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 R2 = 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.
Highlights
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 and software and has become increasingly accurate for complex movement [28], [30],[51]
Tracking lower body movements requires sensors to track at high frequency and speeds, upwards of 5 Hz for a sprinter gait [61], [62], [64], [76], human body can move at a frequency beyond 10 Hz in certain circumstances such as seizures [65]
The random forest regressor estimated the sagittal plane angle of R2 = 0.98 ± 0.01, root mean squared error (RMSE) = 1.63 ± 0.32°, and normalized root mean squared error (NRMSE) = 3.45 ± 0.56%; the frontal plane angle of R2 = 0.93 ± 0.04, RMSE = 1.09 ± 0.22°, and NRMSE = 5.31 ± 0.96%; and the transverse plane angle with R2 = 0.80 ± 0.09, RMSE = 1.17 ± 0.25°, and NRMSE = 7.35 ± 1.20% averaged over all participants in 10-fold cross validation
Summary
Running has been one of the most popular physical activity among individuals [1], [2] This growth might explain the increased scientific interest in studying performance characteristics of recreational and master athletes [3]. Optical motion capture systems (OMCs) can monitor human body kinematics in lab environments but are not suitable for outdoors and in larger areas. This could be the potential room where wearable technologies can be developed and utilized to monitor lower body extremities in lab environments and outdoors. K, is the prediction for a molecule by the kth tree. Repeating the above steps until (a sufficiently large number) K such trees are grown
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
