Center-of-pressure based postural sway measures: Reliability and ability to distinguish between age, fear of falling and fall history

Qiu, Hai; Xiong, Shuping

doi:10.1016/j.ergon.2015.02.004

Cited by 75 publications

(75 citation statements)

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“…Reasons proposed for this included the heterogeneity of the elderly population who may have various disorders and/or who may also have a stability deficit related to a specific component or combination of components of the postural control system (23,24). It was also stated how a significant number of static force-plate measures have been found to be capable of differentiating fallers vs. non-fallers, demonstrating the potential of some of these measures (17,18). Nonetheless studies combining stabilometry measures as part of a classification model have focused mostly on dynamic measures while thorough feature selection protocol have scarcely been investigated.…”

Section: Figure 3: Safe (Vertical Black Line Indicates Optimal Featurmentioning

confidence: 99%

“…The use of a force-plate has become common place in stabilometry as highly informative and reliable signals can be generated (16). Measures of COP derived from a force-plate for example are capable of differentiating subjects in terms of age-group and between fallers & non-fallers (17,18). Stimulating the postural control system by creating challenging conditions while on a force-platform such as with compliant surfaces, narrowing-stance, closing eyes etc.…”

Section: Introductionmentioning

confidence: 99%

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Feature selection for the classification of fall-risk in older subjects: a combinational approach using static force-plate measures

Reilly¹

2019

Preprint

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Introduction: Feature selection prevents over-fitting in predictive models. This study aimed to present an effective feature selection method that leads to a reliable classification of fall-risk in older subjects using static force-platform data across four conditions only.Method: 528 features were generated from a publicly available dataset of force-plate signals from 45 low-risk and 28 high-risk subjects. Subjects were classified as high-or low-risk if they recorded ≥1 falls in the prior 12 months and/or were rated as high-risk on the FES. The feature selection protocol included SVM-RFE, GA and ReliefF and finally SAFE. Several machine-learning models were then used to evaluate classification performance.Results: 67 features were identified after the three-fold process which was further reduced to 18 features after SAFE. The MLP achieved the highest average classification accuracy of 80%. All classification models evaluating this final subset displayed high variance across all performance metrics, especially in terms of sensitivity to high-risk subjects.Interpretation: An optimal feature set of static force-plate measures was insufficient in creating a reliable classifier of fall-risk. This was due potentially to the limited information about fall-risk that could be provided by such measures leading to under-fitting/over-fitting being unavoidable and appeared to be centered around an insensitivity to high-risk subjects.Conclusion: Static stability measures have shown some usability in fall-risk classification however feature sets limited to such measures are inadequately sensitive to high-risk subjects. The utilized feature selection methods demonstrated their ability to identify relevant stability measures and could be used successfully on dynamic measures. Feature selection for fall-risk classification

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Section: Figure 3: Safe (Vertical Black Line Indicates Optimal Featurmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Feature selection for the classification of fall-risk in older subjects: a combinational approach using static force-plate measures

Reilly¹

2019

Preprint

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Section: Measures Of Swaymentioning

confidence: 99%

“…In the last three decades exploration of the additional effects of ageing on balance control and falls incidence has added further to this volume of information (Alhanti, Bruder, Creese, Gregory, & Newton, 1997;Berger, Buisson, Chuzel, & Rougier, 2005b;Bryant, Trew, Bruce, Kuisma, & Smith, 2005;Fukuda, 2012;Horak, 2010;Murray, Seireg, & Sepic, 1975;Nitz, Stock, & Khan, 2013;Peters, McKeown, Carpenter, & Inglis, 2016;Qiu & Xiong, 2015;ZettelWatson, Suen, Wehbe, Rutledge, & Cherry, 2017). For clinicians working with older clients, accessing and integrating this array of information is both time-consuming and, at times, confusing.…”

