DOI: 10.5061/dryad.z34tmpgks
title: A versatile knee exoskeleton mitigates quadriceps fatigue in lifting, lowering, and carrying tasks

description: The quadriceps are particularly susceptible to fatigue during repetitive
 lifting-lowering and carrying (LLC), affecting worker performance,
 posture, and ultimately lower-back injury risk. Although robotic
 exoskeletons have been developed and optimized for specific use cases like
 lifting-lowering, their controllers lack the versatility or
 customizability to target critical muscles across many fatiguing tasks.
 Here we present a task-adaptive knee exoskeleton controller that
 automatically modulates virtual springs, dampers, and gravity and inertia
 compensation to assist squatting, level walking, and ramp and stairs
 ascent/descent. Unlike end-to-end neural networks, the controller is
 composed of predictable, bounded components with interpretable parameters
 that are amenable to both data-driven optimization for biomimetic
 assistance and subsequent application-specific tuning, for example,
 maximizing quadriceps assistance over multi-terrain LLC. When deployed on
 a backdrivable knee exoskeleton, the assistance torques holistically
 reduced quadriceps effort across multi-terrain LLC tasks (significantly,
 except for level walking) in 10 human users without user-specific
 calibration. The exoskeleton also significantly improved fatigue-induced
 deficits in time-based performance and posture during repetitive
 lifting-lowering. Finally, the system facilitated seamless task
 transitions and garnered high effectiveness ratings post-fatigue over a
 multi-terrain circuit. These findings indicate this versatile control
 framework can target critical muscles across multiple tasks, specifically
 mitigating quadriceps fatigue and its deleterious effects.
description: Data collected from 10 able-bodied participants performing
 non-fatiguing (S1) and fatiguing (S2) lifting-lowering-carrying (LLC)
 tasks with and without a bilateral knee exoskeleton. dataset_S1 consists
 of electromyography, kinematics, torque, and footsensor data from
 non-fatiguing LLC tasks. dataset_S2 consists time, posture, and perceptual
 measurements from fatiguing squat lifting-lowering and carying
 tasks.
description: Matlab or Octave (Open Source)
description: # A Versatile Knee Exoskeleton Mitigates Quadriceps Fatigue in Lifting,
 Lowering, and Carrying Tasks --- Data collected from 10 able-bodied
 participants performing fatiguing (S1) and non-fatiguing (S2)
 lifting-lowering-carrying (LLC) tasks with (exo condition) and without
 (bare condition) a bilateral knee exoskeleton. LLC tasks consist of squat
 lifting-lowering (LL), ramp ascent (RA), ramp descent (RD), stairs ascent
 (SA), stairs descent (SD), and level walking (LW). Ramp incline is 15
 degrees and step height of stairs is 7 inches. dataset_S1 consists
 performance and posture measurements from fatiguing squat LL, and
 perceptual measurements from fatigued LLC tasks. dataset_S2 consists of
 electromyography, kinematics, torque, and foot sensor data from
 non-fatiguing LLC tasks. ## Description of the Data and file structure
 dataset_S1.mat contains 2 structures: 1) *emg* 2) *exo*. 1. *emg* is
 organized as emg.muscle.condition.task.measure, where the sub-structures
 are as follows. muscle: *quads, VMO, VL, RF, hams, ST, BF*; condition:
 *bare, exo*; task: *LL, LW, SA, SD, RA, RD*; measure: *ensemble, means*.
 ensemble is a 101 (0% to 100% task cycle) by 10 (subjects) array
 containing the time-normalized ensemble averaged emg profiles (normalized
 to %MVC). means is a 1 by 10 (subjects) array containing the means (in
 %MVC) of the emg profiles, i.e., the across cycle mean of ensemble. Note:
 quads contains the weighted (per physiological cross-sectional area)
 average emg data of vastus medialis oblique (VMO), rectus femoris (RF),
 and vastus lateralis (VL); and similarly hams contains un-weighted avearge
 emg data of biceps femoris (BF) and semitendinosus (ST). 2. *exo* is
 organized as exo.measure.task, where the sub-structures are as follows.
 measure: *torque, thighAngle, shankAngle, kneeAngle, grf;* task: *LL, LW,
 SA, SD, RA, RD*. Each task field is a 101 (0% to 100% task cycle) by 10
 (subjects) array containing the corresponding ensemble averaged measures.
