{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,4]],"date-time":"2026-11-04T17:58:35Z","timestamp":1793815115216,"version":"4.6.1"},"reference-count":51,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2025,9,4]],"date-time":"2025-09-04T00:00:00Z","timestamp":1756944000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["1942669"],"award-info":[{"award-number":["1942669"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["R01 AG071514"],"award-info":[{"award-number":["R01 AG071514"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Institute of Health","award":["1942669"],"award-info":[{"award-number":["1942669"]}]},{"name":"National Institute of Health","award":["R01 AG071514"],"award-info":[{"award-number":["R01 AG071514"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Accurate and scalable gait assessment is essential for clinical and research applications, including fall risk evaluation, rehabilitation monitoring, and early detection of neurodegenerative diseases. While electronic walkways remain the clinical gold standard, their high cost and limited portability restrict widespread use. Wearable inertial measurement units (IMUs) and markerless depth cameras have emerged as promising alternatives; however, prior studies have typically assessed these systems under tightly controlled conditions, with single participants in view, limited marker sets, and without direct cross-technology comparisons. This study addresses these gaps by simultaneously evaluating three sensing technologies\u2014APDM wearable IMUs (tested in two separate configurations: foot-mounted and lumbar-mounted) and the Azure Kinect depth camera\u2014against ProtoKinetics Zeno\u2122 Walkway Gait Analysis System in a realistic clinical environment where multiple individuals were present in the camera\u2019s field of view. Gait data from 20 older adults (mean age 70.06\u00b19.45 years) performing Single-Task and Dual-Task walking trials were synchronously captured using custom hardware for precise temporal alignment. Eleven gait markers spanning macro, micro-temporal, micro-spatial, and spatiotemporal domains were compared using mean absolute error (MAE), Pearson correlation (r), and Bland\u2013Altman analysis. Foot-mounted IMUs demonstrated the highest accuracy (MAE =0.00\u20136.12, r=0.92\u20131.00), followed closely by the Azure Kinect (MAE =0.01\u20136.07, r=0.68\u20130.98). Lumbar-mounted IMUs showed consistently lower agreement with the reference system. These findings provide the first comprehensive comparison of wearable and depth-sensing technologies with a clinical gold standard under real-world conditions and across an extensive set of gait markers. The results establish a foundation for deploying scalable, low-cost gait assessment systems in diverse healthcare contexts, supporting early detection, mobility monitoring, and rehabilitation outcomes across multiple patient populations.<\/jats:p>","DOI":"10.3390\/s25175501","type":"journal-article","created":{"date-parts":[[2025,9,4]],"date-time":"2025-09-04T09:18:57Z","timestamp":1756977537000},"page":"5501","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Comparison of Wearable and Depth-Sensing Technologies with Electronic Walkway for Comprehensive Gait Analysis"],"prefix":"10.3390","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-5426-8039","authenticated-orcid":false,"given":"Marjan","family":"Nassajpour","sequence":"first","affiliation":[{"name":"Department of Computer and Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL 33431, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6056-2115","authenticated-orcid":false,"given":"Mahmoud","family":"Seifallahi","sequence":"additional","affiliation":[{"name":"Department of Computer and Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL 33431, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amie","family":"Rosenfeld","sequence":"additional","affiliation":[{"name":"Comprehensive Center for Brain Health, Department of Neurology, University of Miami, Boca Raton, FL 33433, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Magdalena I.","family":"Tolea","sequence":"additional","affiliation":[{"name":"Comprehensive Center for Brain Health, Department of Neurology, University of Miami, Boca Raton, FL 33433, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5678-245X","authenticated-orcid":false,"given":"James E.","family":"Galvin","sequence":"additional","affiliation":[{"name":"Comprehensive Center for Brain Health, Department of Neurology, University of Miami, Boca Raton, FL 33433, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0075-7663","authenticated-orcid":false,"given":"Behnaz","family":"Ghoraani","sequence":"additional","affiliation":[{"name":"Department of Computer and Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL 33431, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,9,4]]},"reference":[{"key":"ref_1","first-page":"1598","article-title":"Alzheimer\u2019s disease facts and figures","volume":"19","author":"Better","year":"2023","journal-title":"Alzheimers Dement."