State of the Field of waist-mounted sensor algorithm for gait events detection: A scoping review
•Fifteen algorithms are available for gait events detection.•Only a few algorithms were validated in patients with neurological diseases.•Lower gait speed deteriorated the accuracy of gait events estimation. A waist-mounted sensor is an attractive option for detecting initial and end of foot contact...
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| Veröffentlicht in: | Gait & posture Jg. 79; H. NA; S. 152 - 161 |
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01.06.2020
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| Abstract | •Fifteen algorithms are available for gait events detection.•Only a few algorithms were validated in patients with neurological diseases.•Lower gait speed deteriorated the accuracy of gait events estimation.
A waist-mounted sensor is an attractive option for detecting initial and end of foot contacts during gait in a clinical setting without disturbing the subject’s natural gait.
To examine the current state of the field regarding waist-mounted sensor algorithms for gait event detection during locomotion in adults.
A scoping review design was used to search peer-reviewed literature or conference proceedings published through October 2018 for algorithms for gait event detection. We analyzed data from the studies in a descriptive manner.
In total, 588 potentially relevant articles were selected, of which 14 (171 participants, mean age: 44.0 years) met the inclusion criteria. We identified 15 algorithms developed using biomechanical theories including the inverted pendulum model that represents gait during level walking. Most algorithms estimated gait events using triaxial acceleration data with an absolute error of approximately 50–100 ms in healthy adults. However, there was a large amount of inter-trial heterogeneity, and only a few algorithms were validated in patients with neurological diseases. Lower gait speed reduced the accuracy of gait event estimation.
There was no algorithm that showed outstanding performance in the estimation of gait events during level walking using the waist-mounted sensor. More comparisons of all available algorithms with an established reference standard for one data-set are needed to identify the best algorithms. As patients with pathological conditions display altered trunk acceleration and slower gait speeds, the development of an algorithm that does not rely on particular signal characteristics and is robust for a wide range of gait speeds is needed before a specific algorithm can be recommended as a valid strategy for clinical practice. |
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| AbstractList | •Fifteen algorithms are available for gait events detection.•Only a few algorithms were validated in patients with neurological diseases.•Lower gait speed deteriorated the accuracy of gait events estimation.
A waist-mounted sensor is an attractive option for detecting initial and end of foot contacts during gait in a clinical setting without disturbing the subject’s natural gait.
To examine the current state of the field regarding waist-mounted sensor algorithms for gait event detection during locomotion in adults.
A scoping review design was used to search peer-reviewed literature or conference proceedings published through October 2018 for algorithms for gait event detection. We analyzed data from the studies in a descriptive manner.
In total, 588 potentially relevant articles were selected, of which 14 (171 participants, mean age: 44.0 years) met the inclusion criteria. We identified 15 algorithms developed using biomechanical theories including the inverted pendulum model that represents gait during level walking. Most algorithms estimated gait events using triaxial acceleration data with an absolute error of approximately 50–100 ms in healthy adults. However, there was a large amount of inter-trial heterogeneity, and only a few algorithms were validated in patients with neurological diseases. Lower gait speed reduced the accuracy of gait event estimation.
