A review of center of pressure (COP) variables to quantify standing balance in elderly people: Algorithms and open‐access code
Postural control is often quantified by recording the trajectory of the center of pressure (COP)—also called stabilogram—during human quiet standing. This quantification has many important applications, such as the early detection of balance degradation to prevent falls, a crucial task whose relevan...
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| Published in | Physiological reports Vol. 9; no. 22; pp. e15067 - n/a |
|---|---|
| Main Authors | , , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
John Wiley & Sons, Inc
01.11.2021
Wiley John Wiley and Sons Inc |
| Subjects | |
| Online Access | Get full text |
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| Abstract | Postural control is often quantified by recording the trajectory of the center of pressure (COP)—also called stabilogram—during human quiet standing. This quantification has many important applications, such as the early detection of balance degradation to prevent falls, a crucial task whose relevance increases with the aging of the population. Due to the complexity of the quantification process, the analyses of sway patterns have been performed empirically using a number of variables, such as ellipse confidence area or mean velocity. This study reviews and compares a wide range of state‐of‐the‐art variables that are used to assess the risk of fall in elderly from a stabilogram. When appropriate, we discuss the hypothesis and mathematical assumptions that underlie these variables, and we propose a reproducible method to compute each of them. Additionally, we provide a statistical description of their behavior on two datasets recorded in two elderly populations and with different protocols, to hint at typical values of these variables. First, the balance of 133 elderly individuals, including 32 fallers, was measured on a relatively inexpensive, portable force platform (Wii Balance Board, Nintendo) with a 25‐s open‐eyes protocol. Second, the recordings of 76 elderly individuals, from an open access database commonly used to test static balance analyses, were used to compute the values of the variables on 60‐s eyes‐open recordings with a research laboratory standard force platform.
The lack of description of calculation methods for the variables extracted from the stabilogram limits reproducibility and comparison between studies. We propose an explicit corpus of postural variables with their values computed on two databases. |
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| AbstractList | Postural control is often quantified by recording the trajectory of the center of pressure (COP)—also called stabilogram—during human quiet standing. This quantification has many important applications, such as the early detection of balance degradation to prevent falls, a crucial task whose relevance increases with the aging of the population. Due to the complexity of the quantification process, the analyses of sway patterns have been performed empirically using a number of variables, such as ellipse confidence area or mean velocity. This study reviews and compares a wide range of state‐of‐the‐art variables that are used to assess the risk of fall in elderly from a stabilogram. When appropriate, we discuss the hypothesis and mathematical assumptions that underlie these variables, and we propose a reproducible method to compute each of them. Additionally, we provide a statistical description of their behavior on two datasets recorded in two elderly populations and with different protocols, to hint at typical values of these variables. First, the balance of 133 elderly individuals, including 32 fallers, was measured on a relatively inexpensive, portable force platform (Wii Balance Board, Nintendo) with a 25‐s open‐eyes protocol. Second, the recordings of 76 elderly individuals, from an open access database commonly used to test static balance analyses, were used to compute the values of the variables on 60‐s eyes‐open recordings with a research laboratory standard force platform. Postural control is often quantified by recording the trajectory of the center of pressure (COP)-also called stabilogram-during human quiet standing. This quantification has many important applications, such as the early detection of balance degradation to prevent falls, a crucial task whose relevance increases with the ageing of the population. Due to the complexity of the quantification process, the analyses of sway patterns have been performed empirically using a number of variables, such as ellipse confidence area or mean velocity. This study reviews and compares a wide range of state-of-the-art variables that are used to assess the risk of fall in elderly from a stabilogram. When appropriate, we discuss the hypothesis and mathematical assumptions that underlie these variables, and we propose a reproducible method to compute each of them. Additionally, we provide a statistical description of their behavior on two datasets recorded in two elderly populations and with different protocols, to hint at typical values of these variables. First, the balance of 133 elderly individuals, including 32 fallers, was measured on a relatively inexpensive, portable force platform (Wii Balance Board, Nintendo) with a 25-seconds open-eyes protocol. Secondly, the recordings of 76 elderly individuals, from an open access database commonly used to test static balance analyses, were used to compute the values of the variables on 