Estimating human joint moments unifies exoskeleton control and reduces user effort

Robotic lower-limb exoskeletons can augment human mobility, but current systems require extensive, context-specific considerations, limiting their real-world viability. Here, we present a unified exoskeleton control framework that autonomously adapts assistance based on instantaneous user joint moment estimates from a temporal convolutional network (TCN). When deployed on our hip exoskeleton, the TCN achieved an average RMSE of 0.142 ± 0.021 Nm/kg and R2 of 0.840 ± 0.045 across 35 ambulatory conditions without any subject-specific calibration. Further, the unified controller significantly reduced user metabolic cost and lower-limb positive work during level ground and incline walking compared to walking without wearing the exoskeleton (P < 0.05). This advancement bridges the gap between in-lab exoskeleton technology and real-world human ambulation, making exoskeleton control technology viable for a broad community., , , # Estimating Human Joint Moments Unifies Exoskeleton Control and Reduces User Effort

by Dean D. Molinaro, Inseung Kang, and Aaron J. Young
Dataset prepared by Dean D. Molinaro (contact: [[e-mail]](mailto:[e-mail]))

This dataset consists of sensor data from a robotic hip exoskeleton and time-synced with ground-truth human lower-limb biomechanics. All data were sampled at 200 Hz.

## General Terminology, Conventions, and Units

### Ambulation Modes

LG: level ground (either overground or on a treadmill)
RA: ramp ascent (either overground or on a treadmill)
RD: ramp descent (either overground or on a treadmill)
SA: stair ascent (overground staircase)
SD: stair descent (overground staircase)
ST: standing
TR: stand-to-walk and walk-to-stand transitions in same trial (treadmill)
TRA: stand-to-walk transition (overground)
TRB: walk-to-stand transition (overground)

### Controllers

BT: gait phase-based exo control using assistance trajectory based on average biologi...