},{"key":"ref_2","first-page":"1","article-title":"Free-living gait characteristics in ageing and Parkinson\u2019s disease: Impact of environment and ambulatory bout length","volume":"13","author":"Godfrey","year":"2016","journal-title":"J. Neuroeng. Rehabil."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2335","DOI":"10.1109\/TNSRE.2025.3578865","article-title":"The effects of real-time haptic feedback on gait and cognitive load in older adults","volume":"33","author":"Sharafian","year":"2025","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1539","DOI":"10.1016\/j.apmr.2004.12.032","article-title":"Gait analysis in a challenging environment differentiates between fallers and nonfallers among older patients with peripheral neuropathy","volume":"86","author":"Richardson","year":"2005","journal-title":"Arch. Phys. Med. Rehabil."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Hulleck, A.A., Menoth Mohan, D., Abdallah, N., El Rich, M., and Khalaf, K. (2022). Present and future of gait assessment in clinical practice: Towards the application of novel trends and technologies. Front. Med Technol., 4.","DOI":"10.3389\/fmedt.2022.901331"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1191\/026921598666182531","article-title":"Visual gait analysis: The development of a clinical assessment and scale","volume":"12","author":"Lord","year":"1998","journal-title":"Clin. Rehabil."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"63129","DOI":"10.1109\/ACCESS.2025.3558706","article-title":"Quad-tree Based Driver Classification using Deep Learning for Mild Cognitive Impairment Detection","volume":"13","author":"Ghoreishi","year":"2025","journal-title":"IEEE Access"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Kvist, A., Tinmark, F., Bezuidenhout, L., Reimeringer, M., Conradsson, D.M., and Franz\u00e9n, E. (2024). Validation of algorithms for calculating spatiotemporal gait parameters during continuous turning using lumbar and foot mounted inertial measurement units. J. Biomech., 162.","DOI":"10.1016\/j.jbiomech.2023.111907"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1016\/S0966-6362(02)00053-X","article-title":"Concurrent related validity of the GAITRite\u00ae walkway system for quantification of the spatial and temporal parameters of gait","volume":"17","author":"Bilney","year":"2003","journal-title":"Gait Posture"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1469","DOI":"10.1007\/s10586-019-03014-z","article-title":"Alzheimer\u2019s disease detection using skeleton data recorded with Kinect camera","volume":"23","author":"Seifallahi","year":"2020","journal-title":"Clust. Comput."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Nassajpour, M., Shuqair, M., Rosenfeld, A., Tolea, M.I., Galvin, J.E., and Ghoraani, B. (2024). Objective estimation of m-CTSIB balance test scores using wearable sensors and machine learning. Front. Digit. Health, 6.","DOI":"10.3389\/fdgth.2024.1366176"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Nassajpour, M., Shuqair, M., Rosenfeld, D.A., Tolea, M.I., Galvin, J.E., and Ghoraani, B. (2024, January 15\u201319). Integrating Wearable Sensor Technology and Machine Learning for Objective m-CTSIB Balance Score Estimation. Proceedings of the 2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Orlando, FL, USA.","DOI":"10.1109\/EMBC53108.2024.10781988"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Nassajpour, M., Shuqair, M., Rosenfeld, A., Tolea, M.I., Galvin, J.E., and Ghoraani, B. (2024, January 15\u201319). Smartphone-Based Balance Assessment Using Machine Learning. Proceedings of the 2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Orlando, FL, USA.","DOI":"10.1109\/EMBC53108.2024.10781862"},{"key":"ref_14","first-page":"65","article-title":"Class Imbalance-Aware Active Learning with Vision Transformers in Federated Histopathological Imaging","volume":"1","author":"Khaniki","year":"2025","journal-title":"J. Med. Med. Stud."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"130","DOI":"10.1186\/s12984-017-0341-z","article-title":"Comprehensive measurement of stroke gait characteristics with a single accelerometer in the laboratory and community: A feasibility, validity and reliability study","volume":"14","author":"Moore","year":"2017","journal-title":"J. Neuroeng. Rehabil."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Buckley, C., Mic\u00f3-Amigo, M.E., Dunne-Willows, M., Godfrey, A., Hickey, A., Lord, S., Rochester, L., Del Din, S., and Moore, S.A. (2019). Gait asymmetry post-stroke: Determining valid and reliable methods using a single accelerometer located on the trunk. Sensors, 20.","DOI":"10.3390\/s20010037"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Muthukrishnan, N., Abbas, J.J., and Krishnamurthi, N. (2020). A wearable sensor system to measure step-based gait parameters for parkinson\u2019s disease rehabilitation. Sensors, 20.","DOI":"10.3390\/s20226417"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"085005","DOI":"10.1088\/1361-6579\/aceecf","article-title":"Validity and reliability of a commercial wearable sensor system for measuring spatiotemporal gait parameters in a post-stroke population: The effects of walking speed and asymmetry","volume":"44","author":"Lanotte","year":"2023","journal-title":"Physiol. Meas."