Feature Identification With a Heuristic Algorithm and an Unsupervised Machine Learning Algorithm for Prior Knowledge of Gait Events

The purpose of this study was to compare a heuristic feature identification algorithm with output from the Beta Process Auto Regressive Hidden Markov Model (BP-AR-HMM) utilizing minimally sampled (≤ 100 Hz) human locomotion data for identification of gait events prior to their occurrence. Data were...

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Published in:	IEEE transactions on neural systems and rehabilitation engineering Vol. 30; pp. 108 - 114
Main Authors:	Donahue, Seth R., Hahn, Michael E.
Format:	Journal Article
Language:	English
Published:	United States IEEE 2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Algorithms Biomechanical Phenomena Foot Gait Heuristic Heuristic algorithms Heuristic methods Heuristics Hidden Markov models Humans Inertial measurement units Inertial platforms Inertial sensing devices Integrated circuits Lead time Learning algorithms Locomotion Machine learning Machine learning algorithms Markov chains Problem solving real-world gait event detection Time series analysis Unsupervised learning Unsupervised Machine Learning Walking
ISSN:	1534-4320, 1558-0210, 1558-0210
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Abstract	The purpose of this study was to compare a heuristic feature identification algorithm with output from the Beta Process Auto Regressive Hidden Markov Model (BP-AR-HMM) utilizing minimally sampled (≤ 100 Hz) human locomotion data for identification of gait events prior to their occurrence. Data were collected from 16 participants (21-64 years) using a single gyroscopic sensor in an inertial measurement unit on the dorsum of the foot, across multiple locomotion modes, including level ground walking and running (across speeds 0.8 m s −1 - 3.0 m s −1 ), ramps and stairs. Identification of gait events, initial contact (IC) and toe off (TO) with the heuristic algorithm, was 94% across locomotion modes. The features identified prior to initial contact had a lead time of 186.32 ± 86.70 ms, while TO had a lead time of 63.96 ± 46.30 ms. The BP-AR-HMM identified features that indicated an impending IC and TO with 99% accuracy, with a lead time of 59.41 ± 54.41 ms for IC and 90.79 ± 35.51 ms for TO. These approaches are consistent in their identification of gait events and have the potential to be utilized for classification and prediction of locomotion mode.
AbstractList	The purpose of this study was to compare a heuristic feature identification algorithm with output from the Beta Process Auto Regressive Hidden Markov Model (BP-AR-HMM) utilizing minimally sampled (≤ 100 Hz) human locomotion data for identification of gait events prior to their occurrence. Data were collected from 16 participants (21-64 years) using a single gyroscopic sensor in an inertial measurement unit on the dorsum of the foot, across multiple locomotion modes, including level ground walking and running (across speeds 0.8 m s −1 - 3.0 m s −1 ), ramps and stairs. Identification of gait events, initial contact (IC) and toe off (TO) with the heuristic algorithm, was 94% across locomotion modes. The features identified prior to initial contact had a lead time of 186.32 ± 86.70 ms, while TO had a lead time of 63.96 ± 46.30 ms. The BP-AR-HMM identified features that indicated an impending IC and TO with 99% accuracy, with a lead time of 59.41 ± 54.41 ms for IC and 90.79 ± 35.51 ms for TO. These approaches are consistent in their identification of gait events and have the potential to be utilized for classification and prediction of locomotion mode. The purpose of this study was to compare a heuristic feature identification algorithm with output from the Beta Process Auto Regressive Hidden Markov Model (BP-AR-HMM) utilizing minimally sampled (≤ 100 Hz) human locomotion data for identification of gait events prior to their occurrence. Data were collected from 16 participants (21-64 years) using a single gyroscopic sensor in an inertial measurement unit on the dorsum of the foot, across multiple locomotion modes, including level ground walking and running (across speeds 0.8 m s−1 - 3.0 m s−1), ramps and stairs. Identification of gait events, initial contact (IC) and toe off (TO) with the heuristic algorithm, was 94% across locomotion modes. The features identified prior to initial contact had a lead time of 186.32 ± 86.70 ms, while TO had a lead time of 63.96 ± 46.30 ms. The BP-AR-HMM identified features that indicated an impending IC and TO with 99% accuracy, with a lead time of 59.41 ± 54.41 ms for IC and 90.79 ± 35.51 ms for TO. These approaches are consistent in their identification of gait events and have the potential to be utilized for classification and prediction of locomotion mode. The purpose of this study was to compare a heuristic feature identification algorithm with output from the Beta Process Auto Regressive Hidden Markov Model (BP-AR-HMM) utilizing minimally sampled (≤ 100 Hz) human locomotion data for identification of gait events prior to their occurrence. Data were collected from 16 participants (21-64 years) using a single gyroscopic sensor in an inertial measurement unit on the dorsum of