due to multiaxial mode bending deformation. Likewise, voltage output (<italic>Vout) measurements showed a reduction of 97% for samples laminated on PCB, while samples laminated on fabric showed an increase of 200%. These results demonstrate that heat lamination has potential as an integration method for wearable sensor systems.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"10 7","pages":"1501304-1501304"},"PeriodicalIF":2.4,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11563434","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148505385","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}

引用次数: 0

Biocompatible Building-Block-Inspired Brick-Shaped Triboelectric Nanogenerator for Energy Harvesting 生物相容性积木启发的砖形摩擦电纳米发电机用于能量收集

IF 2.4

IEEE Sensors Letters Pub Date : 2026-07-01 Epub Date: 2026-06-04 DOI: 10.1109/LSENS.2026.3700530

Muhammad Umaid Bukhari;Alimohammad Haji Adineh;Valliammai Palaniappan;Anthony J Hanson;Abdulrahman Olalekan Yusuf;Pranava Avuthu;Simin Masihi;Dinesh Maddipatla;Bradley J Bazuin;Massood Z. Atashbar

{"title":"Biocompatible Building-Block-Inspired Brick-Shaped Triboelectric Nanogenerator for Energy Harvesting","authors":"Muhammad Umaid Bukhari;Alimohammad Haji Adineh;Valliammai Palaniappan;Anthony J Hanson;Abdulrahman Olalekan Yusuf;Pranava Avuthu;Simin Masihi;Dinesh Maddipatla;Bradley J Bazuin;Massood Z. Atashbar","doi":"10.1109/LSENS.2026.3700530","DOIUrl":"https://doi.org/10.1109/LSENS.2026.3700530","url":null,"abstract":"The rapid growth of wearable electronics and sensors has driven a substantial increase in the use of portable batteries which in turn leads to regular battery replacement and substantial electronic waste. Improper disposal of these batteries poses serious environmental and health risks by releasing toxic chemicals into soil and waterways. Recently, energy harvesters have emerged as sustainable energy generators due to their compact size, low cost, and ability to utilize ambient mechanical energy. Among these energy harvesters, triboelectric nanogenerators (TENGs) have attracted particular attention due to their high output voltage, flexible design, and efficient operation at low frequencies. Although TENGs have exhibited promising performance throughout various applications, their adoption is often limited by complex fabrication processes and the use of expensive materials. In this work, an economical, easily fabricated building-block-inspired triboelectric nanogenerator (BBI-TENG) is presented using biocompatible and readily available materials, such as polydimethylsiloxane (PDMS) and copper tape. The proposed TENG generates an open-circuit voltage (<italic>Voc) of 43.15 V, a short-circuit current (<italic>Isc) of 2.66 μA, and a maximum output power of 10.66 μW/cm2. BBI-TENG converts simple hand tapping into electrical energy capable of operating low-power electronics, and its novel modular brick-shaped design enables scalable stacking to increase electrical energy output. Furthermore, visually engaging colored BBI-TENG can help promote the basic concepts of renewable energy to children, making it well-suited for hands-on early science, technology, engineering, and mathematics (STEM) education.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"10 7","pages":"2503204-2503204"},"PeriodicalIF":2.4,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148505661","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

Harnessing Transformer-Based Language Models for Interstitial Lung Diseases Diagnosis From Time-Frequency Encodings of Lung Sounds 利用基于变压器的语言模型从肺音时频编码诊断间质性肺疾病

IF 2.4

IEEE Sensors Letters Pub Date : 2026-07-01 Epub Date: 2026-06-10 DOI: 10.1109/LSENS.2026.3702525

