{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T02:37:24Z","timestamp":1760150244941,"version":"build-2065373602"},"reference-count":51,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2023,11,4]],"date-time":"2023-11-04T00:00:00Z","timestamp":1699056000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001505","name":"Health Research Council of New Zealand","doi-asserted-by":"publisher","award":["18\/414","853981","820820"],"award-info":[{"award-number":["18\/414","853981","820820"]}],"id":[{"id":"10.13039\/501100001505","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Innovative Medicines Initiative 2 Joint Undertaking","award":["18\/414","853981","820820"],"award-info":[{"award-number":["18\/414","853981","820820"]}]},{"name":"European Union\u2019s Horizon","award":["18\/414","853981","820820"],"award-info":[{"award-number":["18\/414","853981","820820"]}]},{"name":"European Union\u2019s Horizon","award":["18\/414","853981","820820"],"award-info":[{"award-number":["18\/414","853981","820820"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Accurate and reliable measurement of real-world walking activity is clinically relevant, particularly for people with mobility difficulties. Insights on walking can help understand mobility function, disease progression, and fall risks. People living in long-term residential care environments have heterogeneous and often pathological walking patterns, making it difficult for conventional algorithms paired with wearable sensors to detect their walking activity. We designed two walking bout detection algorithms for people living in long-term residential care. Both algorithms used thresholds on the magnitude of acceleration from a 3-axis accelerometer on the lower back to classify data as \u201cwalking\u201d or \u201cnon-walking\u201d. One algorithm had generic thresholds, whereas the other used personalized thresholds. To validate and evaluate the algorithms, we compared the classifications of walking\/non-walking from our algorithms to the real-time research assistant annotated labels and the classification output from an algorithm validated on a healthy population. Both the generic and personalized algorithms had acceptable accuracy (0.83 and 0.82, respectively). The personalized algorithm showed the highest specificity (0.84) of all tested algorithms, meaning it was the best suited to determine input data for gait characteristic extraction. The developed algorithms were almost 60% quicker than the previously developed algorithms, suggesting they are adaptable for real-time processing.<\/jats:p>","DOI":"10.3390\/s23218973","type":"journal-article","created":{"date-parts":[[2023,11,4]],"date-time":"2023-11-04T15:41:41Z","timestamp":1699112501000},"page":"8973","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Walking Bout Detection for People Living in Long Residential Care: A Computationally Efficient Algorithm for a 3-Axis Accelerometer on the Lower Back"],"prefix":"10.3390","volume":"23","author":[{"given":"Mhairi K.","family":"MacLean","sequence":"first","affiliation":[{"name":"Department of Biomechanical Engineering, Faculty of Engineering Technology, University of Twente, 7522 LW Enschede, The Netherlands"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4885-7455","authenticated-orcid":false,"given":"Rana Zia Ur","family":"Rehman","sequence":"additional","affiliation":[{"name":"Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne NE2 4HH, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5992-3681","authenticated-orcid":false,"given":"Ngaire","family":"Kerse","sequence":"additional","affiliation":[{"name":"School of Population Health, Faculty of Medical and Health Sciences, University of Auckland, Auckland 1023, New Zealand"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5284-1501","authenticated-orcid":false,"given":"Lynne","family":"Taylor","sequence":"additional","affiliation":[{"name":"School of Population Health, Faculty of Medical and Health Sciences, University of Auckland, Auckland 1023, New Zealand"}]},{"given":"Lynn","family":"Rochester","sequence":"additional","affiliation":[{"name":"Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne NE2 4HH, UK"},{"name":"The Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne NE7 7DN, UK"},{"name":"National Institute for Health and Care Research (NIHR), Newcastle Biomedical Research Centre (BRC), Newcastle University and The Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne NE2 4HH, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1154-4751","authenticated-orcid":false,"given":"Silvia","family":"Del Din","sequence":"additional","affiliation":[{"name":"Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne NE2 4HH, UK"},{"name":"National Institute for Health and Care Research (NIHR), Newcastle Biomedical Research Centre (BRC), Newcastle University and The Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne NE2 4HH, UK"}]}],"member":"1968","published-online":{"date-parts":[[2023,11,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1123\/JAPA.2013-0091","article-title":"Levels and Patterns of Daily Physical Activity and Sedentary Behavior Measured Objectively in Older Care Home Residents in the United Kingdom","volume":"23","author":"Barber","year":"2015","journal-title":"J. Aging Phys. Act."