There was no algorithm that showed outstanding performance in the estimation of gait events during level walking using the waist-mounted sensor. More comparisons of all available algorithms with an established reference standard for one data-set are needed to identify the best algorithms. As patients with pathological conditions display altered trunk acceleration and slower gait speeds, the development of an algorithm that does not rely on particular signal characteristics and is robust for a wide range of gait speeds is needed before a specific algorithm can be recommended as a valid strategy for clinical practice. Highlights: Fifteen algorithms are available for gait events detection. Only a few algorithms were validated in patients with neurological diseases. Lower gait speed deteriorated the accuracy of gait events estimation. Abstract: Background: A waist-mounted sensor is an attractive option for detecting initial and end of foot contacts during gait in a clinical setting without disturbing the subject's natural gait. Research question To examine the current state of the field regarding waist-mounted sensor algorithms for gait event detection during locomotion in adults. Methods: A scoping review design was used to search peer-reviewed literature or conference proceedings published through October 2018 for algorithms for gait event detection. We analyzed data from the studies in a descriptive manner. Results: In total, 588 potentially relevant articles were selected, of which 14 (171 participants, mean age: 44.0 years) met the inclusion criteria. We identified 15 algorithms developed using biomechanical theories including the inverted pendulum model that represents gait during level walking. Most algorithms estimated gait events using triaxial acceleration data with an absolute error of approximately 50-100 ms in healthy adults. However, there was a large amount of inter-trial heterogeneity, and only a few algorithms were validated in patients with neurological diseases. Lower gait speed reduced the accuracy of gait event estimation. Significance There was no algorithm that showed outstanding performance in the estimation of gait events during level walking using the waist-mounted sensor. More comparisons of all available algorithms with an established reference standard for one data-set are needed to identify the best algorithms. As patients with pathological conditions display altered trunk acceleration and slower gait speeds, the development of an algorithm that does not rely on particular signal characteristics and is robust for a wide range of gait speeds is needed before a specific algorithm can be recommended as a valid strategy for clinical practice. A waist-mounted sensor is an attractive option for detecting initial and end of foot contacts during gait in a clinical setting without disturbing the subject's natural gait.BACKGROUNDA waist-mounted sensor is an attractive option for detecting initial and end of foot contacts during gait in a clinical setting without disturbing the subject's natural gait.To examine the current state of the field regarding waist-mounted sensor algorithms for gait event detection during locomotion in adults.RESEARCH QUESTIONTo examine the current state of the field regarding waist-mounted sensor algorithms for gait event detection during locomotion in adults.A scoping review design was used to search peer-reviewed literature or conference proceedings published through October 2018 for algorithms for gait event detection. We analyzed data from the studies in a descriptive manner.METHODSA scoping review design was used to search peer-reviewed literature or conference proceedings published through October 2018 for algorithms for gait event detection. We analyzed data from the studies in a descriptive manner.In total, 588 potentially relevant articles were selected, of which 14 (171 participants, mean age: 44.0 years) met the inclusion criteria. We identified 15 algorithms developed using biomechanical theories including the inverted pendulum model that represents gait during level walking. Most algorithms estimated gait events using triaxial acceleration data with an absolute error of approximately 50-100 ms in healthy adults. However, there was a large amount of inter-trial heterogeneity, and only a few algorithms were validated in patients with neurological diseases. Lower gait speed reduced the accuracy of gait event estimation.RESULTSIn total, 588 potentially relevant articles were selected, of which 14 (171 participants, mean age: 44.0 years) met the inclusion criteria. We identified 15 algorithms developed using biomechanical theories including the inverted pendulum model that represents gait during level walking. Most algorithms estimated gait events using triaxial acceleration data with an absolute error of approximately 50-100 ms in healthy adults. However, there was a large amount of inter-trial heterogeneity, and only a few algorithms were validated in patients with neurological diseases. Lower gait speed reduced the accuracy of gait event estimation.There was no algorithm that showed outstanding performance in the estimation of gait events during level walking using the