Textile Capacitive Sensor Research Articles

Preliminary investigation of the effect of artificial sweat on a wearable textile sensing system

Introduction Textile wearable systems for human movement monitoring are increasingly popular. However, few examples report on robustness to sweat, which is relevant for use in real life. Some reported the effect of artificial sweat like phosphate buffered saline (PBS; Lin et al., 2022) or simply moisture (Xu et al., 2020) on custom materials. We previously developed an all-textile wireless sensing platform with commercial conductive yarns and fabrics containing silver. There is no study on the effect of sweat on such materials, therefore we performed a preliminary study to account for moisture and potential oxydation of silver. Methods The textile sensing system is resonating RLC circuit, where the sensing part is a capacitive parallel plate strain sensor (C) located on a joint (knee). All components are textile based and contain silver. As the capacitive sensor stretches, capacitance increases and the resonance of the circuit fres decreases. This information is transmitted wirelessly via inductive coupling (Galli et al., 2023). We sprayed 1 ml of 0.1 M PBS solution on the textile capacitive sensor to simulate sweating, and applied mechanical strain before (damp state) and after air drying (dry state). The unmodified sensor (before the addition of any PBS) was also used as a baseline measure. First, we applied fixed strain (10%) with a universal testing machine; then, we tested the response of the sensorized pants when bending the knee. Results The resonance frequency of the textile sensing (RLC) circuit in the damp state was much lower than the baseline (14.85 ± 0.11 MHz vs 22.70 ± 0.12 MHz) as expected from the higher dielectric constant of water that increases the baseline capacitance of the sensor. As for the change in Δfres upon 10% strain (Δfres = fres,baseline - fres,stretch), interestingly a larger change was observed for the damp configuration as compared to the baseline and dried (1.08 ± 0.08 vs 0.79 ± 0.06 vs 0.66 ± 0.03 MHz). A similar behaviour was observed in the test with pants, where the response for flexion was Δfres = 1.58 MHz for the damp sensor and Δfres = 1.28 MHz for the dried sensor. Discussion/Conclusion This preliminary investigation showed promising results in terms of robustness of our system to artificial sweat, as there was a measurable response both in the damp and dried configurations. Further tests with different sweat amounts and rate are needed to determine the full functioning range, e.g., how much sweat is tolerated.

MoCaPose

We present MoCaPose, a novel wearable motion capturing (MoCap) approach to continuously track the wearer's upper body's dynamic poses through multi-channel capacitive sensing integrated in fashionable, loose-fitting jackets. Unlike conventional wearable IMU MoCap based on inverse dynamics, MoCaPose decouples the sensor position from the pose system. MoCaPose uses a deep regressor to continuously predict the 3D upper body joints coordinates from 16-channel textile capacitive sensors, unbound by specific applications. The concept is implemented through two prototyping iterations to first solve the technical challenges, then establish the textile integration through fashion-technology co-design towards a design-centric smart garment. A 38-hour dataset of synchronized video and capacitive data from 21 participants was recorded for validation. The motion tracking result was validated on multiple levels from statistics (R2 ~ 0.91) and motion tracking metrics (MP JPE ~ 86mm) to the usability in pose and motion recognition (0.9 F1 for 10-class classification with unsupervised class discovery). The design guidelines impose few technical constraints, allowing the wearable system to be design-centric and usecase-specific. Overall, MoCaPose demonstrates that textile-based capacitive sensing with its unique advantages, can be a promising alternative for wearable motion tracking and other relevant wearable motion recognition applications.

Customized Textile Capacitive Insole Sensor for Center of Pressure Analysis

Center of pressure refers to the centroid of the ground reaction force vector detected underneath the walking foot, which is a summary measure representing body segment movements during human locomotion. In this study, we developed a cost-effective, lightweight insole-type textile capacitive sensor (I-TCPs) to analyze plantar pressure (PP) distribution and center of pressure (COP) trajectory. To test the accuracy of I-TCPs, the measured pressure data was compared with that of F-scan. The sensor performance test was divided into a static baseline test and a dynamic gait experiment, both at two different gait speeds self-selected by the subjects. Static gait results showed that I-TCPs were capable of recognizing PP segments at different gait speeds. Dynamic gait results showed an average RMSE of 1.29 ± 0.47 mm in COPx (mediolateral shift) and 12.55 ± 5.08 mm in COPy (anteroposterior shift) at a comfortable gait speed. The COP correlation between I-TCPs and F-scan was 0.54 ± 0.09 in COPx and 0.92 ± 0.04 in COPy in comfortable gait speed conditions, in which COPy values presented a stronger correlation. RMSE and correlation in fast gait speed conditions also showed similar results. The findings of this study can be the basis for future research, including rehabilitation engineering, developing ICT devices, and creating smart wearable sensors to improve quality of life for patients and healthy individuals.

