{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T02:27:51Z","timestamp":1760236071824,"version":"build-2065373602"},"reference-count":26,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2021,10,21]],"date-time":"2021-10-21T00:00:00Z","timestamp":1634774400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000038","name":"Natural Sciences and Engineering Research Council of Canada","doi-asserted-by":"publisher","award":["RGPIN-2019-04106"],"award-info":[{"award-number":["RGPIN-2019-04106"]}],"id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Foot strike detection is important when evaluating a person\u2019s gait characteristics. Accelerometer and gyroscope signals from smartphones have been used to train artificial intelligence (AI) models for automated foot strike detection in able-bodied and elderly populations. However, there is limited research on foot strike detection in lower limb amputees, who have a more variable and asymmetric gait. A novel method for automated foot strike detection in lower limb amputees was developed using raw accelerometer and gyroscope signals collected from a smartphone positioned at the posterior pelvis. Raw signals were used to train a decision tree model and long short-term memory (LSTM) model for automated foot strike detection. These models were developed using retrospective data (n = 72) collected with the TOHRC Walk Test app during a 6-min walk test (6MWT). An Android smartphone was placed on a posterior belt for each participant during the 6MWT to collect accelerometer and gyroscope signals at 50 Hz. The best model for foot strike identification was the LSTM with 100 hidden nodes in the LSTM layer, 50 hidden nodes in the dense layer, and a batch size of 64 (99.0% accuracy, 86.4% sensitivity, 99.4% specificity, and 83.7% precision). This research created a novel method for automated foot strike identification in lower extremity amputee populations that is equivalent to manual labelling and accessible for clinical use. Automated foot strike detection is required for stride analysis and to enable other AI applications, such as fall detection.<\/jats:p>","DOI":"10.3390\/s21216974","type":"journal-article","created":{"date-parts":[[2021,10,21]],"date-time":"2021-10-21T23:27:39Z","timestamp":1634858859000},"page":"6974","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Comparison of Decision Tree and Long Short-Term Memory Approaches for Automated Foot Strike Detection in Lower Extremity Amputee Populations"],"prefix":"10.3390","volume":"21","author":[{"given":"Pascale","family":"Juneau","sequence":"first","affiliation":[{"name":"Ottawa Hospital Research Institute, Ottawa, ON K1Y 4E9, Canada"},{"name":"Department of Mechanical Engineering, University of Ottawa, Ottawa, ON K1N 6N5, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7025-7501","authenticated-orcid":false,"given":"Natalie","family":"Baddour","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, University of Ottawa, Ottawa, ON K1N 6N5, Canada"}]},{"given":"Helena","family":"Burger","sequence":"additional","affiliation":[{"name":"University Rehabilitation Institute, University of Ljubljana, 1000 Ljubljana, Slovenia"},{"name":"Faculty of Medicine, University of Ljubljana, 1000 Ljubljana, Slovenia"}]},{"given":"Andrej","family":"Bavec","sequence":"additional","affiliation":[{"name":"University Rehabilitation Institute, University of Ljubljana, 1000 Ljubljana, Slovenia"},{"name":"Faculty of Medicine, University of Ljubljana, 1000 Ljubljana, Slovenia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4693-2623","authenticated-orcid":false,"given":"Edward D.","family":"Lemaire","sequence":"additional","affiliation":[{"name":"Ottawa Hospital Research Institute, Ottawa, ON K1Y 4E9, Canada"},{"name":"Faculty of Medicine, University of Ottawa, Ottawa, ON K1N 6N5, Canada"}]}],"member":"1968","published-online":{"date-parts":[[2021,10,21]]},"reference":[{"key":"ref_1","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. Neuroeng. Rehabil."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Maqbool, H.F., Husman, M.A.B., Awad, M., Abouhossein, A., Mehryar, P., Iqbal, N., and Dehghani-Sanij, A.A. (2016, January 16\u201320). Real-time gait event detection for lower limb amputees using a single wearable sensor. Proceedings of the 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Orlando, FL, USA.","DOI":"10.1109\/EMBC.2016.7591866"},{"key":"ref_3","unstructured":"Meng, X., Yu, H., and Tham, M.P. (2013, January 3\u20137). Gait phase detection in able-bodied subjects and dementia patients. Proceedings of the 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Osaka, Japan."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1186\/s12984-015-0013-9","article-title":"Novel algorithm for a smartphone-based 6-minute walk test application: Algorithm, application development, and evaluation","volume":"12","author":"Capela","year":"2015","journal-title":"J. Neuroeng. Rehabil."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1016\/j.gaitpost.2017.06.011","article-title":"Gait parameter and event estimation using smartphones","volume":"57","author":"Pepa","year":"2017","journal-title":"Gait Posture"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"e36","DOI":"10.2196\/mhealth.8815","article-title":"Smartphone App\u2013Based Assessment of Gait During Normal and Dual-Task Walking: Demonstration of Validity and Reliability","volume":"6","author":"Manor","year":"2018","journal-title":"JMIR MHealth UHealth"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1778","DOI":"10.1080\/09638288.2018.1449258","article-title":"Fall incidence and associated risk factors among people with a lower limb amputation during various stages of recovery\u2014A systematic review","volume":"41","author":"Steinberg","year":"2018","journal-title":"Disabil. Rehabil."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Vu, H.T.T., Dong, D., Cao, H.