Subject-Independent, Biological Hip Moment Estimation During Multimodal Overground Ambulation Using Deep Learning

• Published in: IEEE transactions on medical robotics and bionics Vol. 4; no. 1; pp. 219 - 229
• Main Authors: Molinaro, Dean D., Kang, Inseung, Camargo, Jonathan, Gombolay, Matthew C., Young, Aaron J.
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.02.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Biological information theory; Biological system modeling; Biomechanics; Biomechanics estimation; Data models; Deep learning; Estimation; Exoskeletons; Gait; Hip; Legged locomotion; machine learning; multimodal ambulation; Neural networks; Stairs; Wearable sensors

Estimating biological joint moments using wearable sensors could enable out-of-lab biomechanical analyses and exoskeletons that assist throughout daily life. To realize these possibilities, this study introduced a subject-independent hip moment estimator using a temporal convolutional network (TCN)...