A review of center of pressure (COP) variables to quantify standing balance in elderly people: Algorithms and open‐access code

• Published in: Physiological reports Vol. 9; no. 22; pp. e15067 - n/a
• Main Authors: Quijoux, Flavien, Nicolaï, Alice, Chairi, Ikram, Bargiotas, Ioannis, Ricard, Damien, Yelnik, Alain, Oudre, Laurent, Bertin‐Hugault, François, Vidal, Pierre‐Paul, Vayatis, Nicolas, Buffat, Stéphane, Audiffren, Julien
• Format: Journal Article
• Language: English
• Published: United States John Wiley & Sons, Inc 01.11.2021; Wiley; John Wiley and Sons Inc
• Subjects: 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; postural control; quiet standing; center of pressure; elderly; Quiet Standing; Postural Control; Elderly; Feature engineering; Center of Pressure
• ISSN: 2051-817X; 2051-817X
• DOI: 10.14814/phy2.15067

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.