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1159\/000512513","article-title":"A Roadmap to Inform Development, Validation and Approval of Digital Mobility Outcomes: The Mobilise-D Approach","volume":"4","author":"Rochester","year":"2020","journal-title":"Digit. Biomark."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1038\/s41746-021-00513-5","article-title":"Walking on Common Ground: A Cross-Disciplinary Scoping Review on the Clinical Utility of Digital Mobility Outcomes","volume":"4","author":"Polhemus","year":"2021","journal-title":"npj Digit. Med."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1186\/s13063-019-3949-4","article-title":"Evaluating the Effects of an Exercise Program (Staying UpRight) for Older Adults in Long-Term Care on Rates of Falls: Study Protocol for a Randomised Controlled Trial","volume":"21","author":"Taylor","year":"2020","journal-title":"Trials"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"442","DOI":"10.7326\/0003-4819-121-6-199409150-00009","article-title":"Falls in the Nursing Home","volume":"121","author":"Rubenstein","year":"1994","journal-title":"Ann. Intern. Med."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1111\/j.1467-842X.2004.tb00933.x","article-title":"Physical Activity: Wearing Slippers, Falls and Injury in Residential Care","volume":"28","author":"Kerse","year":"2004","journal-title":"Aust. N. Z. J. Public Health"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"685","DOI":"10.1016\/j.jamda.2013.05.015","article-title":"Exercise for Falls and Fracture Prevention in Long Term Care Facilities: A Systematic Review and Meta-Analysis","volume":"14","author":"Silva","year":"2013","journal-title":"J. Am. Med. Dir. Assoc."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"891","DOI":"10.1007\/s00198-009-1100-1","article-title":"Cost of Falls in Old Age: A Systematic Review","volume":"21","author":"Heinrich","year":"2010","journal-title":"Osteoporos Int"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1","DOI":"10.2165\/00007256-200029010-00001","article-title":"Exercise, Mobility and Aging","volume":"29","author":"Daley","year":"2000","journal-title":"Sports Med."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1111\/prd.12126","article-title":"The Aging Population: Demographics and the Biology of Aging","volume":"72","author":"Kanasi","year":"2016","journal-title":"Periodontology"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"e12861","DOI":"10.1111\/acel.12861","article-title":"Trends in Age-Related Disease Burden and Healthcare Utilization","volume":"18","author":"Atella","year":"2019","journal-title":"Aging Cell"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2013","DOI":"10.1377\/hlthaff.2013.0714","article-title":"An Aging Population And Growing Disease Burden Will Require ALarge And Specialized Health Care Workforce By 2025","volume":"32","author":"Dall","year":"2013","journal-title":"Health Aff."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"e050785","DOI":"10.1136\/bmjopen-2021-050785","article-title":"Technical Validation of Real-World Monitoring of Gait: A Multicentric Observational Study","volume":"11","author":"Alcock","year":"2021","journal-title":"BMJ Open"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Mc Ardle, R., Del Din, S., 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_15","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1186\/s12984-023-01198-5","article-title":"Assessing Real-World Gait with Digital Technology? Validation, Insights and Recommendations from the Mobilise-D Consortium","volume":"20","author":"Bonci","year":"2023","journal-title":"J. NeuroEngineering Rehabil."},{"key":"ref_16","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_17","doi-asserted-by":"crossref","first-page":"500","DOI":"10.1093\/gerona\/glx254","article-title":"Analysis of Free-Living Gait in Older Adults With and Without Parkinson\u2019s Disease and With and Without a History of Falls: Identifying Generic and Disease-Specific Characteristics","volume":"74","author":"Galna","year":"2019","journal-title":"J. Gerontol. Ser. A"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1186\/1743-0003-11-48","article-title":"Automated Detection of Missteps during Community Ambulation in Patients with Parkinson\u2019s Disease: A New Approach for Quantifying Fall Risk in the Community Setting","volume":"11","author":"Iluz","year":"2014","journal-title":"J. NeuroEngineering Rehabil."