Closing the Wearable Gap—Part IV: 3D Motion Capture Cameras Versus Soft Robotic Sensors Comparison of Gait Movement Assessment

Abstract

The purpose of this study was to use 3D motion capture and stretchable soft robotic sensors (SRS) to collect foot-ankle movement on participants performing walking gait cycles on flat and sloped surfaces. The primary aim was to assess differences between 3D motion capture and a new SRS-based wearable solution. Given the complex nature of using a linear solution to accurately quantify the movement of triaxial joints during a dynamic gait movement, 20 participants performing multiple walking trials were measured. The participant gait data was then upscaled (for the SRS), time-aligned (based on right heel strikes), and smoothed using filtering methods. A multivariate linear model was developed to assess goodness-of-fit based on mean absolute error (MAE; 1.54), root mean square error (RMSE; 1.96), and absolute R 2 (R 2 ; 0.854). Two and three SRS combinations were evaluated to determine if similar fit scores could be achieved using fewer sensors. Inversion (based on MAE and RMSE) and plantar flexion (based on R 2 ) sensor removal provided second-best fit scores. Given that the scores indicate a high level of fit, with further development, an SRS-based wearable solution has the potential to measure motion during gait-based tasks with the accuracy of a 3D motion capture system.

Closing the Wearable Gap—Part V: Development of a Pressure-Sensitive Sock Utilizing Soft Sensors

“…Each data analysis was performed on a per-sensor basis similar to previous studies [3][4][5]. In this approach, correlations were generated for each foot %∆ C-SRSs and corresponding %∆ BodiTrak TM Vector Plate pressure cells, and %∆ GRFs during each type of movement (i.e., left lateral heel C-SRSs compared to D11 BodiTrak TM Vector Plate cell, left medial heel C-SRSs compared to D12 BodiTrak TM Vector Plate cell, left first metatarsal C-SRSs compared to I12 BodiTrak Vector Plate, left mid-metatarsal C-SRSs compared to I11 BodiTrak TM Vector Plate, and fifth metatarsal C-SRSs modeled to H10 BodiTrak TM Vector Plate).…”

“…In Parts II, III, and IV of "Closing the Wearable Gap" paper series [3][4][5], the use of stretchable SRS and optical motion capture were used to assess foot-ankle kinematics. Part II captured active range of motion of the four primary foot-ankle joint movements-PF, DF, INV, and EVR-while participants were seated [3].…”

“…Part III captured PF and DF during dynamic expected and unexpected slip and trip perturbations [4]. Part IV captured all four foot-ankle joint ranges of motion during multiple gait cycles on flat and tilted surfaces [5]. Given that the primary goal of this continuing research effort is to design a wearable solution that accurately captures data collected at the foot-ankle complex in a non-laboratory environment, developing the GRPS is a continuation of this paper series.…”

“…This paper, Part V, assesses the use of compressible StretchSense™ sensors to capture foot ground reaction pressures during closed kinetic chain movements in squats, shifting center-of-pressure right-to-left, and toe-to-heel (anterior to posterior). Therefore, there will be shared similarities in layout, wording, and the goal of developing wearable technology that can measure movement and pressure "from the ground up" originating from the previous papers [3][4][5].…”

“…The goal of this study was to develop a wearable solution using compressible soft robotic sensors (C-SRS, Stretchsense™ sensors, Auckland, New Zealand) to accurately collect data "from the ground up" as defined by the needs of the Strength and Conditioning Coach (S&CC) and Athlete Training (AT) practitioners who commonly rely upon wearable solutions in sports [1] for decisions about health and safety [2]. The aim of this research series was to extend the "Closing the Wearable Gap" paper series where the previous studies used stretchable soft robotic sensors or stretchable SRS to capture both static movements [3] and dynamic movements [4] of the foot-ankle complex as well as to fully quantify gait cycles [5] all through the validation of goodness-of-fit as compared to 3D motion capture. To this point in the paper series, only foot-ankle joint angles from common movements, such as plantar the driver's electrooculography (EOG) signal 0.5 s ahead of time, notifying the driver of the potential dozing off while driving.…”