{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,1,6]],"date-time":"2025-01-06T04:40:16Z","timestamp":1736138416485,"version":"3.32.0"},"reference-count":46,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2022,10,27]],"date-time":"2022-10-27T00:00:00Z","timestamp":1666828800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100019266","name":"Korea Medical Device Development Fund","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100019266","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Korea government (the Ministry of Science and ICT, the Ministry of Trade, Industry and Energy, the Ministry of Health & Welfare, the Ministry of Food and Drug Safety)","award":["1711139131"]},{"name":"Athletes\u2019 training\/matches data management and AI-based performance enhancement solution technology Development Project","award":["1375027374"]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"Elderly gait is a source of rich information about their physical and mental health condition. As an alternative to the multiple sensors on the lower body parts, a single sensor on the pelvis has a positional advantage and an abundance of information acquirable. This study aimed to improve the accuracy of gait event detection in the elderly using a single sensor on the waist and deep learning models. Data were gathered from elderly subjects equipped with three IMU sensors while they walked. The input taken only from the waist sensor was used to train 16 deep-learning models including a CNN, RNN, and CNN-RNN hybrid with or without the Bidirectional and Attention mechanism. The groundtruth was extracted from foot IMU sensors. A fairly high accuracy of 99.73% and 93.89% was achieved by the CNN-BiGRU-Att model at the tolerance window of \u00b16 TS (\u00b16 ms) and \u00b11 TS (\u00b11 ms), respectively. Advancing from the previous studies exploring gait event detection, the model demonstrated a great improvement in terms of its prediction error having an MAE of 6.239 ms and 5.24 ms for HS and TO events, respectively, at the tolerance window of \u00b11 TS. The results demonstrated that the use of CNN-RNN hybrid models with Attention and Bidirectional mechanisms is promising for accurate gait event detection using a single waist sensor. The study can contribute to reducing the burden of gait detection and increase its applicability in future wearable devices that can be used for remote health monitoring (RHM) or diagnosis based thereon.<\/jats:p>","DOI":"10.3390\/s22218226","type":"journal-article","created":{"date-parts":[[2022,10,28]],"date-time":"2022-10-28T02:36:17Z","timestamp":1666924577000},"page":"8226","source":"Crossref","is-referenced-by-count":15,"title":["Gait Events Prediction Using Hybrid CNN-RNN-Based Deep Learning Models through a Single Waist-Worn Wearable Sensor"],"prefix":"10.3390","volume":"22","author":[{"given":"Muhammad Zeeshan","family":"Arshad","sequence":"first","affiliation":[{"name":"Center for Artificial Intelligence, Korea Institute of Science and Technology, Seoul 02792, Korea"}]},{"given":"Ankhzaya","family":"Jamsrandorj","sequence":"additional","affiliation":[{"name":"Department of Human-Computer Interface & Robotics Engineering, University of Science & Technology, Daejon 34113, Korea"}]},{"given":"Jinwook","family":"Kim","sequence":"additional","affiliation":[{"name":"Center for Artificial Intelligence, Korea Institute of Science and Technology, Seoul 02792, Korea"}]},{"given":"Kyung-Ryoul","family":"Mun","sequence":"additional","affiliation":[{"name":"Center for Artificial Intelligence, Korea Institute of Science and Technology, Seoul 02792, Korea"},{"name":"KHU-KIST Department of Converging Science and Technology, Kyung Hee University, Seoul 02447, Korea"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1001\/jama.2010.1923","article-title":"Gait speed and survival in older adults","volume":"305","author":"Studenski","year":"2011","journal-title":"JAMA"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Arshad, M.Z., Jung, D., Park, M., Shin, H., Kim, J., and Mun, K.R. (2021, January 1\u20135). Gait-based Frailty Assessment using Image Representation of IMU Signals and Deep CNN. Proceedings of the 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Online.","DOI":"10.1109\/EMBC46164.2021.9630976"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"4029","DOI":"10.1109\/JBHI.2021.3073372","article-title":"Classifying the Risk of Cognitive Impairment Using Sequential Gait Characteristics and Long Short-Term Memory Networks","volume":"25","author":"Jung","year":"2021","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1244","DOI":"10.1111\/j.1532-5415.2008.01758.x","article-title":"Gait dysfunction in mild cognitive impairment syndromes","volume":"56","author":"Verghese","year":"2008","journal-title":"J. Am. Geriatr. Soc."},{"key":"ref_5","first-page":"929","article-title":"Assessing the temporal relationship between cognition and gait: Slow gait predicts cognitive decline in the Mayo Clinic Study of Aging","volume":"68","author":"Mielke","year":"2013","journal-title":"J. Gerontol. Ser. Biomed. Sci. Med. Sci."