Smart Crutches: Towards Instrumented Crutches for Rehabilitation and Exoskeletons-Assisted Walking

TY - GEN

T1 - Smart Crutches

T2 - 7th IEEE RAS/EMBS International Conference on Biomedical Robotics and Biomechatronics, BIOROB 2018

AU - Chen, Yongqi Felix

AU - Napoli, Danielle

AU - Agrawal, Sunil K.

AU - Zanotto, Damiano

N1 - Publisher Copyright: © 2018 IEEE.

PY - 2018/10/9

Y1 - 2018/10/9

N2 - Recording 3D ground reaction forces through instrumented crutches can assist patients undergoing ambulatory rehabilitation as well as help roboticists develop new assistive controllers for their exoskeletons. Current methods to measure the amount of weight a patient exerts on their limbs are either inaccurate, or not feasible outside of ideal laboratory conditions. This paper introduces Smart Crutches, an instrumented crutch system capable of measuring the weight that a patient places on his/her lower extremities and providing vibratory feedback in response to the measured weight. The device was calibrated using a motion capture system and force plates. Linear regression and support vector regression (SVR) were used for calibration, and 10-fold cross-validation was applied to estimate the system's accuracy. Results indicate that machine learning regression methods may lead to improved accuracy, but the choice of the kernel function is critical. Gaussian kernel yielded root-mean-square errors (RSME) of 2.5N or less relative to force plates, while other kernel functions produced more inconsistent and less accurate results. Instrumented crutches may be a valid alternative to force plates for estimating ground reaction forces in crutch gait.

AB - Recording 3D ground reaction forces through instrumented crutches can assist patients undergoing ambulatory rehabilitation as well as help roboticists develop new assistive controllers for their exoskeletons. Current methods to measure the amount of weight a patient exerts on their limbs are either inaccurate, or not feasible outside of ideal laboratory conditions. This paper introduces Smart Crutches, an instrumented crutch system capable of measuring the weight that a patient places on his/her lower extremities and providing vibratory feedback in response to the measured weight. The device was calibrated using a motion capture system and force plates. Linear regression and support vector regression (SVR) were used for calibration, and 10-fold cross-validation was applied to estimate the system's accuracy. Results indicate that machine learning regression methods may lead to improved accuracy, but the choice of the kernel function is critical. Gaussian kernel yielded root-mean-square errors (RSME) of 2.5N or less relative to force plates, while other kernel functions produced more inconsistent and less accurate results. Instrumented crutches may be a valid alternative to force plates for estimating ground reaction forces in crutch gait.

KW - gait rehabilitation

KW - instrumented crutch

KW - machine learning regression

KW - wearable technology

UR - https://www.scopus.com/pages/publications/85056594904

UR - https://www.scopus.com/pages/publications/85056594904#tab=citedBy

U2 - 10.1109/BIOROB.2018.8487662

DO - 10.1109/BIOROB.2018.8487662

M3 - Conference contribution

T3 - Proceedings of the IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics

SP - 193

EP - 198

BT - BIOROB 2018 - 7th IEEE International Conference on Biomedical Robotics and Biomechatronics

PB - IEEE Computer Society

Y2 - 26 August 2018 through 29 August 2018

ER -