Acceleration-based gait test for healthy subjects: Reliability and reference data

Senden, Rachel; Grimm, Bernd; Heyligers, Ide C.; Savelberg, Hans; Meijer, Kenneth

doi:10.1016/j.gaitpost.2009.04.008

Cited by 129 publications

(118 citation statements)

References 28 publications

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“…Actually, this structure is stable and near the COM of the body, moving parallel to it, which produces the same results for velocity, displacement and acceleration [36,37]. Ideally, these movements present symmetry with respect to the anatomical planes [68].…”

Section: Sensors Signal Analysismentioning

confidence: 96%

“…These equipments are expensive and require technically skilled personnel to deal with them. They are not applicable if there's some limitations on space, time and funding, which frequently happens in clinical practice, limiting their use as routine [11,36,37,38,39].…”

Section: Technologiesmentioning

confidence: 99%

“…force plates and optical motion systems) were developed for gait and balance assessment. Although these systems were able to detect more subtle problems with balance or gait, some restrictions were identified on their use: they are expensive, not portable, and frequently poses limitations on space, time and funding, limiting their use as routine in clinics [11,36,37,38,39].…”

Section: Recent Studies On Fall Risk Predictionmentioning

confidence: 99%

“…They can for instance be easily integrated into the clinical routine or in the patient's own environment, making measures that are representative of real life context [37,38,39].…”

Section: Using Inertial Sensors To Evaluate Gaitmentioning

confidence: 99%

“…A single triaxial accelerometer attached at the lower back of trunk (Figure 2.23) has been frequently used (refer to [69], [45], [38], [31], [37], [75], [76] and [77]), since it produces the most reliable accelerometer signals. Actually, this structure is stable and near the COM of the body, moving parallel to it, which produces the same results for velocity, displacement and acceleration [36,37].…”

Section: Sensors Signal Analysismentioning

confidence: 99%

See 4 more Smart Citations

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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Section: Sensors Signal Analysismentioning

confidence: 96%

Section: Technologiesmentioning

confidence: 99%

Section: Recent Studies On Fall Risk Predictionmentioning

confidence: 99%

“…They can for instance be easily integrated into the clinical routine or in the patient's own environment, making measures that are representative of real life context [37,38,39].…”

Section: Using Inertial Sensors To Evaluate Gaitmentioning

confidence: 99%

Section: Sensors Signal Analysismentioning

confidence: 99%

See 3 more Smart Citations

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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Single and Multiple Sensor Networking for Gait Analysis

2020

Body Sensor Networking, Design and Algorithms

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Entropy analysis of tri‐axial leg acceleration signal waveforms for measurement of decrease of physiological variability in human gait

Tochigi

Segal

Vaseenon

et al. 2011

Journal Orthopaedic Research

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Disease-related and senescent decrease of physiological variability in biological time-series outputs (e.g., heart rate) has drawn increasing attention as a potential new type of biomarker. In this paradigm, measurement of variability in periodic motion may enable quantitative evaluation of functional limitation in people with musculoskeletal disorders. A novel technique to measure variability of leg motion patterns during level walking was used to study 52 adults with symptomatic knee osteoarthritis (OA), and 57 asymptomatic control subjects over a wide range of age (20 to 79 years). The hypothesis was that cycle-to-cycle variability in leg motion patterns, indexed by tri-axial acceleration signal entropy, would be lower in those with greater age or with knee symptoms. Leg motions were assessed using portable inertial monitors attached bilaterally just above each ankle. The tri-axial acceleration data were analyzed using a non-linear variability measurement tool designated as Sample Entropy (SampEn). SampEn data for asymptomatic subjects exhibited a significant negative correlation (r = -0.287, p = 0.0306) with greater age. OA subjects had significantly lower SampEn values (p = 0.0002) than did age-matched asymptomatic subjects who walked at equivalent velocity. This approach holds promise as a basis for valid, inexpensive, and convenient objective evaluation of limitations in human gait function.

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Acceleration-based gait test for healthy subjects: Reliability and reference data

Cited by 129 publications

References 28 publications

Phone Based Fall Risk Prediction

Phone Based Fall Risk Prediction

Single and Multiple Sensor Networking for Gait Analysis

Entropy analysis of tri‐axial leg acceleration signal waveforms for measurement of decrease of physiological variability in human gait

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