skip to main content


This content will become publicly available on January 1, 2024

Title: Textile Knitted Stretch Sensors for Wearable Health Monitoring: Design and Performance Evaluation
The advancement of smart textiles has led to significant interest in developing wearable textile sensors (WTS) and offering new modalities to sense vital signs and activity monitoring in daily life settings. For this, textile fabrication methods such as knitting, weaving, embroidery, and braiding offer promising pathways toward unobtrusive and seamless sensing for WTS applications. Specifically, the knitted sensor has a unique intermeshing loop structure which is currently used to monitor repetitive body movements such as breathing (microscale motion) and walking (macroscale motion). However, the practical sensing application of knit structure demands a comprehensive study of knit structures as a sensor. In this work, we present a detailed performance evaluation of six knitted sensors and sensing variation caused by design, sensor size, stretching percentages % (10, 15, 20, 25), cyclic stretching (1000), and external factors such as sweat (salt-fog test). We also present regulated respiration (inhale–exhale) testing data from 15 healthy human participants; the testing protocol includes three respiration rates; slow (10 breaths/min), normal (15 breaths/min), and fast (30 breaths/min). The test carried out with statistical analysis includes the breathing time and breathing rate variability. These testing results offer an empirically derived guideline for future WTS research, present aggregated information to understand the sensor behavior when it experiences a different range of motion, and highlight the constraints of the silver-based conductive yarn when exposed to the real environment.  more » « less
Award ID(s):
1655221
NSF-PAR ID:
10437663
Author(s) / Creator(s):
; ; ; ; ; ; ;
Date Published:
Journal Name:
Biosensors
Volume:
13
Issue:
1
ISSN:
2079-6374
Page Range / eLocation ID:
34
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. Abstract

    Soft robots adapt passively to complex environments due to their inherent compliance, allowing them to interact safely with fragile or irregular objects and traverse uneven terrain. The vast tunability and ubiquity of textiles has enabled new soft robotic capabilities, especially in the field of wearable robots, but existing textile processing techniques (e.g., cut‐and‐sew, thermal bonding) are limited in terms of rapid, additive, accessible, and waste‐free manufacturing. While 3D knitting has the potential to address these limitations, an incomplete understanding of the impact of structure and material on knit‐scale mechanical properties and macro‐scale device performance has precluded the widespread adoption of knitted robots. In this work, the roles of knit structure and yarn material properties on textile mechanics spanning three regimes–unfolding, geometric rearrangement, and yarn stretching–are elucidated and shown to be tailorable across unique knit architectures and yarn materials. Based on this understanding, 3D knit soft actuators for extension, contraction, and bending are constructed. Combining these actuation primitives enables the monolithic fabrication of entire soft grippers and robots in a single‐step additive manufacturing procedure suitable for a variety of applications. This approach represents a first step in seamlessly “printing” conformal, low‐cost, customizable textile‐based soft robots on‐demand.

     
    more » « less
  2. Abstract

    In this paper, the design and manufacturing of a highly sensitive capacitive‐based soft pressure sensor for wearable electronics applications are presented. Toward this aim, two types of soft conductive fabrics (knitted and woven), as well as two types of sacrificial particles (sugar granules and salt crystals) to create micropores within the dielectric layer of the capacitive sensor are evaluated, and the combined effects on the sensor's overall performance are assessed. It is found that a combination of the conductive knit electrode and higher dielectric porosity (generated using the larger sugar granules) yields higher sensitivity (121 × 10−4kPa−1) due to greater compressibility and the formation of air gaps between silicone elastomer and conductive knit electrode among the other design considerations in this study. As a practical demonstration, the capacitive sensor is embedded into a textile glove for grasp motion monitoring during activities of daily living.