Ayushi Pal;Naseem Babu;Udit Satija;Jimson Mathew;Hugeng Hugeng;Choo W. R. Chiong

{"title":"Harnessing Transformer-Based Language Models for Interstitial Lung Diseases Diagnosis From Time-Frequency Encodings of Lung Sounds","authors":"Ayushi Pal;Naseem Babu;Udit Satija;Jimson Mathew;Hugeng Hugeng;Choo W. R. Chiong","doi":"10.1109/LSENS.2026.3702525","DOIUrl":"https://doi.org/10.1109/LSENS.2026.3702525","url":null,"abstract":"Early and accurate diagnosis of interstitial lung disease (ILD) remains a major clinical challenge due to its wide range of symptoms and overlapping sound patterns with other pulmonary disorders, such as fine crackles and squawks. This letter proposes a transformer-based language model (LM) framework for classifying respiratory sounds associated with ILD using three complementary time–frequency (TF) encodings: constant-Q transform, mel-spectrograms, and mel-frequency cepstral coefficients. Each TF representation is first derived from preprocessed lung sound recordings and then reshaped into token-like sequences suitable for processing by pretrained transformer backbones, specifically GPT-2, GPT-2-medium, Qwen, and Qwen-chat models. The proposed framework is evaluated on publicly available respiratory sound datasets, BRACETS and KAUH, and performance is reported as average accuracy, recall, precision, and F1-score to provide a comprehensive assessment. The proposed framework underscores the potential of transformer-based LM architectures operating on TF representations of respiratory sound (RS) as a promising direction for developing robust, noninvasive, and scalable tools for early ILD screening and for discovering reliable acoustic biomarkers in clinical and real-world applications.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"10 7","pages":"6006404-6006404"},"PeriodicalIF":2.4,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148442368","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

IEEE Sensors Letters Subject Categories for Article Numbering Information 用于物品编号信息的IEEE传感器字母主题分类

IF 2.4

IEEE Sensors Letters Pub Date : 2026-07-01 Epub Date: 2026-07-20 DOI: 10.1109/LSENS.2026.3710958

引用次数: 0

Pixel Watch: Robust Heart Rate Sensing From Multipath PPG and On-Device Deep Learning Trained on 10 000 Hours of Free-Living and Fitness Data 像素手表：从多路径PPG和设备上深度学习训练的10000小时自由生活和健身数据稳健的心率传感

IF 2.4

IEEE Sensors Letters Pub Date : 2026-07-01 Epub Date: 2026-03-18 DOI: 10.1109/LSENS.2026.3694237

Daniel Roggen;Megan Walker;Yojan Patel;Shyam Tailor;Dimitris Spathis;Matt Wimmer;Brennan Garrett;Dan Howe;Abhinuv Pitale;Hamed Vavadi;Tien Le;Steve Diamond;Oleksiy Vyalov;Vik Sharma;Pete Richards;Tracy Giest;Erika Siegel;Tuan Phan;Sam Mravca;Derrick Vickers;Benjamin Stone;Katarina Vukosavljević;Justin Phillips;YongSuk Cho;Stefanie Hollidge;Antony Siahaan;Soren Brage;Shwetak Patel;Robert Harle

