{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T16:07:29Z","timestamp":1774973249079,"version":"3.50.1"},"reference-count":38,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2024,1,12]],"date-time":"2024-01-12T00:00:00Z","timestamp":1705017600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Presbyterian Health Foundation (PHF)","award":["TSET R23-03"],"award-info":[{"award-number":["TSET R23-03"]}]},{"name":"Presbyterian Health Foundation (PHF)","award":["P30CA225520"],"award-info":[{"award-number":["P30CA225520"]}]},{"name":"University of Oklahoma Health Sciences Center (OUHSC)","award":["TSET R23-03"],"award-info":[{"award-number":["TSET R23-03"]}]},{"name":"University of Oklahoma Health Sciences Center (OUHSC)","award":["P30CA225520"],"award-info":[{"award-number":["P30CA225520"]}]},{"name":"College of Allied Health New Investigator Seed Grant to Elizabeth Hile, the Oklahoma Tobacco Settlement Endowment Trust","award":["TSET R23-03"],"award-info":[{"award-number":["TSET R23-03"]}]},{"name":"College of Allied Health New Investigator Seed Grant to Elizabeth Hile, the Oklahoma Tobacco Settlement Endowment Trust","award":["P30CA225520"],"award-info":[{"award-number":["P30CA225520"]}]},{"name":"National Cancer Institute Cancer Center","award":["TSET R23-03"],"award-info":[{"award-number":["TSET R23-03"]}]},{"name":"National Cancer Institute Cancer Center","award":["P30CA225520"],"award-info":[{"award-number":["P30CA225520"]}]},{"name":"OU Department of Electrical and Computer Engineering","award":["TSET R23-03"],"award-info":[{"award-number":["TSET R23-03"]}]},{"name":"OU Department of Electrical and Computer Engineering","award":["P30CA225520"],"award-info":[{"award-number":["P30CA225520"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Pressure sensor-impregnated walkways transform a person\u2019s footfalls into spatiotemporal signals that may be sufficiently complex to inform emerging artificial intelligence (AI) applications in healthcare. Key consistencies within these plantar signals show potential to uniquely identify a person, and to distinguish groups with and without neuromotor pathology. Evidence shows that plantar pressure distributions are altered in aging and diabetic peripheral neuropathy, but less is known about pressure dynamics in chemotherapy-induced peripheral neuropathy (CIPN), a condition leading to falls in cancer survivors. Studying pressure dynamics longitudinally as people develop CIPN will require a composite model that can accurately characterize a survivor\u2019s gait consistencies before chemotherapy, even in the presence of normal step-to-step variation. In this paper, we present a state-of-the-art data-driven learning technique to identify consistencies in an individual\u2019s plantar pressure dynamics. We apply this technique to a database of steps taken by each of 16 women before they begin a new course of neurotoxic chemotherapy for breast or gynecologic cancer. After extracting gait features by decomposing spatiotemporal plantar pressure data into low-rank dynamic modes characterized by three features: frequency, a decay rate, and an initial condition, we employ a machine-learning model to identify consistencies in each survivor\u2019s walking pattern using the centroids for each feature. In this sample, our approach is at least 86% accurate for identifying the correct individual using their pressure dynamics, whether using the right or left foot, or data from trials walked at usual or fast speeds. In future work, we suggest that persistent deviation from a survivor\u2019s pre-chemotherapy step consistencies could be used to automate the identification of peripheral neuropathy and other chemotherapy side effects that impact mobility.<\/jats:p>","DOI":"10.3390\/s24020486","type":"journal-article","created":{"date-parts":[[2024,1,12]],"date-time":"2024-01-12T09:24:11Z","timestamp":1705051451000},"page":"486","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Application of Dynamic Mode Decomposition to Characterize Temporal Evolution of Plantar Pressures from Walkway Sensor Data in Women with Cancer"],"prefix":"10.3390","volume":"24","author":[{"given":"Kangjun","family":"Seo","sequence":"first","affiliation":[{"name":"School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK 73019, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hazem H.","family":"Refai","sequence":"additional","affiliation":[{"name":"School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK 73019, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5030-0779","authenticated-orcid":false,"given":"Elizabeth S.","family":"Hile","sequence":"additional","affiliation":[{"name":"Department of Rehabilitation Sciences, College of Allied Health, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73117, USA"},{"name":"OU Health Stephenson Cancer Center, Oklahoma City, OK 73104, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,1,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"246","DOI":"10.3389\/fnhum.2015.00246","article-title":"Automaticity of walking: Functional significance, mechanisms, measurement and rehabilitation strategies","volume":"9","author":"Clark","year":"2015","journal-title":"Front. Hum. Neurosci."