A versatile knee exoskeleton mitigates quadriceps fatigue in lifting, lowering, and carrying tasks
Data files
Sep 04, 2024 version files 912.35 KB
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dataset_S1.mat
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dataset_S2.mat
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README.md
Abstract
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.
README: 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.
- 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).
- 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:
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
Methods
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.
Usage notes
Matlab or Octave (Open Source)