Closing the Wearable Gap–Part IX: Validation of an Improved Ankle Motion Capture Wearable

Abstract

Soft robotic sensors, a class of pliable, embeddable sensors, are well-suited for applications in wearable technology because of their ease of integration with common clothing articles. The suitability of soft robotic sensors for estimation of human joint angles has been proven; this research represents another step towards development of a reliable laboratory data collection platform for the human ankle joint complex. In this research, the accuracy and repeatability of a newly-developed wearable prototype are evaluated as a potential replacement for camera-based motion capture by measuring differences between simultaneously collected motion capture and stretch sensor data. The accuracy of these measurements is compared to measurements collected using a previous prototype for validation. Results show that the newly-developed prototype is capable of joint angle estimation within 1.86°mean-absolute-error during complex, dynamic movements and that wearing shoes over the sock prototype does not significantly degrade performance.

Closing the Wearable Gap: Foot–ankle kinematic modeling via deep learning models based on a smart sock wearable

“…In conjunction with recent advances in deep neural networks, wearable device development provides the grounds to address the current gaps in gait analysis systems. This is the next study in our Closing the Wearable Gap (CWG) research (Luczak et al, 2018;Chander et al, 2019;Saucier et al, 2019a,b;Davarzani et al, 2020;Luczak et al, 2020b;Talegaonkar et al, 2020;Carroll et al, 2021;Turner et al, 2021) with the ultimate goal of designing a wearable solution "from the ground up" (Luczak et al, 2018) capable of accurately measuring kinematic and kinetic features of the foot and ankle during gait movement. The proposed study implements a wearable prototype designed by the research team, based on soft robotic sensors (SRS) embedded into a sock to track foot-ankle movement on a treadmill at varying speeds using deep neural networks.…”

“…While several previous research exists on ideal sensor placement for assessing gait (Boerema et al, 2014 ; Engineering and Teichmann, 2016 ; Mokhlespour Esfahani and Nussbaum, 2018 ), these papers predominantly use accelerometer-based sensors, which behave differently from the stretch sensors used in this project. With the linear relationship of change in capacitance or resistance with the stretch of the sensors, these sensors have already been validated against motion capture systems to efficiently capture joint kinematics, when placed across a joint axis (Luczak et al, 2018 ; Chander et al, 2019 ; Saucier et al, 2019a , b ; Davarzani et al, 2020 ; Luczak et al, 2020b ; Talegaonkar et al, 2020 ; Carroll et al, 2021 ; Turner et al, 2021 ). Hence, the positioning of these sensors was determined based on our previous research to accurately capture joint kinematics.…”

Comparison of the Capacitance of a Cyclically Fatigued Stretch Sensor to a Non-Fatigued Stretch Sensor When Performing Static and Dynamic Foot-Ankle Motions

“…Due to the limitations of MOCAP, methods to collect movement data, such as joint angles, in a non-clinical setting are desirable. To overcome the limitations of measuring joint angles in a non-clinical setting, video or photographic-based goniometer smartphone applications have been developed and validated to measure joint angles [ 2 , 3 ], personalized medical approaches using additive manufacturing (3D printing) techniques to print sensors using existing or novel materials that can be tailored to suit the needs of a particular patient are being explored [ 4 , 5 , 6 , 7 ], and research is ongoing in the development of a smart sock that utilizes capacitive stretch sensors to measure ankle joint angles in real-time during athletic events [ 8 , 9 , 10 , 11 , 12 , 13 , 14 ].…”

“…The Athlete Engineering team at Mississippi State University is currently conducting research into the ability of the commercially available StretchSense™ StretchFABRIC sensors to capture joint angles and ROM of the foot-ankle complex during athletic practices and competitions [ 8 , 10 , 11 , 12 , 16 ]. A key requirement for any stretch sensor is the ability to produce reliable data both for their intended purpose and for the timeframe of usage [ 15 ]; however, the material properties and the electromechanical fatigue properties of the StretchFABRIC sensors remain unclear.…”

Electromechanical Fatigue Properties of Dielectric Elastomer Capacitive Sensors Based on Plantarflexion of the Human Ankle Joint

“…10 For example, a smart sock prototype that uses stretch sensors to measure the angle of the ankle joint is in development at Mississippi State University, and one of the goals for the prototype is its use to measure joint angles during athletic competitions. 22,24,25,65,66 If the sock prototype were worn over the course of a 20 game women's National Collegiate Athletic Association Division I (NCAA D1) soccer season, at least 178,200 steps would be taken exclusive of conference or national championship games, 67 which would subject the stretch sensors to recurrent HCF.…”