Treffer: Feature extraction of center of pressure signals for the diagnosis of fall risk in older adults.
Title:
Feature extraction of center of pressure signals for the diagnosis of fall risk in older adults.
Authors:
Hernandez-Laredo E; 1Tianguistenco Professional Academic Unit, Autonomous University of the State of Mexico, San Pedro Tlaltizapan, Tianguistenco, State of Mexico, Mexico., Hernández-Castañeda Á; 1Tianguistenco Professional Academic Unit, Autonomous University of the State of Mexico, San Pedro Tlaltizapan, Tianguistenco, State of Mexico, Mexico.; 2Cátedras CONAHCYT, CONAHCYT, Av. Insurgentes Sur 1582, Mexico City, Mexico., García-Hernández RA; 1Tianguistenco Professional Academic Unit, Autonomous University of the State of Mexico, San Pedro Tlaltizapan, Tianguistenco, State of Mexico, Mexico., Ledeneva Y; 1Tianguistenco Professional Academic Unit, Autonomous University of the State of Mexico, San Pedro Tlaltizapan, Tianguistenco, State of Mexico, Mexico.
Source:
Acta of bioengineering and biomechanics [Acta Bioeng Biomech] 2025 Dec 11; Vol. 27 (3), pp. 77-91. Date of Electronic Publication: 2025 Dec 11 (Print Publication: 2025).
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Sciendo Country of Publication: Poland NLM ID: 101194794 Publication Model: Electronic-Print Cited Medium: Internet ISSN: 2450-6303 (Electronic) Linking ISSN: 1509409X NLM ISO Abbreviation: Acta Bioeng Biomech Subsets: MEDLINE
Imprint Name(s):
Publication: 2023- : Warsaw, Poland : Sciendo
Original Publication: Wrocław : Oficyna Wydawnicza Politechniki Wrocławskiej, 1999-
Original Publication: Wrocław : Oficyna Wydawnicza Politechniki Wrocławskiej, 1999-
MeSH Terms:
References:
B ailly A., B lanc C., F rancis É., G uillotin T., J amal F., W akim B., R oy P., Effects of dataset size and interactions on the prediction performance of logistic regression and deep learning models , Computer Methods and Programs in Bio-medicine, 2022, 213, 106504.
B anzhaf W., Artificial Intelligence: Genetic Programming , International Encyclopedia of the Social and Behavioral Sci-ences, 2001, DOI:/10.1016/B0-08-043076-7/00557-X.
B eauchet O., F antino B., A llali G., M uir S., M ontero -O dasso M., A nnweiler C., Timed Up and Go test and risk of falls in older adults: A systematic review , The Journal of Nutrition, Health and Aging, 2011, 15, 933–938.
B urks A.R., P unch W.F., Genetic programming for tubercu-losis screening from raw X-ray images . Proceedings of the Genetic and Evolutionary Computation Conference, 2018, 1214–1221.
C lark R.A., M entiplay B.F., P ua Y.-H., B ower K.J., Reli-ability and validity of the Wii Balance Board for assessment of standing balance: A systematic review , Gait and Posture, 2018, 61, 40–54.
C uaya -S imbro G., P erez -S anpablo A.-I., M orales E.-F., U riostegui I.Q., N uñez -C arrera L., Comparing machine learning methods to improve fall risk detection in elderly with osteoporosis from balance data , Journal of Healthcare Engineering, 2021, 2021.
D avut A., A lagoz B.B., A review of genetic programming: Popu-lar techniques, fundamental aspects, software tools and applications , Sakarya University Journal of Science, 2021, 25 (2), 397–416.
D enil M., T rappenberg T., Overlap versus imbalance , Ad-vances in Artificial Intelligence: 23rd Canadian Conference on Artificial Intelligence, Canadian AI 2010, 2010, 220–231.
E stévez -P edraza Á.G., H ernandez -L aredo E., M illan -G uadarrama M.E., M artínez -M éndez R., C arrillo -V ega M.F., P arra -R odríguez L., Reliability and usability analysis of an embedded system capable of evaluating balance in elderly populations based on a modified with balance board , International Journal of Environmental Research and Public Health, 2022, 19 (17), DOI: 10.3390/ijerph191711026.
E stévez -P edraza Á.G., P arra -R odríguez L., M artínez -M éndez R., P ortillo -R odríguez O., R onzón -H ernández Z., A novel model to quantify balance alterations in older adults based on the center of pressure (CoP) measurements with a cross-sectional study , PLoS One, 2021, 16 (8), e0256129.
