Improving Biological Joint Moment Estimation During Real-World Tasks With EMG and Instrumented Insoles.

ABSTRACT:

Objective

Real-time measurement of biological joint moment could enhance clinical assessments and generalize exoskeleton control. Accessing joint moments outside clinical and laboratory settings requires harnessing non-invasive wearable sensor data for indirect estimation. Previous approaches have been primarily validated during cyclic tasks, such as walking, but these methods are likely limited when translating to non-cyclic tasks where the mapping from kinematics to moments is not unique.

Methods

We trained deep learning models to estimate hip and knee joint moments from kinematic sensors, electromyography (EMG), and simulated pressure insoles from a dataset including 10 cyclic and 18 non-cyclic activities. We assessed estimation error on combinations of sensor modalities

SUBMITTER: Scherpereel KL 

PROVIDER: S-EPMC11364170 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature