{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T15:45:24Z","timestamp":1780501524344,"version":"3.54.1"},"reference-count":48,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2021,12,11]],"date-time":"2021-12-11T00:00:00Z","timestamp":1639180800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000266","name":"Engineering and Physical Sciences Research Council","doi-asserted-by":"publisher","award":["EP\/ R005273\/1"],"award-info":[{"award-number":["EP\/ R005273\/1"]}],"id":[{"id":"10.13039\/501100000266","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100004440","name":"Wellcome Trust","doi-asserted-by":"publisher","award":["204813\/Z\/16\/Z"],"award-info":[{"award-number":["204813\/Z\/16\/Z"]}],"id":[{"id":"10.13039\/100004440","id-type":"DOI","asserted-by":"publisher"}]},{"name":"The Cure Parkinson's Trust","award":["AW021"],"award-info":[{"award-number":["AW021"]}]},{"DOI":"10.13039\/501100015725","name":"IXICO","doi-asserted-by":"publisher","award":["R101507-101"],"award-info":[{"award-number":["R101507-101"]}],"id":[{"id":"10.13039\/501100015725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The use of wearable sensors allows continuous recordings of physical activity from participants in free-living or at-home clinical studies. The large amount of data collected demands automatic analysis pipelines to extract gait parameters that can be used as clinical endpoints. We introduce a deep learning-based automatic pipeline for wearables that processes tri-axial accelerometry data and extracts gait events\u2014bout segmentation, initial contact (IC), and final contact (FC)\u2014from a single sensor located at either the lower back (near L5), shin or wrist. The gait events detected are posteriorly used for gait parameter estimation, such as step time, length, and symmetry. We report results from a leave-one-subject-out (LOSO) validation on a pilot study dataset of five participants clinically diagnosed with Parkinson\u2019s disease (PD) and six healthy controls (HC). Participants wore sensors at three body locations and walked on a pressure-sensing walkway to obtain reference gait data. Mean absolute errors (MAE) for the IC events ranged from 22.82 to 33.09 milliseconds (msecs) for the lower back sensor while for the shin and wrist sensors, MAE ranges were 28.56\u201364.66 and 40.19\u201372.50 msecs, respectively. For the FC-event detection, MAE ranges were 29.06\u201348.42, 40.19\u201372.70 and 36.06\u201360.18 msecs for the lumbar, wrist and shin sensors, respectively. Intraclass correlation coefficients, ICC(2,k), between the estimated parameters and the reference data resulted in good-to-excellent agreement (ICC \u2265 0.84) for the lumbar and shin sensors, excluding the double support time (ICC = 0.37 lumbar and 0.38 shin) and swing time (ICC = 0.55 lumbar and 0.59 shin). The wrist sensor also showed good agreements, but the ICCs were lower overall than for the other two sensors. Our proposed analysis pipeline has the potential to extract up to 100 gait-related parameters, and we expect our contribution will further support developments in the fields of wearable sensors, digital health, and remote monitoring in clinical trials.<\/jats:p>","DOI":"10.3390\/s21248286","type":"journal-article","created":{"date-parts":[[2021,12,13]],"date-time":"2021-12-13T01:29:33Z","timestamp":1639358973000},"page":"8286","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["An Automatic Gait Analysis Pipeline for Wearable Sensors: A Pilot Study in Parkinson\u2019s Disease"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3419-0792","authenticated-orcid":false,"given":"Luis R.","family":"Peraza","sequence":"first","affiliation":[{"name":"IXICO, London EC1A 9PN, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kirsi M.","family":"Kinnunen","sequence":"additional","affiliation":[{"name":"IXICO, London EC1A 9PN, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3761-296X","authenticated-orcid":false,"given":"Roisin","family":"McNaney","sequence":"additional","affiliation":[{"name":"Department of Human Centred Computing, Monash University, Clayton, VIC 3800, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ian J.","family":"Craddock","sequence":"additional","affiliation":[{"name":"Electrical and Electronic Engineering, School of Computer Science, University of Bristol, Bristol BS8 1QU, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alan L.","family":"Whone","sequence":"additional","affiliation":[{"name":"Translational Health Sciences, University of Bristol Medical School, Bristol BS8 1QU, UK"},{"name":"Movement Disorders Group, North Bristol NHS Trust, Westbury on Trym, Bristol BS10 5NB, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0333-2417","authenticated-orcid":false,"given":"Catherine","family":"Morgan","sequence":"additional","affiliation":[{"name":"Translational Health Sciences, University of Bristol Medical