{"title":"Pixel Watch: Robust Heart Rate Sensing From Multipath PPG and On-Device Deep Learning Trained on 10 000 Hours of Free-Living and Fitness Data","authors":"Daniel Roggen;Megan Walker;Yojan Patel;Shyam Tailor;Dimitris Spathis;Matt Wimmer;Brennan Garrett;Dan Howe;Abhinuv Pitale;Hamed Vavadi;Tien Le;Steve Diamond;Oleksiy Vyalov;Vik Sharma;Pete Richards;Tracy Giest;Erika Siegel;Tuan Phan;Sam Mravca;Derrick Vickers;Benjamin Stone;Katarina Vukosavljević;Justin Phillips;YongSuk Cho;Stefanie Hollidge;Antony Siahaan;Soren Brage;Shwetak Patel;Robert Harle","doi":"10.1109/LSENS.2026.3694237","DOIUrl":"https://doi.org/10.1109/LSENS.2026.3694237","url":null,"abstract":"The Pixel Watch 2 (PW2) is the first Google smartwatch to combine multipath photoplethysmography (PPG) with deep learning-based heart rate inference, designed to significantly improve sensing accuracy during motion-heavy activities. The device processes ten optical channels using an on-device, 15-layer temporally dilated convolutional neural network (<inline-formula><tex-math>$sim$</tex-math></inline-formula>300 K parameters) to yield a 1-Hz heart rate output. Crucial to this model’s performance was its training on a massive dataset comprising 10 000 h of data from 962 participants, curated from a broader corpus of controlled and free-living activities. We evaluated the PW2’s sensing performance across two independent validation sets: an in-house fitness dataset (229 participants, 250 h) and an external Free-living dataset (27 participants, 1000+ hours). The system achieved 95% limits of agreement (LoA) of −10.34 to 8.66 beats per minute (BPM) during exercise and −6.57 to 7.48 BPM during free-living activities, demonstrating substantially tighter error margins than previous Google devices. Finally, we discuss key design lessons, emphasizing that large-scale deep learning was instrumental in fully leveraging multipath PPG hardware over traditional signal processing approaches.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"10 7","pages":"5503804-5503804"},"PeriodicalIF":2.4,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11523053","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148442190","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}

引用次数: 0

Sensor Data Quality Scoring for MEMS Accelerometers via Spectral Anomaly Detection 基于光谱异常检测的MEMS加速度计数据质量评分

IF 2.4

IEEE Sensors Letters Pub Date : 2026-07-01 Epub Date: 2026-03-25 DOI: 10.1109/LSENS.2026.3696864

Tim Ruhland;Kilian Müller;Maximilian Lübke;Norman Franchi

{"title":"Sensor Data Quality Scoring for MEMS Accelerometers via Spectral Anomaly Detection","authors":"Tim Ruhland;Kilian Müller;Maximilian Lübke;Norman Franchi","doi":"10.1109/LSENS.2026.3696864","DOIUrl":"https://doi.org/10.1109/LSENS.2026.3696864","url":null,"abstract":"Low-cost micro-electromechanical systems (MEMS) accelerometers are widely deployed for industrial condition monitoring, yet their data streams lack intrinsic quality metrics. This can lead to false alarms caused by sensor artifacts rather than genuine process anomalies, and to diminished trust in condition monitoring systems. This work introduces a lightweight, unsupervised framework for computing a continuous quality of sensing (QoS) score. A feedforward autoencoder learns a spectral fingerprint from power spectral density and time-domain features during nominal operation—the reconstruction error is then mapped to a normalized QoS parameter. The proposed approach isolates intrinsic MEMS degradation mechanisms. The framework is validated against physically grounded noise simulation: thermo-mechanical noise, vibration rectification error (VRE), and flicker-induced bias instability. The autoencoder detects all three degradation mechanisms, with VRE producing the steepest response. Experiments under laboratory shaker excitation, electric motor vibration, and idle conditions show that real operational data yield the optimal detection sensitivity. The lightweight architecture supports future edge-oriented deployment and improves the trustworthiness of Industrial Internet of Things (IIoT) sensor systems.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"10 7","pages":"1501004-1501004"},"PeriodicalIF":2.4,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148442214","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

End-to-End Learning With Modified Focal Loss for End-Fire Target Bearing Estimation in Passive SONAR Using Simulated and Real-Time Datasets 基于改进焦损的端到端被动声呐射击末目标方位估计

IF 2.4

IEEE Sensors Letters Pub Date : 2026-07-01 Epub Date: 2026-06-12 DOI: 10.1109/LSENS.2026.3701785