60-seconds, eyes-open recordings with a research laboratory standard force platform. Postural control is often quantified by recording the trajectory of the center of pressure (COP)-also called stabilogram-during human quiet standing. This quantification has many important applications, such as the early detection of balance degradation to prevent falls, a crucial task whose relevance increases with the aging of the population. Due to the complexity of the quantification process, the analyses of sway patterns have been performed empirically using a number of variables, such as ellipse confidence area or mean velocity. This study reviews and compares a wide range of state-of-the-art variables that are used to assess the risk of fall in elderly from a stabilogram. When appropriate, we discuss the hypothesis and mathematical assumptions that underlie these variables, and we propose a reproducible method to compute each of them. Additionally, we provide a statistical description of their behavior on two datasets recorded in two elderly populations and with different protocols, to hint at typical values of these variables. First, the balance of 133 elderly individuals, including 32 fallers, was measured on a relatively inexpensive, portable force platform (Wii Balance Board, Nintendo) with a 25-s open-eyes protocol. Second, the recordings of 76 elderly individuals, from an open access database commonly used to test static balance analyses, were used to compute the values of the variables on 60-s eyes-open recordings with a research laboratory standard force platform.Postural control is often quantified by recording the trajectory of the center of pressure (COP)-also called stabilogram-during human quiet standing. This quantification has many important applications, such as the early detection of balance degradation to prevent falls, a crucial task whose relevance increases with the aging of the population. Due to the complexity of the quantification process, the analyses of sway patterns have been performed empirically using a number of variables, such as ellipse confidence area or mean velocity. This study reviews and compares a wide range of state-of-the-art variables that are used to assess the risk of fall in elderly from a stabilogram. When appropriate, we discuss the hypothesis and mathematical assumptions that underlie these variables, and we propose a reproducible method to compute each of them. Additionally, we provide a statistical description of their behavior on two datasets recorded in two elderly populations and with different protocols, to hint at typical values of these variables. First, the balance of 133 elderly individuals, including 32 fallers, was measured on a relatively inexpensive, portable force platform (Wii Balance Board, Nintendo) with a 25-s open-eyes protocol. Second, the recordings of 76 elderly individuals, from an open access database commonly used to test static balance analyses, were used to compute the values of the variables on 60-s eyes-open recordings with a research laboratory standard force platform. Abstract Postural control is often quantified by recording the trajectory of the center of pressure (COP)—also called stabilogram—during human quiet standing. This quantification has many important applications, such as the early detection of balance degradation to prevent falls, a crucial task whose relevance increases with the aging of the population. Due to the complexity of the quantification process, the analyses of sway patterns have been performed empirically using a number of variables, such as ellipse confidence area or mean velocity. This study reviews and compares a wide range of state‐of‐the‐art variables that are used to assess the risk of fall in elderly from a stabilogram. When appropriate, we discuss the hypothesis and mathematical assumptions that underlie these variables, and we propose a reproducible method to compute each of them. Additionally, we provide a statistical description of their behavior on two datasets recorded in two elderly populations and with different protocols, to hint at typical values of these variables. First, the balance of 133 elderly individuals, including 32 fallers, was measured on a relatively inexpensive, portable force platform (Wii Balance Board, Nintendo) with a 25‐s open‐eyes protocol. Second, the recordings of 76 elderly individuals, from an open access database commonly used to test static balance analyses, were used to compute the values of the variables on 60‐s eyes‐open recordings with a research laboratory standard force platform. Postural control is often quantified by recording the trajectory of the center of pressure (COP)—also called stabilogram—during human quiet standing. This quantification has many important applications, such as the early detection of balance degradation to prevent falls, a crucial task whose relevance increases with the aging of the population. Due to the complexity of the quantification process, the analyses of sway patterns have been performed empirically using a number of variables, such as ellipse confidence area or mean velocity. This study reviews and compares a wide range of state‐of‐the‐art variables that are used to assess the risk of fall in elderly from a stabilogram. When appropriate, we discuss the hypothesis and mathematical assumptions that underlie these variables, and we propose a reproducible method to compute each of them. Additionally, we provide a statistical description of their behavior on two datasets recorded in two elderly populations and with different protocols, to hint at typical values of these variables. First, the balance of 133 elderly individuals, including 32 fallers, was measured on a relatively inexpensive, portable force platform (Wii Balance Board, Nintendo) with a 25‐s open‐eyes protocol. Second, the recordings of 76 elderly individuals, from an open access database commonly used to test static balance analyses, were used to compute the values of the variables on 60‐s eyes‐open recordings with a research laboratory standard force platform. The lack of description of calculation methods for the variables extracted from the stabilogram limits reproducibility and comparison between studies. We propose an explicit corpus of postural variables with their values computed on two databases. |