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Jacobs, D., Farid, L., Ferr\u00e9, S., Herraez, K., Gracies, J.M., and Hutin, E. (2021). Evaluation of the validity and reliability of connected insoles to measure gait parameters in healthy adults. Sensors, 21.","DOI":"10.3390\/s21196543"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.gaitpost.2021.01.013","article-title":"Agreement and consistency of five different clinical gait analysis systems in the assessment of spatiotemporal gait parameters","volume":"85","author":"Rudisch","year":"2021","journal-title":"Gait Posture"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1159\/000503282","article-title":"Multimodal wearable sensors to measure gait and voice","volume":"3","author":"Psaltos","year":"2020","journal-title":"Digit. Biomarkers"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"952","DOI":"10.1016\/j.medengphy.2016.06.015","article-title":"Concurrent validity of the Microsoft Kinect for Windows v2 for measuring spatiotemporal gait parameters","volume":"38","author":"Dolatabadi","year":"2016","journal-title":"Med. Eng. Phys."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Arizpe-G\u00f3mez, P., Harms, K., Janitzky, K., Witt, K., and Hein, A. (2024). Towards automated self-administered motor status assessment: Validation of a depth camera system for gait feature analysis. Biomed. Signal Process. Control, 87.","DOI":"10.1016\/j.bspc.2023.105352"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2722","DOI":"10.1016\/j.jbiomech.2013.08.011","article-title":"Concurrent validity of the Microsoft Kinect for assessment of spatiotemporal gait variables","volume":"46","author":"Clark","year":"2013","journal-title":"J. Biomech."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"130","DOI":"10.1016\/j.gaitpost.2022.05.021","article-title":"Comparison of Azure Kinect overground gait spatiotemporal parameters to marker based optical motion capture","volume":"96","author":"Guess","year":"2022","journal-title":"Gait Posture"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Albert, J.A., Owolabi, V., Gebel, A., Brahms, C.M., Granacher, U., and Arnrich, B. (2020). Evaluation of the pose tracking performance of the azure kinect and kinect v2 for gait analysis in comparison with a gold standard: A pilot study. Sensors, 20.","DOI":"10.3390\/s20185104"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Steinert, A., Sattler, I., Otte, K., R\u00f6hling, H., Mansow-Model, S., and M\u00fcller-Werdan, U. (2019). Using new camera-based technologies for gait analysis in older adults in comparison to the established GAITRite system. Sensors, 20.","DOI":"10.3390\/s20010125"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"423","DOI":"10.3233\/ADR-230149","article-title":"Curve walking reveals more gait impairments in older adults with mild cognitive impairment than straight walking: A Kinect camera-based study","volume":"8","author":"Seifallahi","year":"2024","journal-title":"J. Alzheimer\u2019s Dis. Rep."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"991","DOI":"10.1007\/s10772-023-10070-9","article-title":"Machine learning for the diagnosis of Parkinson\u2019s disease using speech analysis: A systematic review","volume":"26","author":"Bang","year":"2023","journal-title":"Int. J. Speech Technol."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Wang, Z., Peng, S., Zhang, H., Sun, H., and Hu, J. (2022). Gait parameters and peripheral neuropathy in patients with diabetes: A meta-analysis. Front. Endocrinol., 13.","DOI":"10.3389\/fendo.2022.891356"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"82","DOI":"10.4093\/dmj.2018.42.1.82","article-title":"Single sensor gait analysis to detect diabetic peripheral neuropathy: A proof of principle study","volume":"42","author":"Esser","year":"2018","journal-title":"Diabetes Metab. J."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Brognara, L., Arceri, A., Zironi, M., Traina, F., Faldini, C., and Mazzotti, A. (2025). Gait Spatio-Temporal Parameters Vary Significantly Between Indoor, Outdoor and Different Surfaces. Sensors, 25.","DOI":"10.3390\/s25051314"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"158760","DOI":"10.1016\/j.cej.2024.158760","article-title":"A multi-sensor coupled supramolecular elastomer empowers intelligent monitoring of human gait and arch health","volume":"504","author":"Cheng","year":"2025","journal-title":"Chem. Eng. J."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"2103694","DOI":"10.1002\/advs.202103694","article-title":"Wearable triboelectric sensors enabled gait analysis and waist motion capture for IoT-based smart healthcare applications","volume":"9","author":"Zhang","year":"2022","journal-title":"Adv. Sci."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1038\/s41528-020-00092-7","article-title":"Deep learning-enabled triboelectric smart socks for IoT-based gait analysis and VR applications","volume":"4","author":"Zhang","year":"2020","journal-title":"npj Flex. Electron."