F atima R., K han M.H., N isar M.A., D oniec R., F arid M.S., G rzegorzek M., A Systematic Evaluation of Feature Encod-ing Techniques for Gait Analysis Using Multimodal Sensory Data , Sensors, 2023, 24 (1), 75.
G haheri A., S hoar S., N aderan M., H oseini S.S., The appli-cations of genetic algorithms in medicine , Oman Medical Jour-nal, 2015, 30 (6), 406.
Google. Classification: Accuracy, recall, precision, and related metrics, 2024, https://developers.google.com/machine-learning/crash-course/classification/accuracy-precision-recall [Accessed: July 29, 2025].
H amacher D., S ingh N., V an D ieën J.H., H eller M., T aylor W.R., Kinematic measures for assessing gait stabil-ity in elderly individuals: A systematic review , Journal of The Royal Society Interface, 2011, 8 (65), 1682–1698.
H arbourne R.T., S tergiou N.. Movement variability and the use of nonlinear tools: Principles to guide physical therapist practice , Physical Therapy, 2009, 89 (3), 267–282.
Harvard Health. Balance. Harvard Health, 2023 https://www.health.harvard.edu/topics/balance#balance9 [Accessed: July 29, 2025].
H ernández -G alicia M.A., H ernandez -L aredo E., Detection of Plant-Disease Relationship Using Long Short-Term Memory Networks , XLVII Mexican Conference on Biomedical Engineer-ing: Proceedings of CNIB 2024, 2025, 185, DOI: 10.1007/978-3-031-82123-3_18.
H ernandez -L aredo E., E stévez -P edraza Á.G., S antiago -F uentes L.M., P arra -R odríguez L., Optimizing Fall Risk Diagnosis in Older Adults Using a Bayesian Classifier and Sim-ulated Annealing , Bioengineering, 2024,11(9), DOI: 10.3390/bioengineering11090908.
H ernandez -L aredo E., H ernández -G alicia M.A., Detect-ing Confusing Drug Names Based on the Phonetic Character-istics of Mel-Frequency Cepstral Coefficient and Evolutionary Computation , XLVII Mexican Conference on Biomedical En-gineering: Proceedings of CNIB 2024, 2025, 159.
H ernandez -L aredo E., P arra -R odríguez L., E stévez -P edraza Á.G., M artínez -M éndez R., A Low-Cost, IoT-Connected Force Platform for Fall Risk Assessment in Older Adults , Congreso Nacional de Ingeniería Biomédica, 2023, 374–385, DOI: 10.1007/978-3-031-46933-6_39.
K oza J.R., Genetic programming as a means for program-ming computers by natural selection , Statistics and Compu-ting, 1994, 4, 87–112.
K ozinc Ž., L öfler S., H ofer C., C arraro U., Š arabon N., Diagnostic balance tests for assessing risk of falls and distin-guishing older adult fallers and non-fallers: A systematic review with meta-analysis , Diagnostics, 2020, 10 (9), 667.
K umar A., S ingh D., S hankar Y adav R., Class overlap han-dling methods in imbalanced domain: A comprehensive survey , Multimedia Tools and Applications, 2024, 83 (23), DOI: 10.1007/s11042-023-17864-8.
L iao F.-Y., W u C.-C., W ei Y.-C., C hou L.-W., C hang K.-M., Analysis of center of pressure signals by using decision tree and empirical mode decomposition to predict falls among old-er adults , Journal of Healthcare Engineering, 2021.
P ennone J., A guero N.F., M artini D.M., M ochizuki L., do P asso S uaide A.A., Fall prediction in a quiet standing balance test via machine learning: Is it possible? , PLoS One, 2024, 19 (4), e0296355.
P erell K.L., N elson A., G oldman R.L., L uther S.L., P rieto -L ewis N., R ubenstein L.Z., Fall risk assessment measures: An analytic review , The Journals of Gerontology Series A: Biological Sciences and Medical Sciences, 2001, 56 (12), 761–766.
P opovic D., Computational Intelligence in Time Series Fore-casting. Advances in Industrial Control: Theory and Engineer-ing Applications , Springer, 2005.
P rieto T.E., M yklebust J.B., H offmann R.G., L ovett E.G., M yklebust B.M., Measures of postural steadiness: Differ-ences between healthy young and elderly adults , IEEE Trans-actions on Biomedical Engineering, 1996, 43 (9), 956–966.
