Publication:
A human gait temporal parameters calculation algorithm

Thumbnail Image
Files

Files

A human gait temporal parameters calculation algorithm.pdf (1.09 MB)
View Statistics
Reference Managers
Mendeley
Indexers
Google Scholar CORE
QR Code
QR Code
Authors

Authors

Rodriguez Cheu, Luis Eduardo
Rengifo Rodas, Carlos Felipe
Caicedo Rodríguez, Pablo Eduardo
Abstract (Spanish)
Este artículo presenta un nuevo algoritmo para calcular tres parámetros temporales de la marcha humana: cadencia, tiempo de balanceo y tiempo de zancada. Estos valores se calculan a partir de: el tiempo de talonamiento, el tiempo de despegue de la punta del pie y el tiempo del punto medio de balanceo, que pueden obtenerse a su vez a partir de la primera derivada temporal de la velocidad angular del eje medio-lateral de ambas piernas. Los resultados generados por el algoritmo propuesto se validaron con los del software comercial Tech MCS Studio de Technaid. La comparación arroja una leve diferencia para la cadencia y el tiempo de zancada. Además, el nuevo algoritmo es una buena alternativa a los algoritmos basados en el umbral previamente reportados en la literatura que requieren una gran cantidad de trabajo dedicado a la sintonía manual de sus múltiples parámetros.
Abstract (English)
This paper presents a new algorithm to calculate three temporal parameters of human gait: cadence, swing time and stride time. These values are computed from: heel-strike time, toe-off time and mid-swing point time which can be in turn obtained from the first time derivate of the medio-lateral axis angular velocity of both shanks. The results generated by the proposed algorithm were validated with those of the commercial software Tech MCS Studio of Technaid. The comparison gives a mild difference for cadence and stride time. Aditionally, the new algorithm is a good alternative to the thresholding-based algorithms previously reported in the literature which require a great amount of work dedicated to the manual tuning of their multiple parameters.
URI: https://repositorio.escuelaing.edu.co/handle/001/1568
Collections

Collections

AA. Gibiome
References

Herran Alvaro, García-Zapirain Begoña, Méndez-Zorrilla Amaia. Gait analysis methods: An overview of wearable and non-wearable systems, highlighting clinical applications Sensors (Switzerland). 2014;14:3362–3394.

Bugané F., Benedetti M. G., Casadio G., et al. Estimation of spatial-temporal gait parameters in level walking based on a single accelerometer: Validation on normal subjects by standard gait analysis Computer Methods and Programs in Biomedicine. 2012;108:129–137.

Greene Barry R, Foran Timothy G, McGrath Denise, Doheny Emer P, Burns Adrian, Caulfield Brian. A Comparison of Algorithms for Body-Worn Sensor-Based Spatiotemporal Gait Parameters to the GAITRite Electronic Walkway Journal of Applied Biomechanics. 2012;28:349–355.

Jasiewicz Jan M., Allum John H J, Middleton James W., et al. Gait event detection using linear accelerometers or angular velocity transducers in able-bodied and spinal-cord injured individuals Gait and Posture. 2006;24:502–509.

Aung Min S H, Thies Sibylle B., Kenney Laurence P J, et al. Automated detection of instantaneous gait events using time frequency analysis and manifold embedding IEEE Transactions on Neural Systems and Rehabilitation Engineering. 2013;21:908–916.

Greene Barry R., McGrath Denise, O'Neill Ross, O'Donovan Karol J., Burns Adrian, Caulfield Brian. An adaptive gyroscope-based algorithm for temporal gait analysis Medical and Biological Engineering and Computing. 2010;48:1251–1260.

Fraccaro Paolo, Walsh Lorcan, Doyle Julie, O’Sullivan Dympna. Real-world Gyroscope-based Gait Event Detection and Gait Feature Extraction eTELEMED 2014, The Sixth International Conference on eHealth, Telemedicine, and Social Medicine. 2014:247–252.

Sabatini Angelo M., Martelloni Chiara, Scapellato Sergio, Cavallo Filippo. Assessment of walking features from foot inertial sensing IEEE Transactions on Biomedical Engineering. 2005;52:486–494.