waist-mounted sensor. More comparisons of all available algorithms with an established reference standard for one data-set are needed to identify the best algorithms. As patients with pathological conditions display altered trunk acceleration and slower gait speeds, the development of an algorithm that does not rely on particular signal characteristics and is robust for a wide range of gait speeds is needed before a specific algorithm can be recommended as a valid strategy for clinical practice.SIGNIFICANCEThere was no algorithm that showed outstanding performance in the estimation of gait events during level walking using the waist-mounted sensor. More comparisons of all available algorithms with an established reference standard for one data-set are needed to identify the best algorithms. As patients with pathological conditions display altered trunk acceleration and slower gait speeds, the development of an algorithm that does not rely on particular signal characteristics and is robust for a wide range of gait speeds is needed before a specific algorithm can be recommended as a valid strategy for clinical practice. A waist-mounted sensor is an attractive option for detecting initial and end of foot contacts during gait in a clinical setting without disturbing the subject's natural gait. To examine the current state of the field regarding waist-mounted sensor algorithms for gait event detection during locomotion in adults. A scoping review design was used to search peer-reviewed literature or conference proceedings published through October 2018 for algorithms for gait event detection. We analyzed data from the studies in a descriptive manner. In total, 588 potentially relevant articles were selected, of which 14 (171 participants, mean age: 44.0 years) met the inclusion criteria. We identified 15 algorithms developed using biomechanical theories including the inverted pendulum model that represents gait during level walking. Most algorithms estimated gait events using triaxial acceleration data with an absolute error of approximately 50-100 ms in healthy adults. However, there was a large amount of inter-trial heterogeneity, and only a few algorithms were validated in patients with neurological diseases. Lower gait speed reduced the accuracy of gait event estimation. There was no algorithm that showed outstanding performance in the estimation of gait events during level walking using the waist-mounted sensor. More comparisons of all available algorithms with an established reference standard for one data-set are needed to identify the best algorithms. As patients with pathological conditions display altered trunk acceleration and slower gait speeds, the development of an algorithm that does not rely on particular signal characteristics and is robust for a wide range of gait speeds is needed before a specific algorithm can be recommended as a valid strategy for clinical practice. |
| Author | Iijima, Hirotaka Takahashi, Masaki |
| Author_xml | – sequence: 1 givenname: Hirotaka surname: Iijima fullname: Iijima, Hirotaka email: iijima.hirotaka.4m@yt.sd.keio.ac.jp organization: Department of System Design Engineering, Faculty of Science and Technology, Keio University, Yokohama, Japan – sequence: 2 givenname: Masaki surname: Takahashi fullname: Takahashi, Masaki email: takahashi@sd.keio.ac.jp organization: Department of System Design Engineering, Faculty of Science and Technology, Keio University, Yokohama, Japan |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/32408039$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1109/TBME.2015.2480296 10.7326/M18-0850 10.1016/j.gaitpost.2009.02.017 10.1016/j.medengphy.2011.04.009 10.1016/j.gaitpost.2012.02.019 10.1016/j.jclinepi.2014.03.013 10.3390/s150922089 10.1016/S1350-4533(03)00116-4 10.3390/brainsci9020034 10.1001/jama.283.15.2008 10.1109/IEMBS.2011.6090263 10.1186/s13643-016-0204-x 10.1016/j.medengphy.2010.03.007 10.1016/j.gaitpost.2016.08.012 10.1016/j.gaitpost.2009.11.014 10.7326/0003-4819-138-1-200301070-00012-w1 10.1016/j.gaitpost.2015.05.020 10.1016/S0021-9290(03)00233-1 10.1109/78.533717 10.1016/S0966-6362(02)00190-X 10.1016/0021-9290(94)00074-E 10.1109/TNSRE.2013.2239313 10.1016/j.jclinepi.2013.05.014 10.1186/1743-0003-9-9 10.1016/j.gaitpost.2018.08.025 10.3389/fneur.2017.00457 10.1016/S0966-6362(02)00159-5 10.1016/S0966-6362(01)00203-X 10.1016/j.cmpb.2012.02.003 10.1016/j.gaitpost.2017.06.019 10.1016/j.gaitpost.2015.06.008 |
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| Snippet | •Fifteen algorithms are available for gait events detection.•Only a few algorithms were validated in patients with neurological diseases.•Lower gait speed... A waist-mounted sensor is an attractive option for detecting initial and end of foot contacts during gait in a clinical setting without disturbing the... Highlights: Fifteen algorithms are available for gait events detection. Only a few algorithms were validated in patients with neurological diseases. Lower gait... |
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| Title | State of the Field of waist-mounted sensor algorithm for gait events detection: A scoping review |
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