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Besser, L.M., Chrisphonte, S., Kleiman, M.J., O\u2019Shea, D., Rosenfeld, A., Tolea, M., and Galvin, J.E. (2023). The Healthy Brain Initiative (HBI): A prospective cohort study protocol. PLoS ONE, 18.","DOI":"10.1101\/2023.09.21.23295908"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Ghoraani, B., Boettcher, L.N., Hssayeni, M.D., Rosenfeld, A., Tolea, M.I., and Galvin, J.E. (2021). Detection of mild cognitive impairment and Alzheimer\u2019s disease using dual-task gait assessments and machine learning. Biomed. Signal Process. Control, 64.","DOI":"10.1016\/j.bspc.2020.102249"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1016\/j.gaitpost.2024.10.019","article-title":"Concurrent validity of Protokinetics movement analysis software for estimated centre of mass displacement and velocity during walking","volume":"115","author":"Greenfield","year":"2025","journal-title":"Gait Posture"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Romeo, L., Marani, R., Malosio, M., Perri, A.G., and D\u2019Orazio, T. (2021, January 22\u201325). Performance analysis of body tracking with the microsoft azure kinect. Proceedings of the 2021 29th Mediterranean Conference on Control and Automation (MED), Puglia, Italy.","DOI":"10.1109\/MED51440.2021.9480177"},{"key":"ref_40","unstructured":"Fraccaro, P., Coyle, L., Doyle, J., and O\u2019Sullivan, D. (2014, January 24\u201327). Real-world gyroscope-based gait event detection and gait feature extraction. Proceedings of the Sixth International Conference on eHealth, Telemedicine, and Social Medicine, Barcelona, Spain."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Pham, M.H., Elshehabi, M., Haertner, L., Del Din, S., Srulijes, K., Heger, T., Synofzik, M., Hobert, M.A., Faber, G.S., and Hansen, C. (2017). Validation of a step detection algorithm during straight walking and turning in patients with Parkinson\u2019s disease and older adults using an inertial measurement unit at the lower back. Front. Neurol., 8.","DOI":"10.3389\/fneur.2017.00457"},{"key":"ref_42","first-page":"838","article-title":"Validation of an accelerometer to quantify a comprehensive battery of gait characteristics in healthy older adults and Parkinson\u2019s disease: Toward clinical and at home use","volume":"20","author":"Godfrey","year":"2015","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Seifallahi, M., Galvin, J.E., and Ghoraani, B. (2024). Detection of mild cognitive impairment using various types of gait tests and machine learning. Front. Neurol., 15.","DOI":"10.3389\/fneur.2024.1354092"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Ma, M., Proffitt, R., and Skubic, M. (2018). Validation of a Kinect V2 based rehabilitation game. PLoS ONE, 13.","DOI":"10.1371\/journal.pone.0202338"},{"key":"ref_45","unstructured":"Weinberg, H. (2002). Using the ADXL202 in Pedometer and Personal Navigation Applications, Analog Devices, Inc.. Analog Devices AN-602 Application Note; Technical Report."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"486","DOI":"10.1109\/TBME.2004.840727","article-title":"Assessment of walking features from foot inertial sensing","volume":"52","author":"Sabatini","year":"2005","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Ceyhan, B., LaMar, J., Nategh, P., Neghabi, M., Konjalwar, S., Rodriguez, P., Hahn, M.K., Blakely, R.D., and Ranji, M. (2023, January 24\u201327). Optical imaging reveals liver metabolic perturbations in Mblac1 knockout mice. Proceedings of the 2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Sydney, Australia.","DOI":"10.1109\/EMBC40787.2023.10341032"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1544","DOI":"10.1002\/mds.25684","article-title":"Objective biomarkers of balance and gait for Parkinson\u2019s disease using body-worn sensors","volume":"28","author":"Horak","year":"2013","journal-title":"Mov. Disord."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.exger.2018.11.001","article-title":"Reliability, validity and minimal detectable change of 2-minute walk test, 6-minute walk test and 10-meter walk test in frail older adults with dementia","volume":"115","author":"Chan","year":"2019","journal-title":"Exp. Gerontol."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Cheng, F.Y., Chang, Y., Cheng, S.J., Shaw, J.S., Lee, C.Y., and Chen, P.H. (2021). Do cognitive performance and physical function differ between individuals with motoric cognitive risk syndrome and those with mild cognitive impairment?. BMC Geriatr., 21.","DOI":"10.1186\/s12877-020-01992-z"},{"key":"ref_51","first-page":"e12281","article-title":"Endurance and gait speed relationships with mild cognitive impairment and dementia","volume":"14","author":"Windham","year":"2022","journal-title":"Alzheimer\u2019S Dement. Diagn. Assess. Dis. Monit."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/25\/17\/5501\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:39:15Z","timestamp":1760035155000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/25\/17\/5501"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,4]]},"references-count":51,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2025,9]]}},"alternative-id":["s25175501"],"URL":"https:\/\/doi.org\/10.3390\/s25175501","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,4]]}}}