Real-Time Prediction of Lower-Limb Joint Kinematics, Kinetics, and Ground Reaction Force Using Wearable Sensors and Machine Learning.

Josée Mallah, Yu Zhu, Kailang Xu, Gurvinder S Virk, Shaoping Bai, Luigi G Occhipinti

Abstract read

In one paragraph

Article in Sensors (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Josée MallahElectrical Engineering Division, Department of Engineering, University of Cambridge, Cambridge CB3 0FA, UK.ORCID 0009-0001-3814-825X

Yu ZhuElectrical Engineering Division, Department of Engineering, University of Cambridge, Cambridge CB3 0FA, UK.

Kailang XuElectrical Engineering Division, Department of Engineering, University of Cambridge, Cambridge CB3 0FA, UK.ORCID 0009-0002-6427-5049

Gurvinder S VirkEndoenergy Systems Ltd., Cambridge CB25 0HG, UK.

Shaoping BaiDepartment of Materials and Production, Aalborg University, 9220 Aalborg, Denmark.ORCID 0000-0001-5882-9768

Luigi G OcchipintiElectrical Engineering Division, Department of Engineering, University of Cambridge, Cambridge CB3 0FA, UK.ORCID 0000-0002-9067-2534

Funding

Cambridge Commonwealth European and International TrustMathWorks (United Kingdom)

6 · The paper itself

Abstract

Walking is a key movement of interest in biomechanics, yet gold-standard data collection methods are time- and cost-expensive. This paper presents a real-time, multimodal, high sample rate lower-limb motion capture framework, based on wireless wearable sensors and machine learning algorithms. Random Forests are used to estimate joint angles from IMU data, and ground reaction force (GRF) is predicted from an instrumented insole, while joint moments are predicted from angles and GRF using deep learning based on the ResNet-16 architecture. All three models achieve good accuracy compared to the literature, and the predictions are logged at 1 kHz with a minimal delay of 23 ms for 20s worth of input data.

Indexed as

JointsLower ExtremityMachine LearningWearable Electronic DevicesAlgorithmsBiomechanical PhenomenaHumansKineticsMotion CapturePrediction AlgorithmsPredictive Learning ModelsRandom ForestWalkingbiomechanicsmachine learningreal-time systemswearable sensors

Identifiers

PMID42817461

DOI10.3390/s26185885

PMCPMC13611085

What OpenQuestion holds

Textmetadata

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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.