Evaluating the Performance of Joint Angle Estimation Algorithms on an Exoskeleton Mock-Up via a Modular Testing Approach.

Ryan S Pollard, Sarah M Bass, Mark C Schall, Michael E Zabala

Abstract read

In one paragraph

Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

Comparative Analysis of Robotic Assistive Devices on Paretic Knee Motion in Post-Stroke Patients: An IMU-Based Pilot Study.Journal of functional morphology and kinesiology · 2025
Article

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Ryan S PollardDepartment of Mechanical Engineering, Auburn University, Auburn, AL 36849, USA.ORCID 0009-0005-0423-9443

Sarah M BassDepartment of Mechanical Engineering, Auburn University, Auburn, AL 36849, USA.

Mark C SchallDepartment of Industrial and Systems Engineering, Auburn University, Auburn, AL 36849, USA.ORCID 0000-0002-2516-0292

Michael E ZabalaDepartment of Mechanical Engineering, Auburn University, Auburn, AL 36849, USA.

Funding

U.S. Army Combat Capabilities Development Command (DEVCOM) W15QKN-17-9-1025-RPP-10 MOB 17-08

6 · The paper itself

Abstract

A common challenge for exoskeleton control is discerning operator intent to provide seamless actuation of the device with the operator. One way to accomplish this is with joint angle estimation algorithms and multiple sensors on the human-machine system. However, the question remains of what can be accomplished with just one sensor. The objective of this study was to deploy a modular testing approach to test the performance of two joint angle estimation models-a kinematic extrapolation algorithm and a Random Forest machine learning algorithm-when each was informed solely with kinematic gait data from a single potentiometer on an ankle exoskeleton mock-up. This study demonstrates (i) the feasibility of implementing a modular approach to exoskeleton mock-up evaluation to promote continuity between testing configurations and (ii) that a Random Forest algorithm yielded lower realized errors of estimated joint angles and a decreased actuation time than the kinematic model when deployed on the physical device.

Indexed as

AlgorithmsExoskeleton DeviceAnkle JointBiomechanical PhenomenaGaitHumansJointsMachine Learningestimation algorithmsexoskeleton mock-upjoint angleskinematicsrandom forestsingle sensor

Identifiers

PMID39275584

DOI10.3390/s24175673

PMCPMC11397979

What OpenQuestion holds

Textmetadata

LicenceCC BY

Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.