 Torque is in Nm (positive for extension); thighAngle is in degrees
 (positive for thigh anterior to vertical); shankAngle is in degrees
 (positive for shank anterior to vertical); kneeAngle is in degrees
 (positive for flexion); grf is the ground reaction force as measured by
 the foot sensor and is normalized to bodyweight. dataset_S2.mat contains
 11 structures: 1. *LLrepDuration_progression* contains two fields: *bare*
 and *exo*; each field contains 21 (-100% to 100% every 10%) by 10
 (subjects) array containing the durations (in seconds) of the squat LL
 repetitions across % trial progression. The subjects declared fatigue at
 0% trial progression. While a variable number of LL repetitions were
 performed pre-fatigue (-100% to 0% trial progression), a fixed set of 10
 LL repetitions were performed post-fatigue (0% to 100% trial progression).
 Note that subjects were not allowed to pause in the pre-fatigue phase, but
 during the post-fatigue phase they could take the bare minimum pause
 between subsequent repetitions in order to maintain good squat posture. 2.
 *fatiguedCompletionTime_deficit* contains two fields: *bare* and *exo*;
 each field contains 1 by 10 (subjects) array containing the % increase in
 time to complete the 10 post-fatigue LL repetitions with respect to time
 required to complete the first 10 pre-fatigue LL repetitions in the bare
 condition. 3. *peakLean_progression* contains two fields: *bare* and
 *exo*; each field contains 21 (-100% to 100% every 10%) by 10 (subjects)
 array containing the peak sagittal thorax angles (in degrees, positive for
 trunk flexion) of the squat LL repetitions across % trial progression. 4.
 *fatiguedPeakLean* contains two fields: *bare* and *exo*; each field
 contains 1 by 10 (subjects) array containing the average peak sagittal
 thorax angle of the 10 post-fatigue LL repetitions. 5.
 *peakLean_deviation_progression* contains two fields: *bare* and *exo*;
 each field contains 21 (-100% to 100% every 10%) by 10 (subjects) array
 containing the deviation in peak sagittal thorax angles (in degrees) of
 the squat LL repetitions across % trial progression. Deviation is with
 respect to the minimum peak sagittal thorax angle observed in the
 pre-fatigue bare repetitions. 6. *fatiguedPeakLean_deviation* contains two
 fields: *bare* and *exo*; each field contains 1 by 10 (subjects) array
 containing the average deviation in peak sagittal thorax angle of the 10
 post-fatigue LL repetitions. 7. *peakKneeFlexion_progression* contains two
 fields: *bare* and *exo*; each field contains 21 (-100% to 100% every 10%)
 by 10 (subjects) array containing the peak sagittal knee angles (in
 degrees, positive for flexion) of the squat LL repetitions across % trial
 progression. 8. *fatiguedPeakKneeFlexion* contains two fields: *bare* and
 *exo*; each field contains 1 by 10 (subjects) array containing the average
 peak sagittal knee angle of the 10 post-fatigue LL repetitions. 9.
 *peakKneeFlexion_deviation_progression* contains two fields: *bare* and
 *exo*; each field contains 21 (-100% to 100% every 10%) by 10 (subjects)
 array containing the deviation in peak sagittal knee angles (in degrees)
 of the squat LL repetitions across % trial progression. Deviation is with
 respect to the maximum peak sagittal knee angle observed in the
 pre-fatigue repetitions of the respective conditions. 10.
 *fatiguedPeakKneeFlexion_deviation* contains two fields: *bare* and *exo*;
 each field contains 1 by 10 (subjects) array containing the average
 deviation in peak sagittal knee angle of the 10 post-fatigue LL
 repetitions. 11. *modifiedquest* contains two fields: data and headers.
 data is a 6 (tasks) by 10 (subjects) array containing the modifiedQUEST
 ratings (out of 5) of the 6 tasks named in headers - a 6 (tasks) by 1
 string array. ## Sharing/access Information There are no other publicly
 accessible locations of the data. Data was not derived from any other
 source.