Application-Based Production and Testing of a Core–Sheath Fiber Strain Sensor for Wearable Electronics: Feasibility Study of Using the Sensors in Measuring Tri-Axial Trunk Motion Angles

Abstract

Wearable electronics are recognized as a vital tool for gathering in situ kinematic information of human body movements. In this paper, we describe the production of a core–sheath fiber strain sensor from readily available materials in a one-step dip-coating process, and demonstrate the development of a smart sleeveless shirt for measuring the kinematic angles of the trunk relative to the pelvis in complicated three-dimensional movements. The sensor’s piezoresistive properties and characteristics were studied with respect to the type of core material used. Sensor performance was optimized by straining above the intended working region to increase the consistency and accuracy of the piezoresistive sensor. The accuracy of the sensor when tracking random movements was tested using a rigorous 4-h random wave pattern to mimic what would be required for satisfactory use in prototype devices. By processing the raw signal with a machine learning algorithm, we were able to track a strain of random wave patterns to a normalized root mean square error of 1.6%, highlighting the consistency and reproducible behavior of the relatively simple sensor. Then, we evaluated the performance of these sensors in a prototype motion capture shirt, in a study with 12 participants performing a set of eight different types of uniaxial and multiaxial movements. A machine learning random forest regressor model estimated the trunk flexion, lateral bending, and rotation angles with errors of 4.26°, 3.53°, and 3.44° respectively. These results demonstrate the feasibility of using smart textiles for capturing complicated movements and a solution for the real-time monitoring of daily activities.

Wearable Device to Monitor Back Movements Using an Inductive Textile Sensor

“…Tormene et al concluded that their prototype was able to monitor forward, but not lateral, bending and proposed the placement of additional sensors [28]. Rezaei et al proposed a wearable garment for monitoring three-dimensional movements of trunk [18]. In addition to the higher number of resistive sensors used in that prototype, the calibration was a tedious step involving implementing a machine-learning algorithm to train a model for detecting different postures [18].…”

“…Rezaei et al proposed a wearable garment for monitoring three-dimensional movements of trunk [18]. In addition to the higher number of resistive sensors used in that prototype, the calibration was a tedious step involving implementing a machine-learning algorithm to train a model for detecting different postures [18]. The prototype presented in this paper uses a single sensor to monitor forward bending and to distinguish it from lateral bending or twisting without requiring a lengthy calibration step.…”

Textile‐Based Inductive Soft Strain Sensors for Fast Frequency Movement and Their Application in Wearable Devices Measuring Multiaxial Hip Joint Angles during Running

“…f sensor is the sensor oscillation frequency in Hz, f REFx is the reference frequency in the chip, which was set to 40 MHz, L ( t ) is inductance in Henries, and C is the sum of sensor's capacitance and parasitic capacitance. The parasitic capacitance of the coil is negligible in comparison with the selected chip capacitance—which was 330 pF and chosen to match the inductance—which was constant …”

“…To achieve a sufficient signal with good sensitivity, inductive sensors can encompass a larger area, use multiple perimeter coils, and use higher magnetic permeable materials . Textile‐based inductive sensor technology is under‐represented compared with piezoresistive and capacitive sensors and is yet to be used for multiaxial kinematic motion tracking . Previously, wearable inductive‐based motion tracking sensors have been developed by enclosing an area with conductive thread or wire and have been used to track single‐axis motion including joints such as the elbow or knee, body posture, and skin deformation .…”

“…The ability to track kinematics—complex body movements typically reserved for motion capture systems—with soft sensors has been growing with the development of both hardware (i.e., sensors, electronics, and wireless systems) and software (i.e., apps and neural networks) and has become increasingly accurate for complex movements . Tracking lower body movements requires sensors to track at high frequency and speeds, upward of 5 Hz for a sprinter's gait, although the body can move at a frequency beyond 10 Hz in certain circumstances such as seizures .…”