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Taborri, J., Palermo, E., Rossi, S., and Cappa, P. (2016). Gait Partitioning Methods: A Systematic Review. Sensors, 16.","DOI":"10.3390\/s16010066"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Prasanth, H., Caban, M., Keller, U., Courtine, G., Ijspeert, A., Vallery, H., and von Zitzewitz, J. (2021). Wearable Sensor-Based Real-Time Gait Detection: A Systematic Review. Sensors, 21.","DOI":"10.3390\/s21082727"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2531","DOI":"10.1109\/TMC.2016.2623304","article-title":"Gazelle: Energy-Efficient Wearable Analysis for Running","volume":"16","author":"Liu","year":"2017","journal-title":"IEEE Trans. Mob. Comput."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"13251","DOI":"10.1109\/JSEN.2022.3177951","article-title":"Adaptive Algorithm for Gait Segmentation Using a Single IMU in the Thigh Pocket","volume":"22","author":"Garcia","year":"2022","journal-title":"IEEE Sens. J."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Abhayasinghe, N., and Murray, I. (2014, January 21\u201324). Human Gait Phase Recognition Based on Thigh Movement Computed Using IMUs. Proceedings of the 2014 IEEE Ninth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP), Singapore.","DOI":"10.1109\/ISSNIP.2014.6827604"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2555","DOI":"10.1109\/JSEN.2017.2786587","article-title":"Optimal Foot Location for Placing Wearable IMU Sensors and Automatic Feature Extraction for Gait Analysis","volume":"18","author":"Anwary","year":"2018","journal-title":"IEEE Sens. J."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"2616","DOI":"10.1109\/JSEN.2019.2951923","article-title":"Real-Time Identification of Gait Events in Impaired Subjects Using a Single-IMU Foot-Mounted Device","volume":"20","author":"Siqueira","year":"2020","journal-title":"IEEE Sens. J."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"322","DOI":"10.1016\/j.gaitpost.2009.11.014","article-title":"Real-Time Gait Event Detection for Normal Subjects from Lower Trunk Accelerations","volume":"31","author":"Alvarez","year":"2010","journal-title":"Gait Posture"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/S0966-6362(02)00190-X","article-title":"Assessment of Spatio-Temporal Gait Parameters from Trunk Accelerations during Human Walking","volume":"18","author":"Zijlstra","year":"2003","journal-title":"Gait Posture"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"20552076221150745","DOI":"10.1177\/20552076221150745","article-title":"Acceptability of Wearable Devices for Measuring Mobility Remotely: Observations from the Mobilise-D Technical Validation Study","volume":"9","author":"Keogh","year":"2023","journal-title":"Digit Health"},{"key":"ref_29","first-page":"1869","article-title":"Detection of Gait From Continuous Inertial Sensor Data Using Harmonic Frequencies","volume":"24","author":"Ullrich","year":"2020","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Carcreff, L., Gerber, C.N., Paraschiv-Ionescu, A., De Coulon, G., Newman, C.J., Armand, S., and Aminian, K. (2018). What Is the Best Configuration of Wearable Sensors to Measure Spatiotemporal Gait Parameters in Children with Cerebral Palsy?. Sensors, 18.","DOI":"10.3390\/s18020394"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1016\/j.gaitpost.2013.08.029","article-title":"Spatio-Temporal Gait Analysis in Children with Cerebral Palsy Using, Foot-Worn Inertial Sensors","volume":"39","author":"Mariani","year":"2014","journal-title":"Gait Posture"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Schlachetzki, J.C.M., Barth, J., Marxreiter, F., Gossler, J., Kohl, Z., Reinfelder, S., Gassner, H., Aminian, K., Eskofier, B.M., and Winkler, J. (2017). Wearable Sensors Objectively Measure Gait Parameters in Parkinson\u2019s Disease. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0183989"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"838","DOI":"10.1109\/JBHI.2015.2419317","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":"2016","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1955","DOI":"10.1109\/TNSRE.2021.3111681","article-title":"Algorithms for Walking Speed Estimation Using a Lower-Back-Worn Inertial Sensor: A Cross-Validation on Speed Ranges","volume":"29","author":"Soltani","year":"2021","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_35","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_36","doi-asserted-by":"crossref","unstructured":"Tobis, S., Jaracz, K., Kropi\u0144ska, S., Talarska, D., Hoe, J., Wieczorowska-Tobis, K., and Suwalska, A. (2021). Needs of Older Persons Living in Long-Term Care Institutions: On the Usefulness of Cluster Approach. BMC Geriatr., 21.","DOI":"10.1186\/s12877-021-02259-x"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Maddox, G.L., and Lawton, M.P. (1988). Varieties of Aging, Springer.","DOI":"10.1007\/978-3-662-40050-0"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"e44352","DOI":"10.2196\/44352","article-title":"Using Digital Technology to Quantify Habitual Physical Activity in Community Dwellers With Cognitive Impairment: Systematic Review","volume":"25","author":"Ardle","year":"2023","journal-title":"J. Med. Internet Res."