School, Bristol BS8 1QU, UK"},{"name":"Movement Disorders Group, North Bristol NHS Trust, Westbury on Trym, Bristol BS10 5NB, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Richard","family":"Joules","sequence":"additional","affiliation":[{"name":"IXICO, London EC1A 9PN, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Robin","family":"Wolz","sequence":"additional","affiliation":[{"name":"IXICO, London EC1A 9PN, UK"},{"name":"Department of Computing, Imperial College London, London SW7 2AZ, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,12,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1016\/j.gaitpost.2019.07.132","article-title":"The Primary Gait Screen in Parkinson\u2019s disease: Comparison to standardized measures","volume":"73","author":"Schmitt","year":"2019","journal-title":"Gait Posture"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"McCarney, R., Warner, J., Iliffe, S., Haselen, R.v., Griffin, M., and Fisher, P. (2007). The Hawthorne Effect: A randomised, controlled trial. BMC Med. Res. Methodol., 7.","DOI":"10.1186\/1471-2288-7-30"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1123\/jab.2013-0319","article-title":"Spatiotemporal Gait Patterns During Overt and Covert Evaluation in Patients With Parkinson\u00b4s Disease and Healthy Subjects: Is There a Hawthorne Effect?","volume":"31","author":"Espinosa","year":"2015","journal-title":"J. Appl. Biomech."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1038\/s41531-021-00171-0","article-title":"Gait speed in clinical and daily living assessments in Parkinson\u2019s disease patients: Performance versus capacity","volume":"7","author":"Atrsaei","year":"2021","journal-title":"Npj Parkinson\u2019s Dis."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"462","DOI":"10.1016\/S1474-4422(19)30397-7","article-title":"Long-term unsupervised mobility assessment in movement disorders","volume":"19","author":"Warmerdam","year":"2020","journal-title":"Lancet Neurol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1097\/00002826-200003000-00003","article-title":"A home diary to assess functional status in patients with Parkinson\u2019s disease with motor fluctuations and dyskinesia","volume":"23","author":"Hauser","year":"2000","journal-title":"Clin. Neuropharmacol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"216","DOI":"10.1159\/000517885","article-title":"Digital Endpoints: Definition, Benefits, and Current Barriers in Accelerating Development and Adoption","volume":"5","author":"Landers","year":"2021","journal-title":"Digit. Biomark."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.gaitpost.2016.08.012","article-title":"Gait event detection in laboratory and real life settings: Accuracy of ankle and waist sensor based methods","volume":"50","author":"Storm","year":"2016","journal-title":"Gait Posture"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"N1","DOI":"10.1088\/1361-6579\/38\/1\/N1","article-title":"Detecting free-living steps and walking bouts: Validating an algorithm for macro gait analysis","volume":"38","author":"Hickey","year":"2017","journal-title":"Physiol. Meas."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Tunca, C., Pehlivan, N., Ak, N., Arnrich, B., Salur, G., and Ersoy, C. (2017). Inertial Sensor-Based Robust Gait Analysis in Non-Hospital Settings for Neurological Disorders. Sensors, 17.","DOI":"10.3390\/s17040825"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1778","DOI":"10.21105\/joss.01778","article-title":"GaitPy: An Open-Source Python Package for Gait Analysis Using an Accelerometer on the Lower Back","volume":"4","author":"Czech","year":"2019","journal-title":"J. Open Source Softw."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"5824523","DOI":"10.1155\/2016\/5824523","article-title":"Interrupt-Based Step-Counting to Extend Battery Life in an Activity Monitor","volume":"2016","author":"Kim","year":"2016","journal-title":"J. Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"316","DOI":"10.1016\/j.gaitpost.2012.02.019","article-title":"An enhanced estimate of initial contact and final contact instants of time using lower trunk inertial sensor data","volume":"36","author":"McCamley","year":"2012","journal-title":"Gait Posture"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Godfrey, A., Din, S.D., Barry, G., Mathers, J.C., and Rochester, L. (2014, January 26\u201330). Within trial validation and reliability of a single tri-axial accelerometer for gait assessment. Proceedings of the 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Chicago, IL, USA.","DOI":"10.1109\/EMBC.2014.6944969"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"400","DOI":"10.1016\/j.medengphy.2015.02.003","article-title":"Instrumenting gait with an accelerometer: A system and algorithm examination","volume":"37","author":"Godfrey","year":"2015","journal-title":"Med. Eng. Phys."