Q uijoux F., N icolaï A., C hairi I., B argiotas I., R icard D., Y elnik A., O udre L., B ertin -H ugault F., V idal P.-P., V ayatis N. et al., A review of center of pressure (COP) variables to quantify standing balance in elderly people: Algorithms and open-access code , Physiological Reports, 2021, 9 (22), e15067.
Q uijoux F., V ienne -J umeau A., B ertin -H ugault F., Z awieja P., L efevre M., V idal P.-P., R icard D., Center of pressure dis-placement characteristics differentiate fall risk in older people: A systematic review with meta-analysis , Ageing Research Re-views, 2020, 62, 101117.
R eilly D., Feature selection for the classification of fall-risk in older subjects: A combinational approach using static force-plate measures , 2019, BioRxiv, 807818.
R odrigues F., D omingos C., M onteiro D., M orouço P., A review on aging, sarcopenia, falls, and resistance train-ing in community-dwelling older adults , International Journal of Environmental Research and Public Health, 2022, 19 (2), 874.
R uchinskas R., Clinical prediction of falls in the elderly , American Journal of Physical Medicine and Rehabilitation, 2003, 82 (4), 273–278.
S antos D.A., D uarte M., A public data set of human bal-ance evaluations , PeerJ, 2016, 4, e2648.
S tergiou N., D ecker L.M., Human movement variability, nonlinear dynamics, and pathology: Is there a connection? , Human Movement Science, 2011, 30 (5), 869–888.
V anneschi L., P oli R., Genetic programming – Introduction, applications, theory and open issues , Handbook of Natural Computing, 2012, 2(4), DOI: 10.1007/978-3-540-92910-9_24.
V ázquez E.V., L edeneva Y., G arcía -H ernández R.A., Combination of similarity measures based on symbolic re-gression for confusing drug names identification , Journal of Intelligent and Fuzzy Systems, 2020, 39 (2), DOI: 10.3233/JIFS-179875.
W ang C.-S., J uan C.-J., L in T.-Y., Y eh C.-C., C hiang S.-Y., Prediction model of cervical spine disease established by ge-netic programming , Proceedings of the 4th Multidisciplinary International Social Networks Conference, 2017, 1–6.
WHO. WHO global report on falls prevention in older age. World Health Organization, 2008.
W iśniowska -S zurlej A., Ć wirlej -S ozańska A., W ilmowska -P ietruszyńska A., S ozański B., The Use of Static Posturography Cut-Off Scores to Identify the Risk of Falling in Older Adults , International Journal of Environmental Research and Public Health, 2022, 19 (11), DOI: 10.3390/ijerph19116480.
B anzhaf W., Artificial Intelligence: Genetic Programming , International Encyclopedia of the Social and Behavioral Sci-ences, 2001, DOI:/10.1016/B0-08-043076-7/00557-X.
B eauchet O., F antino B., A llali G., M uir S., M ontero -O dasso M., A nnweiler C., Timed Up and Go test and risk of falls in older adults: A systematic review , The Journal of Nutrition, Health and Aging, 2011, 15, 933–938.
B urks A.R., P unch W.F., Genetic programming for tubercu-losis screening from raw X-ray images . Proceedings of the Genetic and Evolutionary Computation Conference, 2018, 1214–1221.
C lark R.A., M entiplay B.F., P ua Y.-H., B ower K.J., Reli-ability and validity of the Wii Balance Board for assessment of standing balance: A systematic review , Gait and Posture, 2018, 61, 40–54.
C uaya -S imbro G., P erez -S anpablo A.-I., M orales E.-F., U riostegui I.Q., N uñez -C arrera L., Comparing machine learning methods to improve fall risk detection in elderly with osteoporosis from balance data , Journal of Healthcare Engineering, 2021, 2021.
D avut A., A lagoz B.B., A review of genetic programming: Popu-lar techniques, fundamental aspects, software tools and applications , Sakarya University Journal of Science, 2021, 25 (2), 397–416.
D enil M., T rappenberg T., Overlap versus imbalance , Ad-vances in Artificial Intelligence: 23rd Canadian Conference on Artificial Intelligence, Canadian AI 2010, 2010, 220–231.
E stévez -P edraza Á.G., H ernandez -L aredo E., M illan -G uadarrama M.E., M artínez -M éndez R., C arrillo -V ega M.F., P arra -R odríguez L., Reliability and usability analysis of an embedded system capable of evaluating balance in elderly populations based on a modified with balance board , International Journal of Environmental Research and Public Health, 2022, 19 (17), DOI: 10.3390/ijerph191711026.
