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

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 during both activity types. RESULTS: Compared to the kinematics-only baseline, adding EMG reduced RMSE by 16.9% at the hip and 30.4% at the knee (p<0.05) and adding insoles reduced RMSE by 21.7% at the hip and 33.9% at the knee (p<0.05). Adding both modalities reduced RMSE by 32.5% at the hip and 41.2% at the knee (p<0.05) which was significantly higher than either modality individually (p<0.05). All sensor additions improved model performance on non-cyclic tasks more than cyclic tasks (p<0.05). CONCLUSION: These results demonstrate that adding kinetic sensor information through EMG or insoles improves joint moment estimation both individually and jointly. These additional modalities are most important during non-cyclic tasks, tasks that reflect the variable and sporadic nature of the real-world. SIGNIFICANCE: Improved joint moment estimation and task generalization is pivotal to developing wearable robotic systems capable of enhancing mobility in everyday life.

Estimating Knee Joint Load Using Acoustic Emissions During Ambulation.

Quantifying joint load in activities of daily life could lead to improvements in mobility for numerous people; however, current methods for assessing joint load are unsuitable for ubiquitous settings. The aim of this study is to demonstrate that joint acoustic emissions contain information to estimate this internal joint load in a potentially wearable implementation. Eleven healthy, able-bodied individuals performed ambulation tasks under varying speed, incline, and loading conditions while joint acoustic emissions and essential gait measures-electromyography, ground reaction forces, and motion capture trajectories-were collected. The gait measures were synthesized using a neuromuscular model to estimate internal joint contact force which was the target variable for subject-specific machine learning models (XGBoost) trained based on spectral, temporal, cepstral, and amplitude-based features of the joint acoustic emissions. The model using joint acoustic emissions significantly outperformed (p < 0.05) the best estimate without the sounds, the subject-specific average load (MAE = 0.31 ± 0.12 BW), for both seen (MAE = 0.08 ± 0.01 BW) and unseen (MAE = 0.21 ± 0.05 BW) conditions. This demonstrates that joint acoustic emissions contain information that correlates to internal joint contact force and that information is consistent such that unique cases can be estimated.

Low-profile elastic exosuit reduces back muscle fatigue.

We investigated the extent to which an un-motorized, low-profile, elastic exosuit reduced the rate of fatigue for six lumbar extensor muscles during leaning. Six healthy subjects participated in an A-B-A (withdrawal design) study protocol, which involved leaning at 45º for up to 90 s without exosuit assistance (A1), then with assistance (B), then again without assistance (A2). The exosuit provided approximately 12-16 Nm of lumbar extension torque. We measured lumbar muscle activity (via surface electromyography) and assessed fatigue rate via median frequency slope. We found that five of the six subjects showed consistent reductions in fatigue rate (ranging from 26% to 87%) for a subset of lumbar muscles (ranging from one to all six lumbar muscles measured). These findings objectively demonstrate the ability of a low-profile elastic exosuit to reduce back muscle fatigue during leaning, which may improve endurance for various occupations.