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"940","DOI":"10.1016\/j.gaitpost.2013.04.021","article-title":"Trunk-acceleration based assessment of gait parameters in older persons: A comparison of reliability and validity of four inverted pendulum based estimations","volume":"38","author":"Zijlstra","year":"2013","journal-title":"Gait Posture"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1251","DOI":"10.1007\/s11517-010-0692-0","article-title":"An adaptive gyroscope-based algorithm for temporal gait analysis","volume":"48","author":"Greene","year":"2010","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1186\/1743-0003-11-152","article-title":"Estimation of step-by-step spatio-temporal parameters of normal and impaired gait using shank-mounted magneto-inertial sensors: Application to elderly, hemiparetic, parkinsonian and choreic gait","volume":"11","author":"Trojaniello","year":"2014","journal-title":"J. Neuroeng. Rehabil."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Kreuzer, D., and Munz, M. (2021). Deep Convolutional and LSTM Networks on Multi-Channel Time Series Data for Gait Phase Recognition. Sensors, 21.","DOI":"10.3390\/s21030789"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"4760297","DOI":"10.1155\/2020\/4760297","article-title":"Walking Gait Phase Detection Based on Acceleration Signals Using Voting-Weighted Integrated Neural Network","volume":"2020","author":"Yan","year":"2020","journal-title":"Complexity"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Sarshar, M., Polturi, S., and Schega, L. (2021). Gait phase estimation by using LSTM in IMU-based gait analysis\u2014Proof of concept. Sensors, 21.","DOI":"10.3390\/s21175749"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Botros, A., Sch\u00fctz, N., Camenzind, M., Urwyler, P., Bolliger, D., Vanbellingen, T., Kistler, R., Bohlhalter, S., M\u00fcri, R.M., and Mosimann, U.P. (2019). Long-Term Home-Monitoring Sensor Technology in Patients with Parkinson\u2019s Disease-Acceptance and Adherence. Sensors, 19.","DOI":"10.3390\/s19235169"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1109\/MIS.2015.57","article-title":"Bridging e-Health and the Internet of Things: The SPHERE Project","volume":"30","author":"Zhu","year":"2015","journal-title":"IEEE Intell. Syst."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1038\/s41592-019-0686-2","article-title":"SciPy 1.0: Fundamental algorithms for scientific computing in Python","volume":"17","author":"Virtanen","year":"2020","journal-title":"Nat. Methods"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Doherty, A., Jackson, D., Hammerla, N., Pl\u00f6tz, T., Olivier, P., Granat, M.H., White, T., Hees, V.T.v., Trenell, M.I., and Owen, C.G. (2017). Large Scale Population Assessment of Physical Activity Using Wrist Worn Accelerometers: The UK Biobank Study. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0169649"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015). U-Net: Convolutional Networks for Biomedical Image Segmentation. International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"McFee, B., Raffel, C., Liang, D., Ellis, D.P., McVicar, M., Battenberg, E., and Nieto, O. (2015, January 6\u201312). librosa: Audio and Music Signal Analysis in Python. Proceedings of the 14th Python in Science Conference, Austin, TX, USA.","DOI":"10.25080\/Majora-7b98e3ed-003"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Peraza, L.R., Kinnunen, K.M., Joules, R., and Wolz, R. (2021, January 26\u201330). A convolutional neural network algorithm for step and gait bout estimation from wristband accelerometry. Proceedings of the Alzheimer\u2019s Association International Conference, Denver, CO, USA.","DOI":"10.1002\/alz.053487"},{"key":"ref_29","unstructured":"Peraza, L.R., Kinnunen, K., Joules, R., and Wolz, R. (2021, January 9\u201314). An adaptive step detection algorithm for waist-worn wearable devices: A feasibility study in older adults. Proceedings of the AD\/PD 2021, International Conference on Alzheimer\u2019s and Parkinson\u2019s Diseases, Barcelona, Spain."},{"key":"ref_30","unstructured":"Chollet, F. (2018). Keras: The Python Deep Learning library. Astrophys. Source Code Libr., Available online: https:\/\/keras.io\/."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Peraza, L.R., Joules, R., Dauvilliers, Y., and Wolz, R. (December, January 30). Device agnostic sleep-wake segment classification from wrist-worn accelerometry. Proceedings of the 2020 IEEE International Conference on Healthcare Informatics (ICHI), Oldenburg, Germany.","DOI":"10.1109\/ICHI48887.2020.9374318"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"561","DOI":"10.3389\/fneur.2018.00561","article-title":"Remote Monitoring in the Home Validates Clinical Gait Measures for Multiple Sclerosis","volume":"9","author":"Supratak","year":"2018","journal-title":"Front. Neurol."