Section: Introductionmentioning

confidence: 99%

“…These measures of sway are known as summary measures and have been used by researchers to gain insight into characteristics of balance in both steady state and dynamic conditions. Reliability has been demonstrated for sway area Benvenuti et al, 1999;Qiu & Xiong, 2015), sway length Qiu & Xiong, 2015) and sway velocity (Benvenuti, et al, 1999;) parameters.Stance tests in which the participant adopts a fixed stance position (for example, standing with the feet apart or together and with the eyes either open or closed) may be referred to as static stance 17 , quiet stance . Dynamic tests require the systems to respond to unexpected stimuli, that is, there must be reactive strategies, such as taking a step, to maintain a position of balance.…”

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confidence: 99%

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The development of a novel method of recording centre of gravity location in bipedal stance in healthy adults

Boughen¹

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Standing balance in humans requires that the centre of gravity (COG) remains within boundaries of the base of support (BOS). Clinically force-plate technology (for example, the NeuroCom Balance Master® 6.0, Oregon (BM)) is used increasingly to gain objective data when assessing clients' balance. In addition to the primary outcome (sway velocity) provided by the BM in four tests of the modified Clinical Test of Sensory Interaction on Balance (mCTSIB) a representation of COG location is also provided .Currently, there is no objective method for clinicians to record COG location. The purpose of this thesis was to develop a novel method of recording COG location in bipedal stance in healthy adults.The aims were: 1, explore literature associated with COG behaviour and location in healthy adults when sensory inputs and BOS are altered; 2, develop a method for categorising COG location based on the BM results diagram and assess tester reliability for this method; 3, test the new categorical method of recording COG location for (i) between-session repeatability and, (ii) comparability between different force plate systems; 4, test whether the data obtained when applying the method of categorising COG location is sensitive to changes under the different mCTSIB conditions and age; 5, test whether this categorisation or COG location is sensitive to varying BOS test conditions of feet apart to feet together as well as single limb stance, and 6, compare the COG start location to COG mean location obtained simultaneously for the mCTSIB conditions and varying BOS conditions. A literature review incorporating the impact of ageing on sway behaviours and COG location, particularly when sensory inputs or BOS are altered, formed the basis for Study 1 which addressed aim 1. Studies 2 and 3 were a secondary analysis of data from an earlier multidisciplinary study (Longitudinal Assessment of Women (LAW), 2000) of 481 independently-mobile, communitydwelling women aged 40-80 years, assessed on the BM. A new method was developed to categorize COG location data into sectors in Study 2. This categorization method was tested for inter-and intra-rater reliability by two independent raters thus addressing aim 2. For aim 3 two pilot studies assessed the repeatability of the measure between two test sessions and the comparability of results between two force plate systems. These additional aspects of COG location have not previously been tested. In Study 3 data from the LAW study were analysed to assess whether differences in COG start location could be demonstrated under altered sensory conditions using the new method of categorization and whether these were similar to other studies reporting on the location of COG thereby addressing aim 4. Studies 4 and 5 analysed COG location in 81 healthy adults (men, N=31) aged 30-80 years collected using Kistler force-plates. A customised software programme replicated iii the BM set-up allowing for collection of additional information such as COG start and mean locations and additional BOS configu...

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Subjective and Objective Measures to Assess Postural Instability: Their Linear Correlations and Abilities to Detect Effects of Work-Related Factors

Guo

Kou

Xiong

2020

Advances in Physical, Social &Amp; Occupational Ergonomics

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Center-of-pressure based postural sway measures: Reliability and ability to distinguish between age, fear of falling and fall history

Cited by 75 publications

References 48 publications

Feature selection for the classification of fall-risk in older subjects: a combinational approach using static force-plate measures

Feature selection for the classification of fall-risk in older subjects: a combinational approach using static force-plate measures

The development of a novel method of recording centre of gravity location in bipedal stance in healthy adults

Subjective and Objective Measures to Assess Postural Instability: Their Linear Correlations and Abilities to Detect Effects of Work-Related Factors

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