},{"key":"ref_6","first-page":"61","article-title":"Gait and balance disorders in older adults","volume":"82","author":"Salzman","year":"2010","journal-title":"Am. Fam. Phys."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"586","DOI":"10.3949\/ccjm.72.7.586","article-title":"Gait disorders: Search for multiple causes","volume":"72","author":"Alexander","year":"2005","journal-title":"Clevel. Clin. J. Med."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"493","DOI":"10.1016\/j.amjmed.2006.07.022","article-title":"Falls in older adults: Risk assessment, management and prevention","volume":"120","author":"Moylan","year":"2007","journal-title":"Am. J. Med."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"434","DOI":"10.1111\/j.1532-5415.1996.tb06417.x","article-title":"Gait disorders in older adults","volume":"44","author":"Alexander","year":"1996","journal-title":"J. Am. Geriatr. Soc."},{"key":"ref_10","first-page":"111","article-title":"Gait disorders: Prevalence, morbidity, and etiology","volume":"87","author":"Sudarsky","year":"2001","journal-title":"Adv. Neurol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1027","DOI":"10.1093\/ptj\/65.7.1027","article-title":"Reliability of observational kinematic gait analysis","volume":"65","author":"Krebs","year":"1985","journal-title":"Phys. Ther."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"384","DOI":"10.1080\/00140139.2016.1174314","article-title":"Development of an IMU-based foot-ground contact detection (FGCD) algorithm","volume":"60","author":"Kim","year":"2017","journal-title":"Ergonomics"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Oudre, L., Barrois-M\u00fcller, R., Moreau, T., Truong, C., Vienne-Jumeau, A., Ricard, D., Vayatis, N., and Vidal, P.P. (2018). Template-based step detection with inertial measurement units. Sensors, 18.","DOI":"10.3390\/s18114033"},{"key":"ref_14","unstructured":"Lee, H.K., Hwang, S.J., Cho, S.P., Lee, D.R., You, S.H., Lee, K.J., Kim, Y.H., and Choi, H.S. (2009, January 3\u20136). Novel algorithm for the hemiplegic gait evaluation using a single 3-axis accelerometer. Proceedings of the 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Minneapolis, MN, USA."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"460","DOI":"10.1016\/j.medengphy.2005.07.017","article-title":"Accuracy, reliability, and validity of a spatiotemporal gait analysis system","volume":"28","author":"Barker","year":"2006","journal-title":"Med Eng. Phys."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Winter, D.A. (2009). Biomechanics and Motor Control of Human Movement, John Wiley & Sons.","DOI":"10.1002\/9780470549148"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"657","DOI":"10.1016\/j.gaitpost.2012.06.017","article-title":"Gait phase detection and discrimination between walking\u2013jogging activities using hidden Markov models applied to foot motion data from a gyroscope","volume":"36","author":"Mannini","year":"2012","journal-title":"Gait Posture"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"16212","DOI":"10.3390\/s140916212","article-title":"A novel HMM distributed classifier for the detection of gait phases by means of a wearable inertial sensor network","volume":"14","author":"Taborri","year":"2014","journal-title":"Sensors"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"961","DOI":"10.1016\/j.mechatronics.2011.03.003","article-title":"Gait phase analysis based on a Hidden Markov Model","volume":"21","author":"Bae","year":"2011","journal-title":"Mechatronics"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Mannini, A., Trojaniello, D., Cereatti, A., and Sabatini, A.M. (2016). A machine learning framework for gait classification using inertial sensors: Application to elderly, post-stroke and huntington\u2019s disease patients. Sensors, 16.","DOI":"10.3390\/s16010134"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1109\/JBHI.2016.2636456","article-title":"Sensor-based gait parameter extraction with deep convolutional neural networks","volume":"21","author":"Hannink","year":"2016","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Lin, P.H., Shih, C.L., Wong, D.P., and Chou, P.H. (2021, January 19\u201321). Gait Parameters Analysis Based on Leg-and-shoe-mounted IMU and Deep Learning. Proceedings of the 2021 International Symposium on VLSI Design, Automation and Test (VLSI-DAT), Hsinchu, Taiwan.","DOI":"10.1109\/VLSI-DAT52063.2021.9427325"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"543","DOI":"10.2106\/00004623-195335030-00003","article-title":"The major determinants in normal and pathological gait","volume":"35","author":"Inman","year":"1953","journal-title":"JBJS"},{"key":"ref_24","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_25","doi-asserted-by":"crossref","unstructured":"De Ridder, R., Lebleu, J., Willems, T., De Blaiser, C., Detrembleur, C., and Roosen, P. (2019). Concurrent validity of a commercial wireless trunk triaxial accelerometer system for gait analysis. J. Sport Rehabil., 28.","DOI":"10.1123\/jsr.2018-0295"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"601","DOI":"10.1007\/s12603-012-0084-2","article-title":"A simple frailty questionnaire (FRAIL) predicts outcomes in middle aged African Americans","volume":"16","author":"Morley","year":"2012","journal-title":"J. Nutr. Health Aging"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1016\/0022-3956(75)90026-6","article-title":"\u201cMini-mental state\u201d: A practical method for grading the cognitive state of patients for the clinician","volume":"12","author":"Folstein","year":"1975","journal-title":"J. Psychiatr. Res."