Pawan Kumar;Karan Nathwani

{"title":"End-to-End Learning With Modified Focal Loss for End-Fire Target Bearing Estimation in Passive SONAR Using Simulated and Real-Time Datasets","authors":"Pawan Kumar;Karan Nathwani","doi":"10.1109/LSENS.2026.3701785","DOIUrl":"https://doi.org/10.1109/LSENS.2026.3701785","url":null,"abstract":"Accurate target bearing (TB) estimation in passive SONAR is challenging in the presence of self-noise. This issue is particularly severe in the end-fire (EF) region, where conventional methods suffer from poor resolution. This work proposes end-to-end deep learning frameworks that directly map array measurements to TB. The approach eliminates the need for an additional beamforming stage. Two CNN-based frameworks are designed, each consisting of multiple convolutional blocks and fully connected layers. The aim is to extract spatial–temporal features from the input data. The two frameworks are trained with distinct loss formulations to improve robustness to noise and angular obscurity. First, focal angular loss (FAL) incorporates an angular penalty into the focal loss to account for the periodic nature of bearing angles, thereby improving angular sensitivity. Another, modified focal entropy loss (MFEL), adds entropy regularization to the focal loss to reduce overconfident predictions and improve generalization (Lin et al., 2017). The proposed methods are evaluated on both simulated datasets across a range of signal-to-interference-noise ratio (SINR) values under narrowband and wideband conditions, as well as real-time experimental data. The experimental results show that end-to-end (EE)-convolutional neural network (CNN)-FAL achieves high accuracy, particularly in EF regions, while EE-CNN-MFEL provides improved stability and generalization in practical scenarios. Overall, the proposed framework outperforms conventional and existing learning-based methods for TB estimation under challenging conditions.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"10 7","pages":"7003604-7003604"},"PeriodicalIF":2.4,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148442674","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

Conductance-Domain In Situ Single-Point Calibration for an FSR Array Embedded Inside a Handball 手球内嵌FSR阵列的电导域原位单点校准

IF 2.4

IEEE Sensors Letters Pub Date : 2026-07-01 Epub Date: 2026-03-26 DOI: 10.1109/LSENS.2026.3697021

Yuki Yoshinaga;Tomoo Kikuchi;Eri Sato-Shimokawara

{"title":"Conductance-Domain In Situ Single-Point Calibration for an FSR Array Embedded Inside a Handball","authors":"Yuki Yoshinaga;Tomoo Kikuchi;Eri Sato-Shimokawara","doi":"10.1109/LSENS.2026.3697021","DOIUrl":"https://doi.org/10.1109/LSENS.2026.3697021","url":null,"abstract":"Embedding a multichannel force-sensitive resistor (FSR) array inside a sports ball creates a calibration challenge with no precedent in the existing literature: every sensor is permanently sandwiched between the outer leather cover and the inner rubber bladder, subjecting each channel to a structural preload that varies channel-by-channel due to stitching tension and cover-bladder assembly tolerances. Prior FSR calibration practice either eliminates preloads before calibration or treats them as a source of error. This letter shows instead that performing calibration in situ, after full ball assembly in the preloaded state, is not merely acceptable but advantageous. Converting the voltage-divider output to conductance and normalizing by a single 30-N in situ reference measurement causes the structural preload, the pull-down conductance, and the per-channel sensitivity factor to cancel simultaneously in one algebraic step. Applied to a custom 36-channel FSR array (Type A: 30 trapezoidal channels; Type B: Six circular channels, one at the center of each pentagonal panel), this normalization reduces interchannel sensitivity variation substantially for loads of 20 N and above. The normalized conductance exhibits a semilogarithmic linearity with force (<inline-formula><tex-math>$r = 0.975$</tex-math></inline-formula>), consistent with Hertzian contact mechanics and percolation-driven transport, which supports a type-specific two-parameter log-space formula achieving mean absolute percentage error (MAPE) <inline-formula><tex-math>$= 6.3%$</tex-math></inline-formula>, recovering 89% of the accuracy gain of full per-channel five-load calibration (MAPE <inline-formula><tex-math>$= 3.5%$</tex-math></inline-formula>) from a single 30-N in situ measurement per channel.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"10 7","pages":"6006204-6006204"},"PeriodicalIF":2.4,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148442277","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