the foot, across multiple locomotion modes, including level ground walking and running (across speeds 0.8 m s-1 - 3.0 m s-1), ramps and stairs. Identification of gait events, initial contact (IC) and toe off (TO) with the heuristic algorithm, was 94% across locomotion modes. The features identified prior to initial contact had a lead time of 186.32 ± 86.70 ms, while TO had a lead time of 63.96 ± 46.30 ms. The BP-AR-HMM identified features that indicated an impending IC and TO with 99% accuracy, with a lead time of 59.41 ± 54.41 ms for IC and 90.79 ± 35.51 ms for TO. These approaches are consistent in their identification of gait events and have the potential to be utilized for classification and prediction of locomotion mode.The purpose of this study was to compare a heuristic feature identification algorithm with output from the Beta Process Auto Regressive Hidden Markov Model (BP-AR-HMM) utilizing minimally sampled (≤ 100 Hz) human locomotion data for identification of gait events prior to their occurrence. Data were collected from 16 participants (21-64 years) using a single gyroscopic sensor in an inertial measurement unit on the dorsum of the foot, across multiple locomotion modes, including level ground walking and running (across speeds 0.8 m s-1 - 3.0 m s-1), ramps and stairs. Identification of gait events, initial contact (IC) and toe off (TO) with the heuristic algorithm, was 94% across locomotion modes. The features identified prior to initial contact had a lead time of 186.32 ± 86.70 ms, while TO had a lead time of 63.96 ± 46.30 ms. The BP-AR-HMM identified features that indicated an impending IC and TO with 99% accuracy, with a lead time of 59.41 ± 54.41 ms for IC and 90.79 ± 35.51 ms for TO. These approaches are consistent in their identification of gait events and have the potential to be utilized for classification and prediction of locomotion mode. The purpose of this study was to compare a heuristic feature identification algorithm with output from the Beta Process Auto Regressive Hidden Markov Model (BP-AR-HMM) utilizing minimally sampled (≤ 100 Hz) human locomotion data for identification of gait events prior to their occurrence. Data were collected from 16 participants (21-64 years) using a single gyroscopic sensor in an inertial measurement unit on the dorsum of the foot, across multiple locomotion modes, including level ground walking and running (across speeds 0.8 m s - 3.0 m s ), ramps and stairs. Identification of gait events, initial contact (IC) and toe off (TO) with the heuristic algorithm, was 94% across locomotion modes. The features identified prior to initial contact had a lead time of 186.32 ± 86.70 ms, while TO had a lead time of 63.96 ± 46.30 ms. The BP-AR-HMM identified features that indicated an impending IC and TO with 99% accuracy, with a lead time of 59.41 ± 54.41 ms for IC and 90.79 ± 35.51 ms for TO. These approaches are consistent in their identification of gait events and have the potential to be utilized for classification and prediction of locomotion mode.
Author	Donahue, Seth R. Hahn, Michael E.
Author_xml	– sequence: 1 givenname: Seth R. surname: Donahue fullname: Donahue, Seth R. organization: Department of Human Physiology, University of Oregon, Eugene, OR, USA – sequence: 2 givenname: Michael E. orcidid: 0000-0001-5024-700X surname: Hahn fullname: Hahn, Michael E. email: mhahn@uoregon.edu organization: Department of Human Physiology, University of Oregon, Eugene, OR, USA
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Cites_doi	10.1016/j.gaitpost.2017.06.019 10.1016/j.gaitpost.2012.06.017 10.1109/TNSRE.2018.2868094 10.1109/TSP.2010.2102756 10.1016/j.asoc.2013.07.027 10.3390/s150922089 10.3390/s140305470 10.1109/TNSRE.2016.2536278 10.1016/j.gaitpost.2016.09.023 10.1109/BIOROB.2018.8487694 10.1007/s10439-015-1407-3 10.1109/EMBC.2017.8037735 10.3390/s16010066 10.1016/j.gaitpost.2018.08.025 10.1016/j.neunet.2018.02.017 10.1109/TBME.2004.840727 10.1109/TBME.2008.2003293 10.1016/j.gaitpost.2008.12.003 10.3390/electronics8080894 10.1109/JBHI.2014.2377749 10.1109/TNSRE.2014.2337914 10.1214/14-AOAS742 10.1186/s12938-020-00803-1 10.3390/s100605683 10.1109/BHI.2014.6864429 10.3390/s17112647 10.1155/2020/4760297 10.1109/JBHI.2013.2293887 10.1109/86.867873 10.1016/j.gaitpost.2009.11.014 10.1098/rsif.2012.0980 10.1109/JSEN.2004.823671 10.1109/FUZZ-IEEE.2017.8015447 10.1093/biomet/82.4.711 10.1016/j.bspc.2018.08.030 10.1088/1741-2560/7/5/056005 10.1109/TNSRE.2013.2291907
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SubjectTerms	Algorithms Biomechanical Phenomena Foot Gait Heuristic Heuristic algorithms Heuristic methods Heuristics Hidden Markov models Humans Inertial measurement units Inertial platforms Inertial sensing devices Integrated circuits Lead time Learning algorithms Locomotion Machine learning Machine learning algorithms Markov chains Problem solving real-world gait event detection Time series analysis Unsupervised learning Unsupervised Machine Learning Walking
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Title	Feature Identification With a Heuristic Algorithm and an Unsupervised Machine Learning Algorithm for Prior Knowledge of Gait Events
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