Textile‐Based Inductive Soft Strain Sensors for Fast Frequency Movement and Their Application in Wearable Devices Measuring Multiaxial Hip Joint Angles during Running. (28th February 2020)

• Record Type: Journal Article
• Authors: Tavassolian, Mohammad; Cuthbert, Tyler J.; Napier, Christopher; Peng, JingYang; Menon, Carlo
• Is Part Of: Advanced intelligent systems. Volume 2:Number 4(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-02-28
• Subjects: inductive sensors -- kinematic tracking -- smart sensors -- soft sensors -- wearable devices; Artificial intelligence -- Periodicals; Robotics -- Periodicals; Control theory -- Periodicals; 006.3
• DOI: 10.1002/aisy.201900165
• Languages: English
• ISSNs: 2640-4567

Abstract

Abstract : Wearable multiaxes motion tracking with inductive sensors and machine learning is presented. The production, characterization, and use of a modular and size‐adjustable inductive sensor for kinematic motion tracking are introduced. The sensor is highly stable and able to track high‐frequency (>15 Hz) and high strain rates (>450% s −1 ). Four sensors are used to fabricate a pair of motion capture shorts. A random forest machine learning algorithm is used to predict the sagittal, transverse, and frontal hip joint angle, using the raw signals from sport shorts during running with a cohort of 12 participants against a gold standard optical motion capture system to an accuracy as high as R 2 = 0.98 and root mean squared error of 2° in all three planes. Herein, an alternative strain sensor is provided to those typically used (piezoresistive/capacitive) for soft wearable motion capture devices with distinct advantages that can find applications in smart wearable devices, robotics, or direct integration into textiles. Abstract : Textile‐based inductive sensors for soft wearable strain‐sensing devices are capable of tracking strain rates >450% s −1 . Four sensors are used in a wearable device to track hip joint angles across multiple axes in a 12‐participant cohort with the use of a random forest machine learning algorithm to the root mean squared error (RMSE) of less than 2°.