},{"key":"ref_28","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_29","doi-asserted-by":"crossref","first-page":"502","DOI":"10.1016\/j.gaitpost.2005.12.017","article-title":"Gait event detection using linear accelerometers or angular velocity transducers in able-bodied and spinal-cord injured individuals","volume":"24","author":"Jasiewicz","year":"2006","journal-title":"Gait Posture"},{"key":"ref_30","unstructured":"Whittle, M.W. (2014). Gait Analysis: An Introduction, Butterworth-Heinemann."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Cho, K., Van Merri\u00ebnboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., and Bengio, Y. (2014). Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv.","DOI":"10.3115\/v1\/D14-1179"},{"key":"ref_33","unstructured":"Bahdanau, D., Cho, K., and Bengio, Y. (2014). Neural machine translation by jointly learning to align and translate. arXiv."},{"key":"ref_34","unstructured":"Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., and Isard, M. (2016, January 2\u20134). Tensorflow: A system for large-scale machine learning. Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 16), Savannah, GA, USA."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Graves, A. (2012). Supervised sequence labelling. Supervised Sequence Labelling with Recurrent Neural Networks, Springer.","DOI":"10.1007\/978-3-642-24797-2"},{"key":"ref_36","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, \u0141., and Polosukhin, I. (2017, January 4\u20139). Attention is all you need. Proceedings of the Advances in Neural Information Processing Systems, Long Beach, CA, USA."},{"key":"ref_37","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_38","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1016\/j.gaitpost.2020.06.019","article-title":"Automated gait event detection for a variety of locomotion tasks using a novel gyroscope-based algorithm","volume":"81","author":"Fadillioglu","year":"2020","journal-title":"Gait Posture"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Yu, Z., Zhao, J., Zhou, X., Liu, K., and Yan, Y. (2021, January 2\u20134). Gait Phase Detection Based on a Foot-Mounted Inertial Sensor Using Long Short-Term Memory Enhanced by Hidden Markov Model. Proceedings of the 2021 26th International Conference on Automation and Computing (ICAC), Portsmouth, UK.","DOI":"10.23919\/ICAC50006.2021.9594161"},{"key":"ref_40","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_41","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_42","doi-asserted-by":"crossref","first-page":"933","DOI":"10.1109\/TBME.2015.2480296","article-title":"Step detection and parameterization for gait assessment using a single waist-worn accelerometer","volume":"63","author":"Soaz","year":"2015","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"994","DOI":"10.3389\/fneur.2020.00994","article-title":"Surface electromyography applied to gait analysis: How to improve its impact in clinics?","volume":"11","author":"Agostini","year":"2020","journal-title":"Front. Neurol."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Morbidoni, C., Cucchiarelli, A., Fioretti, S., and Di Nardo, F. (2019). A deep learning approach to EMG-based classification of gait phases during level ground walking. Electronics, 8.","DOI":"10.3390\/electronics8080894"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"334","DOI":"10.1016\/j.bspc.2018.08.030","article-title":"Walking gait event detection based on electromyography signals using artificial neural network","volume":"47","author":"Nazmi","year":"2019","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_46","unstructured":"Perry, J., and Burnfield, J.M. (2010). Gait Analysis\u2014Normal and Pathological Function, Slack. [2nd ed.]."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/21\/8226\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,6]],"date-time":"2025-01-06T04:03:19Z","timestamp":1736136199000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/21\/8226"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,27]]},"references-count":46,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2022,11]]}},"alternative-id":["s22218226"],"URL":"https:\/\/doi.org\/10.3390\/s22218226","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2022,10,27]]}}}