TY - GEN
T1 - Development of a wearable modular IMU sensor network suit with a distributed vibrotactile feedback for on-line movement guidance
AU - Nassour, John
AU - Tacca, Nicholas
AU - Erjiage, Guan
AU - Cheng, Gordon
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021/7/12
Y1 - 2021/7/12
N2 - This paper describes the fabrication of a modular wireless IMU sensor network suit with a distributed vibrotactile motor for body postural measurements with feedback. Each sensor measures the absolute orientations then transmits them into one portable hub. The module can be connected with up to four vibration motors to provide feedback to the user. Each vibration motor is hosted by a block sliding through the elastic strap used for attaching the module. The vibration motors on the strap can be positioned to different sides of the body to indicate movement directions. The suit is used to train and evaluate various static and dynamic tasks. The distribution of vibrotactile feedback targets particular features of movement in real-time such as amplitude and velocity. Preliminary experiments show the ability to identify normal walking and limping based on metrics such as the trajectory of the center of mass, the energy of left and right legs, and the calf and thigh angles to the vertical. Online identification of gait is the primary key to trigger a wearable assistive device.
AB - This paper describes the fabrication of a modular wireless IMU sensor network suit with a distributed vibrotactile motor for body postural measurements with feedback. Each sensor measures the absolute orientations then transmits them into one portable hub. The module can be connected with up to four vibration motors to provide feedback to the user. Each vibration motor is hosted by a block sliding through the elastic strap used for attaching the module. The vibration motors on the strap can be positioned to different sides of the body to indicate movement directions. The suit is used to train and evaluate various static and dynamic tasks. The distribution of vibrotactile feedback targets particular features of movement in real-time such as amplitude and velocity. Preliminary experiments show the ability to identify normal walking and limping based on metrics such as the trajectory of the center of mass, the energy of left and right legs, and the calf and thigh angles to the vertical. Online identification of gait is the primary key to trigger a wearable assistive device.
UR - http://www.scopus.com/inward/record.url?scp=85114965327&partnerID=8YFLogxK
U2 - 10.1109/AIM46487.2021.9517473
DO - 10.1109/AIM46487.2021.9517473
M3 - Conference contribution
AN - SCOPUS:85114965327
T3 - IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM
SP - 371
EP - 376
BT - 2021 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2021
Y2 - 12 July 2021 through 16 July 2021
ER -