Classifying activities of daily living based on hybrid features from vertical ground reaction force data.

K Ghousiya Begum, M Jagannath, K Adalarasu, Sannasi Ganapathy

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In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

4 authors.

K Ghousiya BegumDepartment of Electronics and Instrumentation Engineering, School of Electrical and Electronics Engineering, SASTRA Deemed to be University, Thanjavur, Tamil Nadu, India.

M JagannathSchool of Electronics Engineering, Vellore Institute of Technology (VIT), Chennai, Tamil Nadu, India.

K AdalarasuDepartment of Electronics and Instrumentation Engineering, School of Electrical and Electronics Engineering, SASTRA Deemed to be University, Thanjavur, Tamil Nadu, India. adalarasu@eie.sastra.edu.

Sannasi GanapathyDepartment of Computer Science and Engineering Education, National Institute of Technical Teachers Training & Research, Bhopal, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Leg posture plays a crucial role in performing activities of daily living (ADL) by influencing balance, mobility, and overall quality of life. The objective of this study was to classify various instrumental activities of daily living (IADL) leg postures using 1D force data using machine learning techniques. The study collected data from a SENSIX force platform, which measured the vertical ground reaction force (VGRF) and center of pressure (COP) data from 25 participants performing four static postures. These postures included double-leg standing, toe standing, single-leg standing, and squat standing. The study used five statistical and six nonlinear time series features from the VGRF data and COP to classify IADL postures. The artificial neural network performed the best, with an accuracy of approximately 98%, in organizing postures for everyday life activities.

Indexed as

Activities of Daily LivingPostureAdultBiomechanical PhenomenaClassification AlgorithmsFemaleHumansLegMachine LearningMaleNeural Networks, Computer1D force dataClassification.Human activity recognition (HAR)Instrumental activities of daily living (IADL)Machine learningPostures

Identifiers

PMID41298981

DOI10.1038/s41598-025-29328-0

PMCPMC12749533

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Textmetadata

LicenceCC BY-NC-ND

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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.