Fabrication of Silver Electrical Circuits on Textile Substrates by Reactive Inkjet Printing

Delivery of electronic functionality such as sense and response to the human body using smart textiles is important for developing wearable electronics. As the key component of connecting various devices in smart textiles, electrical circuit with excellent electrical performance is an essential effort for ongoing research in textronics. In this work, a new liquid phase chemical reduction method combined with inkjet printing technology for fabricating electrical circuits on textile substrates is adopted. The aqueous silver nitrate is ejected spot by spot onto textiles that are wetted with ascorbic acid solution and placed on the copper foil. After redox and replacement reactions, silver particles are deposited to form conductive layer for electrical circuits. Deposited layers of silver with high conductivity up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${0.86201}\times {10}^{{6}}$ </tex-math></inline-formula> S/m and the sheet resistance in the range 0.0116- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0.06913~\Omega $ </tex-math></inline-formula> /sq. Based on these findings, we realized the textile capacitive sensor with good response time, and stability for 1500 cycles of repeated loading. The fabricated textile electrocardiogram (ECG) electrode attached to braces could be effective for human heart rate and ECG signal monitoring.

Portable Respiration Monitoring System with an Embroidered Capacitive Facemask Sensor.

Respiration monitoring is a very important indicator of health status. It can be used as a marker in the recognition of a variety of diseases, such as sleep apnea, asthma or cardiac arrest. The purpose of the present study is to overcome limitations of the current state of the art in the field of respiration monitoring systems. Our goal was the development of a lightweight handheld device with portable operation and low power consumption. The proposed approach includes a textile capacitive sensor with interdigitated electrodes embroidered into the facemask, integrated with readout electronics. Readout electronics is based on the direct interface of the capacitive sensor and a microcontroller through just one analog and one digital pin. The microcontroller board and sensor are powered by a smartphone or PC through a USB cable. The developed mobile application for the Android™ operating system offers reliable data acquisition and acts as a bridge for data transfer to the remote server. The embroidered sensor was initially tested in a humidity-controlled chamber connected to a commercial impedance analyzer. Finally, in situ testing with 10 volunteering subjects confirmed stable operation with reliable respiration monitoring.

Estimation of Knee Joint Angle Using Textile Capacitive Sensor and Artificial Neural Network Implementing with Three Shoe Types at Two Gait Speeds: A Preliminary Investigation.

The lower limb joints might be affected by different shoe types and gait speeds. Monitoring joint angles might require skill and proper technique to obtain accurate data for analysis. We aimed to estimate the knee joint angle using a textile capacitive sensor and artificial neural network (ANN) implementing with three shoe types at two gait speeds. We developed a textile capacitive sensor with a simple structure design and less costly placing in insole shoes to measure the foot plantar pressure for building the deep learning models. The smartphone was used to video during walking at each condition, and Kinovea was applied to calibrate the knee joint angle. Six ANN models were created; three shoe-based ANN models, two speed-based ANN models, and one ANN model that used datasets from all experiment conditions to build a model. All ANN models at comfortable and fast gait provided a high correlation efficiency (0.75 to 0.97) with a mean relative error lower than 15% implement for three testing shoes. And compare the ANN with A convolution neural network contributes a similar result in predict the knee joint angle. A textile capacitive sensor is reliable for measuring foot plantar pressure, which could be used with the ANN algorithm to predict the knee joint angle even using high heel shoes.

A Smart Textile System to Detect Urine Leakage

In this paper a wearable system with a sensor embroidered on a textile substrate to detect urine leaks is presented. The system consists of a moisture textile capacitive sensor together with the signal conditioning and its wireless transmission to the cloud. The proposed system has been integrated on underwear and hospital sheet to detect the urine leakage on the diaper users and critical ill patient, respectively. The methodology used by the microcontroller to measure the sensor value is a charge/discharge method. The information is visualised through a computer or smartphone, where can be seen the current state of the sensor. The system has a warning set up to communicate any urine leakage. The experimental results show the functionality of the proposed system which could supply a new tool to hospitals, nursing homes or other institutions to detect when the patient diaper or sheet need to be removed. This tool can optimise the hospital protocol and improve the patient quality of life.