| Author | Quijoux, Flavien Chairi, Ikram Vayatis, Nicolas Bargiotas, Ioannis Oudre, Laurent Vidal, Pierre‐Paul Audiffren, Julien Bertin‐Hugault, François Yelnik, Alain Ricard, Damien Nicolaï, Alice Buffat, Stéphane |
| AuthorAffiliation | 1 Centre Borelli UMR 9010/Université Paris‐Saclay ENS Paris‐Saclay CNRS SSA, Inserm Université de Paris Paris France 2 ORPEA Group Puteaux France 4 Service de Neurologie de l’Hôpital d’Instruction des Armées de Percy SSA Clamart France 5 Ecole du Val‐de‐Grâce Ecole de Santé des Armées Paris France 3 Groupe MSDA Université Mohammed VI Polytechnique Benguerir Maroc 6 PRM Department GH Lariboisière F. Widal AP‐HP Université de Paris UMR 8257 Paris France 9 Department of Neuroscience University of Fribourg Fribourg Switzerland 7 Institute of Information and Control Hangzhou Dianzi University Zhejiang China 8 Laboratoire d’accidentologie de biomécanique et du comportement des conducteurs GIE Psa Renault Groupes Nanterre France |
| AuthorAffiliation_xml | – name: 6 PRM Department GH Lariboisière F. Widal AP‐HP Université de Paris UMR 8257 Paris France – name: 9 Department of Neuroscience University of Fribourg Fribourg Switzerland – name: 1 Centre Borelli UMR 9010/Université Paris‐Saclay ENS Paris‐Saclay CNRS SSA, Inserm Université de Paris Paris France – name: 5 Ecole du Val‐de‐Grâce Ecole de Santé des Armées Paris France – name: 7 Institute of Information and Control Hangzhou Dianzi University Zhejiang China – name: 3 Groupe MSDA Université Mohammed VI Polytechnique Benguerir Maroc – name: 8 Laboratoire d’accidentologie de biomécanique et du comportement des conducteurs GIE Psa Renault Groupes Nanterre France – name: 2 ORPEA Group Puteaux France – name: 4 Service de Neurologie de l’Hôpital d’Instruction des Armées de Percy SSA Clamart France |
| Author_xml | – sequence: 1 givenname: Flavien orcidid: 0000-0002-9842-2361 surname: Quijoux fullname: Quijoux, Flavien email: f.quijoux@orpea.fr organization: ORPEA Group – sequence: 2 givenname: Alice surname: Nicolaï fullname: Nicolaï, Alice organization: Université de Paris – sequence: 3 givenname: Ikram surname: Chairi fullname: Chairi, Ikram organization: Université Mohammed VI Polytechnique – sequence: 4 givenname: Ioannis surname: Bargiotas fullname: Bargiotas, Ioannis organization: Université de Paris – sequence: 5 givenname: Damien surname: Ricard fullname: Ricard, Damien organization: Ecole de Santé des Armées – sequence: 6 givenname: Alain surname: Yelnik fullname: Yelnik, Alain organization: UMR 8257 – sequence: 7 givenname: Laurent surname: Oudre fullname: Oudre, Laurent organization: Université de Paris – sequence: 8 givenname: François surname: Bertin‐Hugault fullname: Bertin‐Hugault, François organization: ORPEA Group – sequence: 9 givenname: Pierre‐Paul surname: Vidal fullname: Vidal, Pierre‐Paul organization: Hangzhou Dianzi University – sequence: 10 givenname: Nicolas surname: Vayatis fullname: Vayatis, Nicolas organization: Université de Paris – sequence: 11 givenname: Stéphane surname: Buffat fullname: Buffat, Stéphane organization: GIE Psa Renault Groupes – sequence: 12 givenname: Julien surname: Audiffren fullname: Audiffren, Julien organization: University of Fribourg |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/34826208$$D View this record in MEDLINE/PubMed https://hal.science/hal-03664681$$DView record in HAL |
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| Publisher | John Wiley & Sons, Inc Wiley John Wiley and Sons Inc |
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| Snippet | Postural control is often quantified by recording the trajectory of the center of pressure (COP)—also called stabilogram—during human quiet standing. This... Postural control is often quantified by recording the trajectory of the center of pressure (COP)-also called stabilogram-during human quiet standing. This... Abstract Postural control is often quantified by recording the trajectory of the center of pressure (COP)—also called stabilogram—during human quiet standing.... |
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| SubjectTerms | Accidental Falls Aged Aging Algorithms Applications Balance Bioengineering Biomechanical Phenomena Biomechanics center of pressure Databases, Factual Datasets elderly Engineering Sciences Falls Geriatrics Geriatry and gerontology Human health and pathology Humans Life Sciences Mechanics Older people Physiology Postural Balance postural control quiet standing Review Reviews Risk Assessment Statistics Systematic review Tissues and Organs Variables |
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| Title | A review of center of pressure (COP) variables to quantify standing balance in elderly people: Algorithms and open‐access code |
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