Comparison and adaptation of step length and gait speed estimators from single belt worn accelerometer positioned on lateral side of the body

Sayeed, Taufique; Samà, Albert; Català, Andreu; Cabestany, Joan

doi:10.1109/wisp.2013.6657475

Cited by 17 publications

(13 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In previous study [11,14], it was shown that C, could be calculated using following equation that provides same results:…”

Section: A) Zijlstra's Methodsmentioning

confidence: 85%

“…In this section, three step detection methods and three step length estimators selected from a previous study [14] are described.…”

Section: Step Detection and Step Length Estimatorsmentioning

confidence: 99%

“…In a previous study in which the ASIS position was analysed to estimate gait parameters through a tri-axial accelerometer [14], three step length estimation methods -Zijlstra's method [16], Gonzalez's method [17] and Weinberg's method [18] performed better among 6 methods. They have less than 5% error in average step length estimation from healthy users.…”

Section: Step Length Estimatorsmentioning

confidence: 99%

See 2 more Smart Citations

Adapted step length estimators for patients with Parkinson's disease using a lateral belt worn accelerometer

Sayeed

Samà

Català

et al. 2015

THC

Self Cite

View full text Add to dashboard Cite

BACKGROUND: Parkinson's disease (PD) is a neurodegenerative disease that predominantly alters patients' motor performance. Reduced step length and inability of step are important symptoms associated with PD. Assessing patients' motor state monitoring step length helps to detect periods in which patients suffer lack of medication effect. OBJECTIVE: Evaluate the adaption of existing step length estimation methods based on accelerometer sensors to a new position on left lateral side of waist in 28 PD patients. METHODS: In this paper, a user-friendly position, the lateral side of the waist, is selected to place a tri-axial accelerometer. A newly developed step detection algorithm -Sliding Window Averaging Technique (SWAT) is evaluated in detecting steps using signals from this location. The detected steps are then used to estimate step length using four proposed correction factors for Zijlstra's, Gonzalez's and Weinberg's methods that were originally developed for the signals from lower back. RESULT: Results obtained from 28 PD patients are discussed and the effects of calibrating in each motor state are compared. A generic correction factor is also proposed and compared with the best method to use instead of individual calibration. Despite variable gait speed and different motor state, SWAT achieved overall accuracy of 96.76% in step detection. Among the different step length estimators, the Zijlstra method performs better with multiplying individual correction factors that consider left and right step length separately providing average error of 0.033 m. CONCLUSIONS: Zijlstra's method with individual correction factor that considers left and right step length separately and obtained from during ON state of a PD patients provide most accurate estimation among the others. As training session is during ON state, data from induced OFF state to train the methods are not required. A generic correction factor is also proposed to apply with Zijlstra's method to avoid individual calibration process.

show abstract

“…In previous study [11,14], it was shown that C, could be calculated using following equation that provides same results:…”

Section: A) Zijlstra's Methodsmentioning

confidence: 85%

“…In this section, three step detection methods and three step length estimators selected from a previous study [14] are described.…”

Section: Step Detection and Step Length Estimatorsmentioning

confidence: 99%

Section: Step Length Estimatorsmentioning

confidence: 99%

See 1 more Smart Citation

Adapted step length estimators for patients with Parkinson's disease using a lateral belt worn accelerometer

Sayeed

Samà

Català

et al. 2015

THC

Self Cite

View full text Add to dashboard Cite

show abstract

“…Entre los software más conocidos y validados se encuentran sistema Opto Gait, sistema BTS y Kinovea; estos permiten tener un control del movimiento del paciente y realizar una medición de forma cuantitativa de parámetros específicos de la marcha como longitud de paso, longitud de zancada, distancia, tiempo de contacto total, tiempo de paso, velocidad de marcha , aceleración, tiempo de paso progresivo, cadencia, ciclo de marcha, fase de apoyo, fase de balanceo, fase de contacto con el talón, fase de pie plano, fase de despegue, soporte donde se puede evaluar condiciones físicas, identificación de deficiencias y asimetrías musculares y ángulos de movimiento. Gracias a esto, se generan programas de rehabilitación específicos con resultados cuantificables, se controla de forma periódica el proceso de rehabilitación y se crean bases estadísticas para realizar estudios de normalización de datos (60)(61)(62)(63)(64)(65)(66)(67)(68)(69)(70)(71).…”

Section: Software Electrónicosunclassified

Herramientas tecnológicas para el estudio e intervención de la biomecanica en el deporte de alto rendimiento: una mirada desde fisioterapia

Mantilla

2019

RICCAFD

View full text Add to dashboard Cite

La biomecánica es el área de estudio de componentes cinéticos y cinemáticos implicados en el movimiento corporal humano profundizando en elementos tales como fuerza muscular, rangos de movimiento, aceleración, velocidad y desplazamiento. En el deporte de alto rendimiento, el estudio de las variables biomecánicas que inciden en el gesto motor es de vital importancia para entender los mecanismos de control y adaptación del movimiento a actividades especificas, con el fin de desarrollar programas para mejorar la ejecución del movimiento en determinadas fases del gesto deportivo y prevenir lesiones a partir del estudio del gesto motor. Por tal motivo el objetivo de este artículo es realizar una revisión sistemática de la literatura sobre el uso de la tecnología para la evaluación e intervención de la biomecánica en el deporte de alto rendimiento

show abstract

“…One complete cycle of human body locomotion, called as gait cycle, consists of two consecutive steps. Gait cycle is able to provide some information such as Parkinson's disease [1,2], human surveillance [3,4], and biometric [5]. Because of the important information contained in gait cycle, researchers have proposed the method to extract the gait signal from the accelerometer sensor.…”

Section: Introductionmentioning

confidence: 99%

Step Estimator Based on a Wearable ECG Monitor

Prawiro

Lin

2018

Mobile Information Systems

View full text Add to dashboard Cite

ECG signal acquisition may be contaminated with various unwanted signals from the environment, but the contaminating signals are not always be categorized as noise. In this paper, we use the signal processing method to extract the step signal from an ECG signal. The proposed algorithm has been designed, tested, and evaluated using ensemble empirical mode decomposition (EEMD) algorithm as ECG data extraction and accelerometer data as the step counter reference. The main idea is to choose the correct intrinsic mode function (IMF) that represents gait signal delineation of ECG, and then calculating the detection accuracy of gait signal based on the step counter from accelerometer data. We use different belt tightness configurations based on chest circumference for each subject during the treadmill test, and then perform the detection accuracy for each configuration. Five healthy subjects were participated in the treadmill test as training subjects. Each participant was asked to walk in four levels of speeds and continue to run in five different speeds: 1.8, 2.7, 3.6, 4.5, 5.4, 6.3, 7.2, 8.1, and 9.0 km/h. Later on, other five healthy subjects were used to evaluate the accuracy of the proposed algorithm using identical configuration as training subjects. The proposed algorithm is able to achieve the detection accuracy of 91.93% and 94.72% for training and testing subjects using the belt tightness scenario of 100% length of chest circumference.

show abstract

Comparison and adaptation of step length and gait speed estimators from single belt worn accelerometer positioned on lateral side of the body

Cited by 17 publications

References 14 publications

Adapted step length estimators for patients with Parkinson's disease using a lateral belt worn accelerometer

Adapted step length estimators for patients with Parkinson's disease using a lateral belt worn accelerometer

Herramientas tecnológicas para el estudio e intervención de la biomecanica en el deporte de alto rendimiento: una mirada desde fisioterapia

Step Estimator Based on a Wearable ECG Monitor

Contact Info

Product

Resources

About