TY - GEN
T1 - Wearable monitoring of joint angle and muscle activity
AU - Cotton, R. James
AU - Rogers, John
N1 - Funding Information:
This work was supported by the National Institutes of Health NIH R01 HD081525.
Funding Information:
*This work was supported by the Craig H. Neilsen foundation R. James Cotton is with Shirley Ryan AbilityLab and Northwestern University, Chicago IL, United States rcotton@ric.org John Rogers is with Northwestern University, Evanston IL, United States
Publisher Copyright:
© 2019 IEEE.
PY - 2019/6
Y1 - 2019/6
N2 - Simultaneous tracking of muscle activity and joint rotation is of significant interest in rehabilitation, but gold-standard methods with optical motion tracking and wireless electromyography recording typically restricts this to the laboratory setting. There has been significant progress using wear-able inertial measurement units (IMUs) for motion tracking, but there are no systems that can easily be deployed to home and provide simultaneous electromyography. We addressed this gap by developing a flexible, wearable, Bluetooth-connected sensor that records both IMU and EMG activity. The sensor runs an efficient quaternion-based complementary filter that estimates the sensor orientation while correcting for estimate drift and constraining magnetometer estimates to only influence heading. The difference in two sensor orientations is used to estimate the joint angle, which can be further improved with joint axis estimation. We demonstrate successful tracking of joint angle and muscle activity in a home environment with just the sensors and a smartphone.
AB - Simultaneous tracking of muscle activity and joint rotation is of significant interest in rehabilitation, but gold-standard methods with optical motion tracking and wireless electromyography recording typically restricts this to the laboratory setting. There has been significant progress using wear-able inertial measurement units (IMUs) for motion tracking, but there are no systems that can easily be deployed to home and provide simultaneous electromyography. We addressed this gap by developing a flexible, wearable, Bluetooth-connected sensor that records both IMU and EMG activity. The sensor runs an efficient quaternion-based complementary filter that estimates the sensor orientation while correcting for estimate drift and constraining magnetometer estimates to only influence heading. The difference in two sensor orientations is used to estimate the joint angle, which can be further improved with joint axis estimation. We demonstrate successful tracking of joint angle and muscle activity in a home environment with just the sensors and a smartphone.
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U2 - 10.1109/ICORR.2019.8779538
DO - 10.1109/ICORR.2019.8779538
M3 - Conference contribution
C2 - 31374639
AN - SCOPUS:85071151598
T3 - IEEE International Conference on Rehabilitation Robotics
SP - 258
EP - 263
BT - 2019 IEEE 16th International Conference on Rehabilitation Robotics, ICORR 2019
PB - IEEE Computer Society
T2 - 16th IEEE International Conference on Rehabilitation Robotics, ICORR 2019
Y2 - 24 June 2019 through 28 June 2019
ER -