Evidence map›Paper›PMID 34884118›Full record

ArticleSensors (Basel, Switzerland)2021

Integrated Pedal System for Data Driven Rehabilitation.

Alessandro Schaer, Oskar Helander, Francesco Buffa, Alexis Müller, Kevin Schneider, Henrik Maurenbrecher, Barna Becsek, George Chatzipirpiridis, Olgac Ergeneman, Salvador Pané and 2 more

Open access · goldAbstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2021. 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, top 74% of its field
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, 1 citations in OpenAlex.

  1. 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

12 authors at 3 institutions in 2 countries.

Alessandro SchaerMagnes AG, Selnaustrasse 5, 8001 Zurich, Switzerland.ORCID 0000-0001-9865-9185
Oskar HelanderInstitute of Robotics and Intelligent Systems, ETH Zurich, Tannenstrasse 3, 8092 Zurich, Switzerland.ORCID 0000-0003-4914-5561
Francesco BuffaMagnes AG, Selnaustrasse 5, 8001 Zurich, Switzerland.
Alexis MüllerInstitute of Robotics and Intelligent Systems, ETH Zurich, Tannenstrasse 3, 8092 Zurich, Switzerland.
Kevin SchneiderInstitute of Robotics and Intelligent Systems, ETH Zurich, Tannenstrasse 3, 8092 Zurich, Switzerland.
Henrik MaurenbrecherInstitute of Robotics and Intelligent Systems, ETH Zurich, Tannenstrasse 3, 8092 Zurich, Switzerland.
Barna BecsekMagnes AG, Selnaustrasse 5, 8001 Zurich, Switzerland.
George ChatzipirpiridisMagnes AG, Selnaustrasse 5, 8001 Zurich, Switzerland.
Olgac ErgenemanMagnes AG, Selnaustrasse 5, 8001 Zurich, Switzerland.
Salvador PanéInstitute of Robotics and Intelligent Systems, ETH Zurich, Tannenstrasse 3, 8092 Zurich, Switzerland.
Bradley J NelsonInstitute of Robotics and Intelligent Systems, ETH Zurich, Tannenstrasse 3, 8092 Zurich, Switzerland.
Nina SchaffertInstitute für Bewegungswissenschaft, University Hamburg, Turmweg 2, 20148 Hamburg, Germany.ORCID 0000-0002-9588-5969
ETH Zurich · CHUnion Bank of Switzerland · CHUniversität Hamburg · DE

Funding

EU Horizon 2020 Eurostars E!113491
6 · The paper itself

Abstract

We present a system capable of providing visual feedback for ergometer training, allowing detailed analysis and gamification. The presented solution can easily upgrade any existing ergometer device. The system consists of a set of pedals with embedded sensors, readout electronics and wireless communication modules and a tablet device for interaction with the users, which can be mounted on any ergometer, transforming it into a full analytical assessment tool with interactive training capabilities. The methods to capture the forces and moments applied to the pedal, as well as the pedal's angular position, were validated using reference sensors and high-speed video capture systems. The mean-absolute error (MAE) for load is found to be 18.82 N, 25.35 N, 0.153 Nm for Fx, Fz and Mx respectively and the MAE for the pedal angle is 13.2°. A fully gamified experience of ergometer training has been demonstrated with the presented system to enhance the rehabilitation experience with audio visual feedback, based on measured cycling parameters.

Indexed as

FootGamificationBicyclingGravitationergometer rehabilitationgamified rehabilitationintegrated pedal systeminteractive training

Identifiers

PMID34884118
PMCPMC8662464
OpenAlexW4200440128

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.