0438 Automatic Nighttime Agitation and Sleep Disruption Detection Using a Wearable Ankle Device and Machine Learning

Abstract Introduction Nighttime agitation behavior such as wandering and restlessness during awake and sleep in people with Alzheimer’s disease (AD) is expensive to manage and adversely affects sleep. Nighttime agitation is mostly noted by subjective caregiver reports. An automated process for this assessment would improve clinical management. Here we report on the RestEaZeTM system that uses an ankle band and machine learning to automatically classify sleep status and nighttime agitation behaviors in older adults with AD. Methods We collected data on 7 adults (mean: 81 years, SD: 10.6) with AD. They wore the RestEaZeTM ankle band with a 3-axis accelerometer, a 3-axis gyroscope, and three textile capacitive sensors. A trained Research Assistant (RA) continuously observed for wandering, restlessness, wake, and sleep between 5pm and 7am using the Cohen Mansfield Agitation Inventory (CMAI). We merged, and band-pass filtered the data and divided it into 10-second non-overlapping windows. CMAI labels and time-series features (scaled using StandardScaler) extracted from the RestEaZeTM data were used to train a Random Forest binary classifier. The significant features were extracted based on the impact on the p-value for the classifier. We used the Synthetic Minority Oversampling Technique (SMOTE) to balance the dataset and performed 5-fold cross-validation with a 67-33 train-test split. Results We report the sensitivity, specificity, accuracy, and Area-under-the Curve (AUC) for the ROC curve for the classifiers: (1) Sleep/Awake: sensitivity=0.95, specificity=0.87, accuracy=0.92, AUC=0.97; (2) Wandering/Non-Wandering: sensitivity=0.85, specificity=0.99, accuracy=0.98, AUC=0.99; and (3) Restless/Non-Restless: sensitivity=0.84, specificity=0.84, accuracy=0.84, AUC=0.92. The significant features were related to the intensity of movements. Conclusion Our preliminary results show the feasibility of using RestEaZeTM for quantitatively measuring nighttime agitation. These can provide clinically useful objective measures of agitation that can be automatically transmitted to clinical or research records with minimal staff time requirements. Support The authors acknowledge the funding support from the National Institute on Aging under award R01AG051588 and Arbor Pharmaceuticals for support for Horizant and the matching placebo.

Design and fabrication of touch-sensitive polymeric optical fibre (POF) fabric

Polymeric optical fibres (POFs) can be integrated into a textile structure for illuminative fashion and interior fabric applications. In this study, the development of a touch-sensitive POF fabric system through the integration of conductive yarns and an electronic controlling system was explored. Distinct from conventional sensor applications, the capacitive sensor used in this study was embedded into the textile structure using conductive yarns via experimentations with different weave structures. With the embedded controlling system and RGB light-emitting diodes, the POF fabrics were able to transfer sensory signals into illumination. This study combined novel weaving techniques, different materials and electronic system development to develop a multi-touch interface based on textile capacitive sensors that can sense both touch contact and non-contact/proximity. The final prototype can be washed up to 10 laundry cycles. The results demonstrated that multiple functionalities can be integrated into the structure to produce an interactive textile based product.

Event-Driven Low-Power Gesture Recognition Using Differential Capacitance

Individuals with mobility impairments represent a large portion of the population. These impairments are often the sequels of strokes, spinal cord injury, or diseases, such as amyotrophic lateral sclerosis and Guillain–Barre syndrome. Many of these diagnoses manifest in upper-extremity mobility impairment, which may prohibit or make difficult actuating devices within the home. While solutions exist for smart-home automation and control, a few are approachable by those with mobility impairments. To address this issue, we designed Inviz, a touchless low-power assistive gesture recognition system that utilizes an array of textile capacitive sensors to control a smart-home automation system. We present two novel research contributions; the use of flexible textile-based capacitive arrays as differential proximity sensors, and a low-power event-driven hierarchical signal processing algorithm for capacitor sensor arrays. A fully functional prototype system is implemented and evaluated in the context of an end-to-end home automation system using intuitive swipe and hover gestures that are comfortable and approachable for users with mobility impairments. The evaluation demonstrates that the system achieves high accuracy (>90%) while maintaining low latency ( $\mu \text{W}$ ). In addition, we evaluate the system on a subject with upper extremity mobility impairment to verify its usage as an accessibility device.