-L., Verstraten, T., Lefeber, D., VanderBorght, B., and Geeroms, J. (2020). A Review of Gait Phase Detection Algorithms for Lower Limb Prostheses. Sensors, 20.","DOI":"10.3390\/s20143972"},{"key":"ref_9","unstructured":"Thibault, G. (2019). Amputee FS Identification Accuracy from 6 Minute Walk Test Raw Data. [Licentiate Thesis, Dept. Mechanical Engineering, University of Ottawa]."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Daines, K.J.F., Baddour, N., Burger, H., Bavec, A., and Lemaire, E.D. (2021). Fall risk classification for people with lower extremity amputations using random forests and smartphone sensor features from a 6-minute walk test. PLoS ONE, 16.","DOI":"10.1371\/journal.pone.0247574"},{"key":"ref_11","first-page":"283","article-title":"Human gait analysis based on decision tree, random forest and KNN algorithms","volume":"Volume 1155","author":"Iyer","year":"2020","journal-title":"Applied Computer Vision and Image Processing. Advances in Intelligent Systems and Computing"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Zhang, Y., and Gu, D. (2019, January 19\u201321). A Deep Convolutional-Recurrent Neural Network for Freezing of Gait Detection in Patients with Parkinson\u2019s Disease. Proceedings of the 2019 12th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), Suzhou, China.","DOI":"10.1109\/CISP-BMEI48845.2019.8965723"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Kidzi\u0144ski, \u0141., Delp, S., and Schwartz, M. (2019). Automatic real-time gait event detection in children using deep neural networks. PLoS ONE, 14.","DOI":"10.1371\/journal.pone.0211466"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"130","DOI":"10.1016\/j.gaitpost.2020.05.026","article-title":"A data-driven approach for detecting gait events during turning in people with Parkinson\u2019s disease and freezing of gait","volume":"80","author":"Filtjens","year":"2020","journal-title":"Gait Posture"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"105400","DOI":"10.1016\/j.cmpb.2020.105400","article-title":"Decision tree-based diagnosis of coronary artery disease: CART model","volume":"192","author":"Ghiasi","year":"2020","journal-title":"Comput. Methods Progr. Biomed."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Sathiyanarayanan, P., Pavithra, S., Saranya, M.S., and Makeswari, M. (2019, January 29\u201330). Identification of breast cancer using the decision tree algorithm. Proceedings of the 2019 IEEE International Conference on System, Computation, Automation and Networking (ICSCAN), Pondicherry, India.","DOI":"10.1109\/ICSCAN.2019.8878757"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1186\/s12984-019-0486-z","article-title":"Design, development, and evaluation of a local sensor-based gait phase recognition system using a logistic model decision tree for orthosis-control","volume":"16","author":"Farah","year":"2019","journal-title":"J. Neuroeng. Rehabil."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Kamath, C.N., Bukhari, S.S., and Dengel, A. Comparative study between traditional machine learning and deep learning approaches for text classification. Proceedings of the ACM Symposium on Document Engineering 2018 (DocEng \u201818).","DOI":"10.1145\/3209280.3209526"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"855","DOI":"10.1109\/TPAMI.2008.137","article-title":"A Novel Connectionist System for Unconstrained Handwriting Recognition","volume":"31","author":"Graves","year":"2009","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"355","DOI":"10.1016\/j.pmr.2018.12.007","article-title":"Gait Segmentation of Data Collected by Instrumented Shoes Using a Recurrent Neural Network Classifier","volume":"30","author":"Prado","year":"2019","journal-title":"Phys. Med. Rehabil. Clin. North Am."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Mekruksavanich, S., and Jitpattanakul, A. (2021). LSTM Networks Using Smartphone Data for Sensor-Based Human Activity Recognition in Smart Homes. Sensors, 21.","DOI":"10.3390\/s21051636"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"101","DOI":"10.5535\/arm.2014.38.1.101","article-title":"Comparing self-selected speed walking of the elderly with self-selected slow, moderate, and fast speed walking of young adults","volume":"38","author":"Kim","year":"2014","journal-title":"Ann. Rehabil. Med."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1016\/j.gaitpost.2019.09.007","article-title":"Time series classification using a modified LSTM approach from accelerometer-based data: A comparative study for gait cycle detection","volume":"74","author":"Tan","year":"2019","journal-title":"Gait Posture"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.gaitpost.2015.11.014","article-title":"The test\u2013retest reliability and minimal detectable change of spatial and temporal gait variability during usual over-ground walking for younger and older adults","volume":"44","author":"Almarwani","year":"2016","journal-title":"Gait Posture"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"R\u00e1bago, C.A., Dingwell, J.B., and Wilken, J.M. (2015). Reliability and minimum detectable change of temporal-spatial, kinematic, and dynamic stability measures during perturbed gait. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0142083"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"10229","DOI":"10.1038\/s41598-021-88794-4","article-title":"Spatio-temporal gait parameters obtained from foot-worn inertial sensors are reliable in healthy adults in single- and dual-task conditions","volume":"11","author":"Soulard","year":"2021","journal-title":"Sci. Rep."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/21\/6974\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:19:45Z","timestamp":1760167185000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/21\/6974"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,21]]},"references-count":26,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2021,11]]}},"alternative-id":["s21216974"],"URL":"https:\/\/doi.org\/10.3390\/s21216974","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2021,10,21]]}}}