Evidence map›Paper›PMID 40968908›Full record

ArticleSensors (Basel, Switzerland)2025

Validity of a Single Inertial Measurement Unit to Measure Hip Range of Motion During Gait in Patients Undergoing Total Hip Arthroplasty.

Noor Alalem, Xavier Gasparutto, Kevin Rose-Dulcina, Peter DiGiovanni, Didier Hannouche, Stéphane Armand

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2025. 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.

  1. Trial
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

6 authors.

Noor AlalemKinesiology Laboratory, Geneva University Hospitals and University of Geneva, 1205 Geneva, Switzerland.ORCID 0009-0003-7570-0466
Xavier GasparuttoKinesiology Laboratory, Geneva University Hospitals and University of Geneva, 1205 Geneva, Switzerland.ORCID 0000-0001-8617-6522
Kevin Rose-DulcinaKinesiology Laboratory, Geneva University Hospitals and University of Geneva, 1205 Geneva, Switzerland.ORCID 0000-0002-8698-3268
Peter DiGiovanniDivision of Orthopaedics and Traumatology, Faculty of Medicine, Geneva University Hospitals, 1205 Geneva, Switzerland.ORCID 0000-0003-0066-3253
Didier HannoucheDivision of Orthopaedics and Traumatology, Faculty of Medicine, Geneva University Hospitals, 1205 Geneva, Switzerland.ORCID 0000-0003-2060-2394
Stéphane ArmandKinesiology Laboratory, Geneva University Hospitals and University of Geneva, 1205 Geneva, Switzerland.ORCID 0000-0001-9144-4982

Funding

Division of Orthopaedic Surgery and Musculoskeletal Trauma Care of the Geneva University Hospitals GenevaESKAS Swiss ExcellenceScholarship GenevaFondation pour la recherche ostéo-articulaire of Geneva Geneva
6 · The paper itself

Abstract

Hip flexion range of motion (ROM) during gait is an important surgery outcome for patients undergoing total hip arthroplasty (THA) that could help patient monitoring and rehabilitation. To allow systematic measurements during patients' clinical pathways, hip ROM measurement should be as simple and cheap as possible to ensure patient and clinician acceptance. Single IMU options can match these requirements and offer measurements both during daily living conditions and standardized clinical tests (e.g., 10 m walk, timed up-and-go). However, single-IMU approaches to measure hip ROM have been limited. Thus, the objective of this study was to explore the accuracy of one IMU in measuring hip ROM during gait and to determine whether a single-IMU approach can provide results comparable to those of multi-IMU systems. To assess this, machine learning models were employed, ranging from the simplest (linear regression) to more complex approaches (artificial neural networks). Eighteen patients undergoing THA and seven controls were measured using a 3D opto-electronic motion capture system and one thigh-mounted IMU. Hip ROM was predicted from thigh ROM using regression and classification models and was compared to the reference hip ROM. Multiple regression was the best-performing model, with limits of agreement (LoA) of ±13° and a systematic bias of 0. Random forest, RNN, GRU and LSTM models yielded LoA ranges > 27.8°, exceeding the threshold of acceptable error. These results showed that one IMU can measure hip ROM with errors comparable to those of two-IMU methods, with potential for improvement. Using multiple linear regression was sufficient and more appropriate than employing complex ANN models. This approach offers simplicity and acceptance to users in clinical settings.

Indexed as

Arthroplasty, Replacement, HipGaitHipHip JointRange of Motion, ArticularAgedFemaleHumansMaleMiddle Agedartificial neural networkship range of motioninertial measurement unitsmachine learningtotal hip arthroplasty

Identifiers

PMID40968908
PMCPMC12157145

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Registered trials

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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.