E stévez -P edraza Á.G., P arra -R odríguez L., M artínez -M éndez R., P ortillo -R odríguez O., R onzón -H ernández Z., A novel model to quantify balance alterations in older adults based on the center of pressure (CoP) measurements with a cross-sectional study , PLoS One, 2021, 16 (8), e0256129.
F atima R., K han M.H., N isar M.A., D oniec R., F arid M.S., G rzegorzek M., A Systematic Evaluation of Feature Encod-ing Techniques for Gait Analysis Using Multimodal Sensory Data , Sensors, 2023, 24 (1), 75.
G haheri A., S hoar S., N aderan M., H oseini S.S., The appli-cations of genetic algorithms in medicine , Oman Medical Jour-nal, 2015, 30 (6), 406.
Google. Classification: Accuracy, recall, precision, and related metrics, 2024, https://developers.google.com/machine-learning/crash-course/classification/accuracy-precision-recall [Accessed: July 29, 2025].
H amacher D., S ingh N., V an D ieën J.H., H eller M., T aylor W.R., Kinematic measures for assessing gait stabil-ity in elderly individuals: A systematic review , Journal of The Royal Society Interface, 2011, 8 (65), 1682–1698.
H arbourne R.T., S tergiou N.. Movement variability and the use of nonlinear tools: Principles to guide physical therapist practice , Physical Therapy, 2009, 89 (3), 267–282.
Harvard Health. Balance. Harvard Health, 2023 https://www.health.harvard.edu/topics/balance#balance9 [Accessed: July 29, 2025].
H ernández -G alicia M.A., H ernandez -L aredo E., Detection of Plant-Disease Relationship Using Long Short-Term Memory Networks , XLVII Mexican Conference on Biomedical Engineer-ing: Proceedings of CNIB 2024, 2025, 185, DOI: 10.1007/978-3-031-82123-3_18.
H ernandez -L aredo E., E stévez -P edraza Á.G., S antiago -F uentes L.M., P arra -R odríguez L., Optimizing Fall Risk Diagnosis in Older Adults Using a Bayesian Classifier and Sim-ulated Annealing , Bioengineering, 2024,11(9), DOI: 10.3390/bioengineering11090908.
H ernandez -L aredo E., H ernández -G alicia M.A., Detect-ing Confusing Drug Names Based on the Phonetic Character-istics of Mel-Frequency Cepstral Coefficient and Evolutionary Computation , XLVII Mexican Conference on Biomedical En-gineering: Proceedings of CNIB 2024, 2025, 159.
H ernandez -L aredo E., P arra -R odríguez L., E stévez -P edraza Á.G., M artínez -M éndez R., A Low-Cost, IoT-Connected Force Platform for Fall Risk Assessment in Older Adults , Congreso Nacional de Ingeniería Biomédica, 2023, 374–385, DOI: 10.1007/978-3-031-46933-6_39.
K oza J.R., Genetic programming as a means for program-ming computers by natural selection , Statistics and Compu-ting, 1994, 4, 87–112.
K ozinc Ž., L öfler S., H ofer C., C arraro U., Š arabon N., Diagnostic balance tests for assessing risk of falls and distin-guishing older adult fallers and non-fallers: A systematic review with meta-analysis , Diagnostics, 2020, 10 (9), 667.
K umar A., S ingh D., S hankar Y adav R., Class overlap han-dling methods in imbalanced domain: A comprehensive survey , Multimedia Tools and Applications, 2024, 83 (23), DOI: 10.1007/s11042-023-17864-8.
L iao F.-Y., W u C.-C., W ei Y.-C., C hou L.-W., C hang K.-M., Analysis of center of pressure signals by using decision tree and empirical mode decomposition to predict falls among old-er adults , Journal of Healthcare Engineering, 2021.
P ennone J., A guero N.F., M artini D.M., M ochizuki L., do P asso S uaide A.A., Fall prediction in a quiet standing balance test via machine learning: Is it possible? , PLoS One, 2024, 19 (4), e0296355.
P erell K.L., N elson A., G oldman R.L., L uther S.L., P rieto -L ewis N., R ubenstein L.Z., Fall risk assessment measures: An analytic review , The Journals of Gerontology Series A: Biological Sciences and Medical Sciences, 2001, 56 (12), 761–766.
P opovic D., Computational Intelligence in Time Series Fore-casting. Advances in Industrial Control: Theory and Engineer-ing Applications , Springer, 2005.