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Cola, G., Avvenuti, M., Musso, F., and Vecchio, A. (2017, January 9\u201312). Personalized Gait Detection Using a Wrist-Worn Accelerometer. Proceedings of the 2017 IEEE 14th International Conference on Wearable and Implantable Body Sensor Networks (BSN), Eindhoven, The Netherlands.","DOI":"10.1109\/BSN.2017.7936035"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"261","DOI":"10.3389\/fneur.2020.00261","article-title":"Personalized Template-Based Step Detection From Inertial Measurement Units Signals in Multiple Sclerosis","volume":"11","author":"Oudre","year":"2020","journal-title":"Front. Neurol."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1007\/s11910-018-0896-5","article-title":"Wearable Sensors to Monitor, Enable Feedback, and Measure Outcomes of Activity and Practice","volume":"18","author":"Dobkin","year":"2018","journal-title":"Curr Neurol Neurosci Rep"},{"key":"ref_42","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":"2016","journal-title":"Physiol. Meas."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Potluri, S., Chandran, A.B., Diedrich, C., and Schega, L. (2019, January 23\u201327). Machine Learning Based Human Gait Segmentation with Wearable Sensor Platform. Proceedings of the 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin, Germany.","DOI":"10.1109\/EMBC.2019.8857509"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"O\u2019Callaghan, B.P.F., Doheny, E.P., Goulding, C., Fortune, E., and Lowery, M.M. (2020, January 20\u201324). Adaptive Gait Segmentation Algorithm for Walking Bout Detection Using Tri-Axial Accelerometers. Proceedings of the 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Montreal, QC, Canada.","DOI":"10.1109\/EMBC44109.2020.9176460"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Halfwerk, F.R., van Haaren, J.H.L., Klaassen, R., van Delden, R.W., Veltink, P.H., and Grandjean, J.G. (2021). Objective Quantification of In-Hospital Patient Mobilization after Cardiac Surgery Using Accelerometers: Selection, Use, and Analysis. Sensors, 21.","DOI":"10.3390\/s21061979"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1186\/s12984-019-0494-z","article-title":"Locomotion and Cadence Detection Using a Single Trunk-Fixed Accelerometer: Validity for Children with Cerebral Palsy in Daily Life-like Conditions","volume":"16","author":"Newman","year":"2019","journal-title":"J. NeuroEngineering Rehabil."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Paraschiv-Ionescu, A., Soltani, A., and Aminian, K. (2020, January 20\u201324). Real-World Speed Estimation Using Single Trunk IMU: Methodological Challenges for Impaired Gait Patterns. Proceedings of the 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Montreal, QC, Canada.","DOI":"10.1109\/EMBC44109.2020.9176281"},{"key":"ref_48","unstructured":"Ladha, C., Jackson, D., Ladha, K., Nappey, T., and Olivier, P. (2013, January 17\u201319). Shaker Table Validation of OpenMovement Accelerometer. Proceedings of the Presented at the 3rd International Conference on Ambulatory Monitoring of Physical Activity and Movement, Amherst, MA, USA."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"320","DOI":"10.1016\/S0268-0033(98)00089-8","article-title":"A New Method for Evaluating Motor Control in Gait under Real-Life Environmental Conditions. Part 1: The Instrument","volume":"13","year":"1998","journal-title":"Clin. Biomech."},{"key":"ref_50","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":"Godfrey","year":"2016","journal-title":"J. NeuroEngineering Rehabil."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"676","DOI":"10.1016\/j.gaitpost.2014.07.023","article-title":"Trunk Sway during Walking among Older Adults: Norms and Correlation with Gait Velocity","volume":"40","author":"Lee","year":"2014","journal-title":"Gait Posture"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/21\/8973\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:17:42Z","timestamp":1760131062000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/21\/8973"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,4]]},"references-count":51,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2023,11]]}},"alternative-id":["s23218973"],"URL":"https:\/\/doi.org\/10.3390\/s23218973","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2023,11,4]]}}}