     
    more » « less
  3. Force sensors play an important role in the biomedical devices industry, especially in motion- and pressure-related devices. Such sensors are designed to collect force or pressure data by converting it into electrical signals. The data can then be sent to and analyzed by a local or cloud-based processing unit. It is vital that the sensors can be fabricated in a way that time efficiency, cost efficiency, and quality are all maximized. The advent of three-dimensional (3D) printing has greatly facilitated prototyping and customized manufacturing, as compared to older crafting methods (such as welding and woodworking), 3D printing requires less skill and involves less costly materials making it much more time- and cost-efficient. Technological advancements have also improved the quality of the actual sensing materials used in sensor-based devices, and notably, carbon-based materials have become increasingly favored for use as sensing elements. In the presented sensor, the modern sensor fabrication methods of 3D printing and using carbon materials as sensing elements are combined. The sensor presented as a proof of the above concepts is a cantilever flex sensor. The sensor consists of a 30 mm-long cantilever extending from a 2.5 mm-thick wall, with a second wall of the same thickness parallel to the cantilever. After designing this structure and printing it using a 3D printer, the top surface of the cantilever was coated with a thin layer of conductive carbon paste and two copper wires were stripped and soldered to a pair of copper alligator clips, to be used for testing purposes. To test the sensor, the two copper wires were clipped onto the sensor (Figure 1A) and each wire was connected to a multimeter probe on the end opposite of the alligator clip. Then, using a set of four through holes in the parallel wall (along with a slotted rod), the tip of the cantilever was pressed down to an angle of 5, 10, 15, or 20 degrees (Figures 1B, 1C, 1D, and 1E, respectively) below the original plane of the cantilever and held there for 2 minutes. The resistance between the ends of the cantilever was measured throughout each trial by the multimeter, and the results (Figure 1F) for each angle were compiled and analyzed to determine the effect of each depression angle on impedance change, and thus, the overall effectiveness of the sensor. In the future, a notable improvement would be miniaturizing the sensor to facilitate in integration of the sensor in wearable and biomedical devices. 
    more » « less
  4. An ongoing component of the Baltimore urban long-term ecological research (LTER) project (Baltimore Ecosystem Study, BES) is the use of the watershed approach and monitoring of stream water quality to evaluate the integrated ecosystem functioning of Baltimore. The LTER research has focused on the Gwynns Falls watershed, which spans a gradient from highly urban, urban-residential, and suburban zones. In addition, a forested watershed serves as a reference. The long-term sampling network includes four longitudinal sampling sites along the Gwynns Falls mainstem, as well as several small (40-100 ha) watershed within or near the Gwynns Falls, providing data on water quality in different land use zones of the watersheds. Each study site is continuously monitored for discharge and is sampled weekly for water chemistry. Those data are available elsewhere on the BES website. We are interested in studying the bioreactivity of streams in our watersheds in an attempt to quantify how streams themselves may affect or be affected by water quality. To assess the bioreactivity of streams, we measure whole stream metabolism, which is an integrative metric which quantifies the production and consumption of energy by a stream ecosystem. Stream metabolism represents how energy is created (primary production) and used (respiration) within a stream; it can be thought of as a stream breathing, with primary production being similar to an inhale, and respiration as an exhale. We are monitoring stream metabolism in each of our long-term water quality monitoring stations by deploying sensors that record dissolve oxygen and temperature of the stream every five minutes, and we also have deployed light sensors to record irradiance every five minutes at long-term BES water chemistry streams, which is needed for metabolism modeling. In addition, each dissolved oxygen sensor is located near a USGS gage which estimates discharge every 15 minutes. We used USGS manual discharge estimations linked with channel geometry measurements to develop a unique discharge-stream depth relationship (contact AJ Reisinger for details). The combination of the USGS discharge data and our discharge-depth relationship allows us to estimate average daily discharge and depth. We have included these data as well as dissolved oxygen, temperature, and PAR, allowing metabolism to be scaled on an areal basis. Primary production and respiration of streams integrate all biological activity in a stream, and therefore are good metrics to assess the state of an ecosystem. These metrics can also be used to predict other ecosystem functions. This dataset includes all information needed for whole-stream metabolism modeling using the streammetabolizer R package. Data will updated as it becomes available from the core stream study sites (see http://md.water.usgs.gov/BES for a detailed description of these sites). 
    more » « less
  5. Vital signs (e.g., heart and respiratory rate) are indicative for health status assessment. Efforts have been made to extract vital signs using radio frequency (RF) techniques (e.g., Wi-Fi, FMCW, UWB), which offer a non-touch solution for continuous and ubiquitous monitoring without users’ cooperative efforts. While RF-based vital signs monitoring is user-friendly, its robustness faces two challenges. On the one hand, the RF signal is modulated by the periodic chest wall displacement due to heartbeat and breathing in a nonlinear manner. It is inherently hard to identify the fundamental heart and respiratory rates (HR and RR) in the presence of higher order harmonics of them and intermodulation between HR and RR, especially when they have overlapping frequency bands. On the other hand, the inadvertent body movements may disturb and distort the RF signal, overwhelming the vital signals, thus inhibiting the parameter estimation of the physiological movement (i.e., heartbeat and breathing). In this paper, we propose DeepVS, a deep learning approach that addresses the aforementioned challenges from the non-linearity and inadvertent movements for robust RF-based vital signs sensing in a unified manner. DeepVS combines 1D CNN and attention models to exploit local features and temporal correlations. Moreover, it leverages a two-stream scheme to integrate features from both time and frequency domains. Additionally, DeepVS unifies the estimation of HR and RR with a multi-head structure, which only adds limited extra overhead (<1%) to the existing model, compared to doubling the overhead using two separate models for HR and RR respectively. Our experiments demonstrate that DeepVS achieves 80-percentile HR/RR errors of 7.4/4.9 beat/breaths per minute (bpm) on a challenging dataset, as compared to 11.8/7.3 bpm of a non-learning solution. Besides, an ablation study has been conducted to quantify the effectiveness of DeepVS. 
    more » « less