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"374","DOI":"10.1182\/blood.2019000758","article-title":"Gait speed, grip strength, and clinical outcomes in older patients with hematologic malignancies","volume":"134","author":"Liu","year":"2019","journal-title":"Blood"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"202","DOI":"10.1007\/s12603-016-0734-x","article-title":"Slow Gait Speed Is an Independent Predictor of Early Death in Older Cancer Outpatients: Results from a Prospective Cohort Study","volume":"21","author":"Pamoukdjian","year":"2017","journal-title":"J. Nutr. Health Aging"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"715","DOI":"10.1111\/ggi.14654","article-title":"Optimal objective measurement of physical function and its predictive capacity for mortality among community-dwelling older women","volume":"23","author":"Luo","year":"2023","journal-title":"Geriatr. Gerontol. Int."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"749274","DOI":"10.3389\/frobt.2021.749274","article-title":"A Survey of Human Gait-Based Artificial Intelligence Applications","volume":"8","author":"Harris","year":"2022","journal-title":"Front. Robot AI"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2437831","DOI":"10.1155\/2022\/2437831","article-title":"Characteristics of Plantar Pressure Distribution in Diabetes with or without Diabetic Peripheral Neuropathy and Peripheral Arterial Disease","volume":"2022","author":"Cao","year":"2022","journal-title":"J. Health Eng."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1091","DOI":"10.1016\/j.apmr.2023.02.019","article-title":"Clinical Utility of Plantar Pressure Measurements as Screening in Patients With Parkinson Disease With and Without Freezing of Gait History","volume":"104","author":"Zou","year":"2023","journal-title":"Arch. Phys. Med. Rehabil."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1016\/j.jgo.2017.05.008","article-title":"Prevalence of self-reported falls, balance or walking problems in older cancer survivors from Surveillance, Epidemiology and End Results-Medicare Health Outcomes Survey","volume":"8","author":"Huang","year":"2017","journal-title":"J. Geriatr. Oncol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1007\/s10549-016-3939-0","article-title":"Long-term chemotherapy-induced peripheral neuropathy among breast cancer survivors: Prevalence, risk factors, and fall risk","volume":"159","author":"Bao","year":"2016","journal-title":"Breast Cancer Res. Treat."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2604","DOI":"10.1200\/JCO.2016.71.3552","article-title":"Falls, Functioning, and Disability Among Women with Persistent Symptoms of Chemotherapy-Induced Peripheral Neuropathy","volume":"35","author":"Horak","year":"2017","journal-title":"J. Clin. Oncol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1016\/j.gaitpost.2016.11.006","article-title":"Novel dynamic peak and distribution plantar pressure measures on diabetic patients during walking","volume":"51","author":"Khandoker","year":"2017","journal-title":"Gait Posture"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Eils, E., Nolte, S., Tewes, M., Thorwesten, L., V\u00f6lker, K., and Rosenbaum, D. (2002). Modified pressure distribution patterns in walking following reduction of plantar sensation. J. Biomech., 35.","DOI":"10.1016\/S0021-9290(02)00168-9"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Zhang, G., Wong, D.W.-C., Wong, I.K.-K., Chen, T.L.-W., Hong, T.T.-H., Peng, Y., Wang, Y., Tan, Q., and Zhang, M. (2021). Plantar Pressure Variability and Asymmetry in Elderly Performing 60-Minute Treadmill Brisk-Walking: Paving the Way towards Fatigue-Induced Instability Assessment Using Wearable In-Shoe Pressure Sensors. Sensors, 21.","DOI":"10.3390\/s21093217"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1097\/PEP.0000000000000208","article-title":"Gait Patterns in Children With Cancer and Vincristine Neuropathy","volume":"28","author":"Gilchrist","year":"2016","journal-title":"Pediatr. Phys. Ther."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1016\/j.jcrpr.2017.03.005","article-title":"Chemotherapy-induced-peripheral neuropathy, gait and fall risk in older adults following cancer treatment","volume":"4","author":"Marshall","year":"2017","journal-title":"J. Cancer Res. Pract."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"206","DOI":"10.1016\/j.gaitpost.2019.06.014","article-title":"Gait variability is altered in cancer survivors with self-reported neuropathy","volume":"72","author":"Hsieh","year":"2019","journal-title":"Gait Posture"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"960","DOI":"10.1016\/j.jgo.2019.01.010","article-title":"Using wearables to screen motor performance deterioration because of cancer and chemotherapy-induced peripheral neuropathy (CIPN) in adults - Toward an early diagnosis of CIPN","volume":"10","author":"Zahiri","year":"2019","journal-title":"J. Geriatr. Oncol."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Thaler-Kall, K., Peters, A., Thorand, B., Grill, E., Autenrieth, C.S., Horsch, A., and Meisinger, C. (2015). Description of spatio-temporal gait parameters in elderly people and their association with history of falls: Results of the population-based cross-sectional KORA-Age study. BMC Geriatr., 15.","DOI":"10.1186\/s12877-015-0032-1"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1594","DOI":"10.1007\/s00415-020-09725-3","article-title":"Gait variability as digital biomarker of disease severity in Huntington\u2019s disease","volume":"267","author":"Jensen","year":"2020","journal-title":"J. Neurol."