A new method for evaluating motor control in gait under real-life environmental conditions. Part 1: The instrument

Moe‐Nilssen, Rolf

doi:10.1016/s0268-0033(98)00089-8

Cited by 305 publications

(220 citation statements)

References 18 publications

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“…Moe-Nilssen presented an algorithm to calculate the average tilt of an accelerometer device during walking by estimating the gravitational components in the anteriorposterior and mediolateral directions [33]. However, in the current study, the gravitational component was disabled by using a direct current filter during the measurements.…”

Section: Figurementioning

confidence: 92%

Reliability and validity of trunk accelerometry-derived performance measurements in a standardized heel-rise test in elderly subjects

Schmid

Hilfiker²,

Radlinger³

2011

JRRD

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Abstract-This cross-sectional study evaluated the intrasession reliability and concurrent validity of trunk accelerometry (with force plate measurements) for vertical ground reaction force and external mechanical power recorded during a standardized heel-rise (HR) test in 54 elderly subjects (mean +/-standard deviation age 81.2 +/-6.4 yr). Peak force as well as peak and average power revealed intraclass correlation coefficients of >0.75 and low standard errors of measurement for both the force plate-and the accelerometer-based curves. Correlation coefficients for these variables ranged from 0.95 to 0.98. The accelerometry-derived variables indicated significantly lower absolute values. Trunk accelerometry can be used as a reliable and valid tool for the quantification of the HR test in the elderly population. However, due to several limitations in the protocol, the use of this tool can currently only be recommended in a test-retest manner. Therefore, more research is needed to fully validate this tool for clinical use.

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Section: Figurementioning

confidence: 92%

Reliability and validity of trunk accelerometry-derived performance measurements in a standardized heel-rise test in elderly subjects

Schmid

Hilfiker²,

Radlinger³

2011

JRRD

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“…In accordance with previous studies [1][2][3][4][5][6] , the subjects were instructed as follows: (i) FG: walk as fast as possible without running, (ii) MD: adopt your usual walking speed, and (iii) SL: walk slowly as if, for example, waiting at a bus stop or windowshopping. Then, training was provided to enable the subjects to learn these gait velocities.…”

Section: Methodsmentioning

confidence: 99%

“…Our aim is to prepare a coste f f e c t i v e s y s t e m t h a t i s h i g h l y r e l i a b l e , reproducible, and valid in clinical practice. This evaluation tool includes an electromyograph, an accelerometer [1][2][3][4][5][6] , a goniometer, a dynamometer 7,8) a thermometer, a muscle hardness meter [9][10][11] , and a pain meter 12,13) . We are also aiming to develop a system in which these sensors are connected wirelessly to reduce the restrictions imposed by the use of connection cords.…”

Section: Introductionmentioning

confidence: 99%

Quantitative and Qualitative Evaluation of Knee Electromyograms by a Bluetooth-communication Gait Analyzer: Integration and Power Spectral Analysis of Surface Electromyograms

et al. 2010

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Abstract. [Purpose] We observed the action of muscles during gait using a Bluetooth Gait Analyzer (sampling frequency 1 kHz), by integrating electromyograms and performing power spectral analysis. Analyzer was described in the quantitative and qualitative evaluation of these muscles.[Subjects] The subjects were 15 healthy adult males.[Methods] A Bluetooth electromyography gait analyzer was used to observe the action of the vastus medialis muscle (Quad muscle) and the long head of the biceps femoris muscle (Hamstrings) during slow, medium, and fast gait. Electromyograms were subjected to integration and power spectral analysis evaluation.[Results] The integrated electromyograms of the Quad muscles and the Hamstrings increased significantly with increases in gait speed. Power spectral analysis showed the mean power frequency of the Quad muscles tended to decrease, and the high frequency component (81-250 Hz) was significantly reduced. The mean power frequency of the Hamstrings increased with increases in gait speed, but the high frequency component was significantly increased.[Conclusion] The observed tendencies might have resulted from the induction of positive action in type I muscle fibers in the Quad muscles with increased gait speed and of the muscle action of type II fibers in the Hamstrings. We suggest that, as the gait speed increases, the Quad muscles are forced to play the role of a braking muscle.

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“…Force platforms and cameras are usually used for quantitative analysis of gait, balance and postural control [34,35,36]. These equipments are expensive and require technically skilled personnel to deal with them.…”

Section: Technologiesmentioning

confidence: 99%

Phone Based Fall Risk Prediction

Guimar�ães

Teixeira

Monteiro

et al. 2012

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Falls in older people can represent serious individual and socioeconomic problems. With the increasing number of older people in the population, management of falls is becoming a challenge.It is now recognized that several risk factors for falling can be identified, and specific interventions can be designed in order to reduce those risks. Several tools were then developed and are now available to screen for fall risk. However, these tools are applied with low frequency, usually requiring the use of expensive equipments that can only be manipulated by experts. This poses some constraints on the prevention of falls, since when these techniques are applied, it can be too late.In order to improve the earlier detection of risks and the application of fall prevention strategies, new community based solutions must be developed. In this project, the mobile phone was purposed as a potential instrument to perform fall risk screening. Several strategies were then evaluated to be adapted to the phone for fall risk screening purposes.Phone's inertial sensors were used to extract some parameters of gait that were related with an increased risk of falling. Foot contacts, gait phases, step length, step duration, walking velocity and variability parameters could then be determined from sensors data. Acceptable measures of step length and walking velocity could be obtained. Additionally, other risk factors were selected to be assessed by self-report, using some clinical scales/questionnaires.Results suggest that phone can be used as a major strategy to improve fall prevention, being a great value for older persons and society.i ii ResumoUma queda num idoso pode resultar em graves problemas individuais e socioeconómicos. Com o aumento do número de idosos na população, a gestão de quedas está a tornar-se um desafio.Actualmente, é possível identificar vários factores de risco para a ocorrência de quedas bem como reduzir esses riscos, através da aplicação da aplicação de intervenções especí-ficas. Nesse sentido, há já algumas ferramentas actualmente disponíveis para avaliar o risco de queda. Estas ferramentas no entanto usadas com baixa frequência, exigindo o uso de equipamentos caros que apenas podem ser manipulados por pessoas qualificadas. Muitas vezes, estas técnicas podem já ser aplicadas tarde demais.Para que a detecção dos riscos e a aplicação de estratégias de prevenção de quedas possa ser feita mais precocemente, é necessário o desenvolvimento de novas soluções dirigidas à população. Neste projecto, foi proposto o uso do telemóvel para a detecção do risco de queda nos idosos.Os sinais dos sensores do telemóvel foram utilizados para extrair parâmetros da marcha relacionados com um risco de queda aumentado. A partir deles, os contactos do pé com o chão, as fases da marcha, o tamanho e duração dos passos, a velocidade e parâmetros que quantificam a variabilidade da marcha puderam ser determinados. Medidas aceitáveis do tamanho dos passos e da velocidade foram obtidas. Adicionalmente, outros factores de risco foram sele...

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A new method for evaluating motor control in gait under real-life environmental conditions. Part 1: The instrument

Cited by 305 publications

References 18 publications

Reliability and validity of trunk accelerometry-derived performance measurements in a standardized heel-rise test in elderly subjects

Reliability and validity of trunk accelerometry-derived performance measurements in a standardized heel-rise test in elderly subjects

Quantitative and Qualitative Evaluation of Knee Electromyograms by a Bluetooth-communication Gait Analyzer: Integration and Power Spectral Analysis of Surface Electromyograms

Phone Based Fall Risk Prediction

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