Textile Capacitive Sensor Arrays with Hidden Markove Model and Dynamic Time Warping

Textile Capacitive Sensor Arrays with Hidden Markove Model and Dynamic Time Warping

Adaptive and Personalized Gesture Recognition Using Textile Capacitive Sensor Arrays

Upper extremity mobility impairment is a common sequel of Spinal Cord Injury (SCI), brain injury, strokes, and degenerative diseases such as Guillain-Barre and ALS. Existing assistive technology solutions that provide access as user input devices are intrusive and expensive, and require physical contact that can have deleterious effects such as skin friction injury for paralyzed users who have reduced skin sensitivity. To address this problem, in this paper, we present the design, implementation, and evaluation of a non-contact proximity gesture recognition system using fabric capacitive sensor arrays. The fabric sensors are lightweight, flexible, and can be easily integrated into items of quotidian use such as clothing, bed sheets, and pillow covers. Our gesture recognition algorithm builds on two known classification techniques, Hidden Markov Model and Dynamic Time Warping to convert raw capacitance values to alphanumeric gestures. Our system is personalized to the user, allowing personalized selection of gesture sets and definition of gesture patterns in accordance with their capabilities. Our system adapts to changes in sensor configuration and orientation with minimal user training and intervention. We have evaluated our system in the context of a gesture-driven home automation system on six subjects that includes an individual who has a C6 Spinal Cord injury. We show that our system can recognize gestures of varying complexity with an average accuracy of 99 percent with minimal training.

Designing Sensitive Wearable Capacitive Sensors for Activity Recognition

We investigate the design space of flexible, textile capacitive sensors for applications in human activity recognition. In a previous paper, we showed that conductive textile patches can be used to measure capacitance of the human body and could reveal information about a broad range of activities. In this paper, we systematically investigate how different design parameters such as electrode size, electric field frequency, and the concrete analog circuit design influence sensor performance. To this end, we combine FEM electric field simulations, circuit analysis, and measurements. We illustrate the performance of sensor systems that implemented according to the design guidelines that we derived. Results from four typical activity recognition scenarios were considered, including heart rate and breathing rate monitoring, hand gesture recognition, swallowing monitoring, and gait analysis.

깔창 형태의 전기용량성 섬유압력센서를 이용한 보행 횟수 검출 및 자세 모니터링 시스템

본 논문에서는 인간의 가장 기본적이며 기초적인 운동인 걸음걸이로부터 검출할 수 있는 걸음 수 및 보행분석을 위해 전도성 섬유를 이용한 전기용량성 압력 센서를 깔창형태로 개발하였다. 개발된 깔창 형태의 센서는 보행시의 압력을 측정하여 보행신호를 검출하고, 검출된 신호를 이용하여 걸음 수 및 자세에 따른 압력 분포를 관찰하였다. 개발된 센서의 성능 검증을 위하여 국제규격의 표준분동을 사용하여 0 kg에서 100 kg 까지 10 kg씩 증가하여 무게에 따른 압력변화를 관찰하였으며, 그 결과 압력에 따라 비선형적인 특성을 가지고 캐패시턴스 값이 증가함을 보였다. 자세에 따른 압력변화 실험과 보행 횟수 검출비교를 위한 실험에서는 건강한 성인남성 다섯 명을 대상으로 4가지의 서로 다른 자세로 있을 때의 압력 변화를 관찰하였고, 보행 횟수 검출을 위해서는 시속 1 km/h와 4 km/h의 두 가지 걸음속도에서 3분 동안 걷게 하여 보행신호를 검출하였다. 상용 만보계 및 관찰자의 수계로 도출된 보수를 비교하였다. 기존의 상용 만보계는 저속(1 km/h)으로 걸었을 때 보수가 잘 측정되지 않은 반면 개발된 센서는 저속에서도 관찰자 수계대비 정확한 보수를 도출 할 수 있었다(상용 만보계 대비 평균 98.06 %의 인식률). 또한 자세에 따라 압력 값을 토대로 사용자의 자세를 모니터링 할 수 있음을 보였다. 본 연구는 향후 스마트폰과 무선 연동하는 스마트 보행관리 시스템을 개발하기 위한 기초연구이다. We have developed a textile capacitive pressure sensor for smart gait analysis. The proposed system can convert sensor signal into step counts and pressure levels by different posture. To evaluate the performance of insole type textile capacitive sensor, we measured capacitance change by increment of weights from 10 kg to 100 kg with 10 kg increment using M1 class rectangular weights (four 20 kg weights and two 10 kg weights) which have <TEX>${\pm}10%$</TEX> tolerance. The result showed non-linearity characteristic of a general capacitive pressure sensor. The test was performed according to a test protocol for four different postures (sitting, standing, standing on a left leg and standing on a right leg) and different walking speeds (1 km/h and 4 km/h). Five healthy male subjects were participated in each test. As we expected, the pressure level was changed by pressure distribution according to posture. Also, developed textile pressure sensor showed higher recognition rate (average 98.06 %) than commercial pedometer at all walking speed. Therefore, the proposed step counts and posture monitoring system using conductive textile capacitive pressure sensor proved to be a reliable and useful tool for monitoring gait parameters.