P rieto T.E., M yklebust J.B., H offmann R.G., L ovett E.G., M yklebust B.M., Measures of postural steadiness: Differ-ences between healthy young and elderly adults , IEEE Trans-actions on Biomedical Engineering, 1996, 43 (9), 956–966.
Q uijoux F., N icolaï A., C hairi I., B argiotas I., R icard D., Y elnik A., O udre L., B ertin -H ugault F., V idal P.-P., V ayatis N. et al., A review of center of pressure (COP) variables to quantify standing balance in elderly people: Algorithms and open-access code , Physiological Reports, 2021, 9 (22), e15067.
Q uijoux F., V ienne -J umeau A., B ertin -H ugault F., Z awieja P., L efevre M., V idal P.-P., R icard D., Center of pressure dis-placement characteristics differentiate fall risk in older people: A systematic review with meta-analysis , Ageing Research Re-views, 2020, 62, 101117.
R eilly D., Feature selection for the classification of fall-risk in older subjects: A combinational approach using static force-plate measures , 2019, BioRxiv, 807818.
R odrigues F., D omingos C., M onteiro D., M orouço P., A review on aging, sarcopenia, falls, and resistance train-ing in community-dwelling older adults , International Journal of Environmental Research and Public Health, 2022, 19 (2), 874.
R uchinskas R., Clinical prediction of falls in the elderly , American Journal of Physical Medicine and Rehabilitation, 2003, 82 (4), 273–278.
S antos D.A., D uarte M., A public data set of human bal-ance evaluations , PeerJ, 2016, 4, e2648.
S tergiou N., D ecker L.M., Human movement variability, nonlinear dynamics, and pathology: Is there a connection? , Human Movement Science, 2011, 30 (5), 869–888.
V anneschi L., P oli R., Genetic programming – Introduction, applications, theory and open issues , Handbook of Natural Computing, 2012, 2(4), DOI: 10.1007/978-3-540-92910-9_24.
V ázquez E.V., L edeneva Y., G arcía -H ernández R.A., Combination of similarity measures based on symbolic re-gression for confusing drug names identification , Journal of Intelligent and Fuzzy Systems, 2020, 39 (2), DOI: 10.3233/JIFS-179875.
W ang C.-S., J uan C.-J., L in T.-Y., Y eh C.-C., C hiang S.-Y., Prediction model of cervical spine disease established by ge-netic programming , Proceedings of the 4th Multidisciplinary International Social Networks Conference, 2017, 1–6.
WHO. WHO global report on falls prevention in older age. World Health Organization, 2008.
W iśniowska -S zurlej A., Ć wirlej -S ozańska A., W ilmowska -P ietruszyńska A., S ozański B., The Use of Static Posturography Cut-Off Scores to Identify the Risk of Falling in Older Adults , International Journal of Environmental Research and Public Health, 2022, 19 (11), DOI: 10.3390/ijerph19116480.
Contributed Indexing:
Keywords: Adaptive Genetic Programming; Center of Pressure (CoP); fall risk assessment; feature extraction; older adults; signal processing
Entry Date(s):
Date Created: 20251212 Date Completed: 20251212 Latest Revision: 20251212
Update Code:
20251213
DOI:
10.37190/abb/209530
PMID:
41384477
Database:
MEDLINE
Weitere Informationen
Purpose: This study aimed to develop feature extraction strategies for Center of Pressure (CoP) signals using adaptive genetic programming to characterize fall risk in older adults. Methods: The individual performance of CoP indices reported in the state-of-the-art was optimized through adaptive genetic programming across mathematical domains, such as entropy, time-based (distance, area, hybrid measures) and frequency-based ones. The validity of the new CoP indices was tested using mean difference tests for groups with and without fall risk, measuring the correlation with existing measures, as well as through the performance of univariate and multiple logistic regressions, which were reported in terms of the macro-average F 1-score, recall, accuracy, specificity, sensitivity, and Area Under the Curve (AUC). Results: The newly generated genetic CoP indices outperformed state-of-the-art indices in fall risk identification. The genetic-frequency CoP index achieved the best performance in univariate logistic regression, with an AUC of 0.763 using five-fold cross-validation. Moreover, all genetic indices showed statistically significant differences between older adults with and without fall risk. Conclusions: These results suggest that the proposed methodology provides some simple calculation formulas that facilitate its future adoption in clinical settings and increase fall risk classification performance by up to 27.0%.
(© 2025 Enrique Hernandez-Laredo et al., published by Wroclaw University of Science and Technology.)