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Vandermeeren, S., Bruneel, H., and Steendam, H. (2020). Feature Selection for Machine Learning Based Step Length Estimation Algorithms. Sensors, 20.","DOI":"10.3390\/s20030778"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Shah, V.V., McNames, J., Harker, G., Mancini, M., Carlson-Kuhta, P., Nutt, J.G., El-Gohary, M., Curtze, C., and Horak, F.B. (2020). Effect of Bout Length on Gait Measures in People with and without Parkinson\u2019s Disease during Daily Life. Sensors, 20.","DOI":"10.3390\/s20205769"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1186\/s12984-016-0154-5","article-title":"Free-living gait characteristics in ageing and Parkinson\u2019s disease: Impact of environment and ambulatory bout length","volume":"13","author":"Din","year":"2016","journal-title":"J. Neuroeng. Rehabil."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"e041303","DOI":"10.1136\/bmjopen-2020-041303","article-title":"Protocol for PD SENSORS: Parkinson\u2019s Disease Symptom Evaluation in a Naturalistic Setting producing Outcome measuRes using SPHERE technology. An observational feasibility study of multi-modal multi-sensor technology to measure symptoms and activities of daily living in Parkinson\u2019s disease","volume":"10","author":"Morgan","year":"2020","journal-title":"BMJ Open"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1016\/j.jcm.2016.02.012","article-title":"A Guideline of Selecting and Reporting Intraclass Correlation Coefficients for Reliability Research","volume":"15","author":"Koo","year":"2016","journal-title":"J. Chiropr. Med."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1026","DOI":"10.21105\/joss.01026","article-title":"Pingouin: Statistics in Python","volume":"3","author":"Vallat","year":"2018","journal-title":"J. Open Source Softw."},{"key":"ref_39","unstructured":"Seabold, S., and Perktold, J. (July, January 28). Statsmodels: Econometric and Statistical Modeling with Python. Proceedings of the 9th Python in Science Conference, Austin, TX, USA."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1016\/j.gaitpost.2011.03.024","article-title":"Normative spatiotemporal gait parameters in older adults","volume":"34","author":"Hollman","year":"2011","journal-title":"Gait Posture"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.medengphy.2020.11.005","article-title":"Gait analysis in neurological populations: Progression in the use of wearables","volume":"87","author":"Celik","year":"2021","journal-title":"Med. Eng. Phys."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"617","DOI":"10.1249\/MSS.0000000000001124","article-title":"Comparison of Accelerometry Methods for Estimating Physical Activity","volume":"49","author":"Kerr","year":"2017","journal-title":"Med. Sci. Sports Exerc."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Rehman, R.Z.U., Klocke, P., Hryniv, S., Galna, B., Rochester, L., Din, S.D., and Alcock, L. (2020). Turning Detection During Gait: Algorithm Validation and Influence of Sensor Location and Turning Characteristics in the Classification of Parkinson\u2019s Disease. Sensors, 20.","DOI":"10.3390\/s20185377"},{"key":"ref_44","unstructured":"Alvarez, D., Gonzalez, R.C., Lopez, A., and Alvarez, J.C. (September, January 30). Comparison of step length estimators from weareable accelerometer devices. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, New York, NY, USA."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Ardle, R.M., Din, S.D., Donaghy, P., Galna, B., Thomas, A.J., and Rochester, L. (2021). The Impact of Environment on Gait Assessment: Considerations from Real-World Gait Analysis in Dementia Subtypes. Sensors, 21.","DOI":"10.3390\/s21030813"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"e19068","DOI":"10.2196\/19068","article-title":"Real-Life Gait Performance as a Digital Biomarker for Motor Fluctuations: The Parkinson@Home Validation Study","volume":"22","author":"Evers","year":"2020","journal-title":"J. Med. Internet Res."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1134","DOI":"10.1520\/JFS13129J","article-title":"Height estimation from foot and shoeprint length","volume":"36","author":"Giles","year":"1991","journal-title":"J. Forensic Sci."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"310","DOI":"10.1136\/adc.43.229.310","article-title":"Prediction of height from foot length: Use of measurement in field surveys","volume":"43","author":"Rutishauser","year":"1968","journal-title":"Arch. Dis. Child."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/24\/8286\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:45:29Z","timestamp":1760168729000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/24\/8286"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,12,11]]},"references-count":48,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2021,12]]}},"alternative-id":["s21248286"],"URL":"https:\/\/doi.org\/10.3390\/s21248286","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,12,11]]}}}