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Pulido-Valdeolivas, I., G\u00f3mez-Andr\u00e9s, D., Mart\u00edn-Gonzalo, J.A., Rodr\u00edguez-Andonaegui, I., L\u00f3pez-L\u00f3pez, J., Pascual-Pascual, S.I., and Rausell, E. (2018). Gait phenotypes in pediatric hereditary spastic paraplegia revealed by dynamic time warping analysis and random forests. PLoS ONE, 13.","DOI":"10.1371\/journal.pone.0192345"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"315","DOI":"10.1073\/pnas.17.5.315","article-title":"Hamiltonian systems and transformation in Hilbert space","volume":"17","author":"Koopman","year":"1931","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1073\/pnas.18.3.255","article-title":"Dynamical systems of continuous spectra","volume":"18","author":"Koopman","year":"1932","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_23","unstructured":"Schmid, P.J., and Sesterhenn, J. (2008, January 23\u201325). Dynamic mode decomposition of numerical and experimental data. Proceedings of the 61st Annual Meeting of the APS Division of Fluid Dynamics, San Antonio, TX, USA."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1017\/S0022112010001217","article-title":"Dynamic mode decomposition of numerical and experimental data","volume":"656","author":"Schmid","year":"2010","journal-title":"J. Fluid Mech."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"391","DOI":"10.3934\/jcd.2014.1.391","article-title":"On dynamic mode decomposition: Theory and applications","volume":"1","author":"Tu","year":"2014","journal-title":"J. Comput. Dyn."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2096","DOI":"10.1137\/17M1125236","article-title":"Ergodic theory, dynamic mode decomposition, and computation of spectral properties of the Koopman operator","volume":"16","author":"Arbabi","year":"2017","journal-title":"SIAM J. Appl. Dyn. Syst."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Berger, E., Sastuba, M., Vogt, D., Jung, B., and Amor, H.B. (2014, January 25\u201329). Dynamic mode decomposition for perturbation estimation in human robot interaction. Proceedings of the 23rd IEEE International Symposium on Robot and Human Interactive Communication, Edinburgh, Scotland.","DOI":"10.1109\/ROMAN.2014.6926317"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.jneumeth.2015.10.010","article-title":"Extracting spatial\u2013temporal coherent patterns in large-scale neural recordings using dynamic mode decomposition","volume":"258","author":"Brunton","year":"2016","journal-title":"J. Neurosci. Methods"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"713","DOI":"10.1137\/15M1023543","article-title":"Multiresolution dynamic mode decomposition","volume":"15","author":"Kutz","year":"2016","journal-title":"SIAM J. Appl. Dyn. Syst."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1137\/15M1013857","article-title":"Dynamic mode decomposition with control","volume":"15","author":"Proctor","year":"2016","journal-title":"SIAM J. Appl. Dyn. Syst."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"723","DOI":"10.1109\/TBDATA.2020.2980849","article-title":"A Survey on the Methods and Results of Data-Driven Koopman Analysis in the Visualization of Dynamical Systems","volume":"8","author":"Parmar","year":"2022","journal-title":"IEEE Trans. Big Data"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Boudali, A.M., Sinclair, P.J., Smith, R., and Manchester, I.R. (2017, January 11\u201315). Human locomotion analysis: Identifying a dynamic mapping between upper and lower limb joints using the Koopman operator. Proceedings of the 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Jeju, Korea.","DOI":"10.1109\/EMBC.2017.8037216"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Barth, J., Oberndorfer, C., Pasluosta, C., Sch\u00fclein, S., Ga\u00dfner, H., Reinfelder, S., Kugler, P., Schuldhaus, D., Winkler, J., and Klucken, J. (2015). Stride segmentation during free walk movements using multi-dimensional subsequence dynamic time warping on inertial sensor data. Sensors, 15.","DOI":"10.3390\/s150306419"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Rampp, A., Barth, J., Sch\u00fclein, S., Ga\u00dfmann, K.G., Klucken, J., and Eskofier, B.M. (2015). Inertial sensor-based stride parameter calculation from gait sequences in geriatric patients. IEEE Trans. Biomed. Eng., 62.","DOI":"10.1109\/TBME.2014.2368211"},{"key":"ref_35","unstructured":"Levine, D., Richards, J., and Whittle, M.W. (2007). Whittle\u2019s Gait Analysis, Elsevier. [4th ed.]."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Ben-Gal, O., Benady, A., Zadik, S., Doniger, G.M., Beeri, M.S., and Plotnik, M. (2020). Using the loading response peak for defining gait cycle timing: A novel solution for the double-belt problem. J. Biomech., 110.","DOI":"10.1016\/j.jbiomech.2020.109963"},{"key":"ref_37","unstructured":"Feng, Y., Ge, Y., and Song, Q. (2011, January 6\u20138). A human identification method based on dynamic plantar pressure distribution. Proceedings of the 2011 IEEE International Conference on Information and Automation, Shenzhen, China."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1186\/s42358-018-0014-z","article-title":"Within and between-days repeatability and variability of plantar pressure measurement during walking in children, adults and older adults","volume":"58","author":"Franco","year":"2019","journal-title":"Adv. Rheumatol."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/2\/486\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T13:45:42Z","timestamp":1760103942000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/2\/486"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,12]]},"references-count":38,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2024,1]]}},"alternative-id":["s24020486"],"URL":"https:\/\/doi.org\/10.3390\/s24020486","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,12]]}}}