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Visualizing Gait Patterns of Able bodied Individuals and Transtibial Amputees with the Use of Accelerometry in Smart Phones
Visualización de patrones de paso de individuos con y sin discapacidad con el uso de acelerometría en teléfonos inteligentes
DOI:
https://doi.org/10.15446/rce.v37n2spe.47951Keywords:
Decision Tree Analysis, Feature Selection, Gait Monitoring, Transtibial Amputees, Wireless Sensors (en)Análisis de árboles de decisión, Discapacitados, Monitores de paso, Selección de característica, Sensores inalámbricos. (es)
Human gait analysis is used to indirectly monitor the rehabilitation of patients affected by diseases or to directly monitor patients under orthotic care. Visualization of gait patterns on the instrument are used to capture the data. In this study, we created a mobile application that serves as a wireless sensor to capture movement through a smartphone accelerometer. The application was used to collect gait data from two groups (able-bodied and unilateral transtibial amputees). Standard gait activities such as walking, running and climbing, including non-movement, sitting were captured, stored and analyzed. This paper discusses different visualization techniques that can be derived from accelerometer data. Removing gravity data, accelerometer data can be transformed into distribution data using periodicity; features were derived from histograms. Decision tree analysis shows that only three significant features are necessary to classify subject activity, namely: average of minimum peak values, student t-statistics of minimum peak values and mode of maximum peak values. We found that the amputee group had a higher acceleration and a lower skewness period between peaks of accelerations than the able-bodied group.
Análisis del paso de humanos es usado como una manera indirecta de monitorear la rehabilitación de pacientes afectados por enfermedades o bajo el cuidado ortopédico. La visualización de patrones de paso se usa para captura de datos. En este estudio, se creó una aplicación móvil que sirve como un sensor inalámbrico para capturar el movimiento a través de un acelerómetro en un teléfono móvil. Se recolectaron datos de dos grupos (con y sin discapacidad tibial). Datos de actividades de paso estándar tales como caminar, correr y escalar, incluso moverse o sentarse fueron recogidos, grabados y analizados. Este artículo discute diferentes técnicas de visualizaciíon que fueron derivadas de estos datos de acelerómetro. Removiendo datos de gravedad, los datos del acelerómetro pueden ser transformados en datos de distribución usando periodicidad a partir de histogramas. Análisis del árbol de decisión muestra que sólo tres características significativas son necesarios para clasificar la actividad de los sujetos: promedio estadísticas t-student y moda de valores altos mínimos. Se encontró que el grupo de personas con discapacidad tibial tienen una aceleración alta, y un período de sesgo más bajo entre picos de aceleración que el grupo de no discapacitados.
https://doi.org/10.15446/rce.v37n2spe.47951
1Ateneo de Manila University, School of Science and Engineering, Department of Information Systems and Computer Science, Quezon City, Philippines. Associate Professor. Email: kteknomo@ateneo.edu
2Ateneo de Manila University, School of Science and Engineering, Department of Information Systems and Computer Science, Quezon City, Philippines. Associate Professor. Email: restuar@ateneo.edu
Human gait analysis is used to indirectly monitor the rehabilitation of patients affected by diseases or to directly monitor patients under orthotic care. Visualization of gait patterns on the instrument are used to capture the data. In this study, we created a mobile application that serves as a wireless sensor to capture movement through a smartphone accelerometer. The application was used to collect gait data from two groups (able-bodied and unilateral transtibial amputees). Standard gait activities such as walking, running and climbing, including non-movement, sitting were captured, stored and analyzed. This paper discusses different visualization techniques that can be derived from accelerometer data. Removing gravity data, accelerometer data can be transformed into distribution data using periodicity; features were derived from histograms. Decision tree analysis shows that only three significant features are necessary to classify subject activity, namely: average of minimum peak values, student t-statistics of minimum peak values and mode of maximum peak values. We found that the amputee group had a higher acceleration and a lower skewness period between peaks of accelerations than the able-bodied group.
Key words: Decision Tree Analysis, Feature Selection, Gait Monitoring, Transtibial Amputees, Wireless Sensors.
Análisis del paso de humanos es usado como una manera indirecta de monitorear la rehabilitación de pacientes afectados por enfermedades o bajo el cuidado ortopédico. La visualización de patrones de paso se usa para captura de datos. En este estudio, se creó una aplicación móvil que sirve como un sensor inalámbrico para capturar el movimiento a través de un acelerómetro en un teléfono móvil. Se recolectaron datos de dos grupos (con y sin discapacidad tibial). Datos de actividades de paso estándar tales como caminar, correr y escalar, incluso moverse o sentarse fueron recogidos, grabados y analizados. Este artículo discute diferentes técnicas de visualización que fueron derivadas de estos datos de acelerómetro. Removiendo datos de gravedad, los datos del acelerómetro pueden ser transformados en datos de distribución usando periodicidad a partir de histogramas. Análisis del árbol de decisión muestra que sólo tres características significativas son necesarios para clasificar la actividad de los sujetos: promedio estadísticas t-student y moda de valores altos mínimos. Se encontró que el grupo de personas con discapacidad tibial tienen una aceleración alta, y un período de sesgo más bajo entre picos de aceleración que el grupo de no discapacitados.
Palabras clave: análisis deárboles de desición, discapacitados, monitores de paso, selección de característica, sensores inalámbricos.
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References
1. A. Sawers, M.E. Hahn, V.E. Kelly, J.M. Czerniecki, & D. Kartin, (2012), 'Beyond componentry: How principles of motor learning can enhance locomotor rehabilitation of individuals with lower limb loss-A review', Journal of Rehabilitation Research & Development 40(10), 1431-1442.
2. Adam M Howell, Toshiki Kobayashi, Heather A Hayes, K Bo Foreman, & Stacy J Morris Bamberg, (2013), 'Kinetic gait analysis using a low-cost insole', Biomedical Engineering, IEEE Transactions on 60(12), 3284-3290.
3. Alvaro Muro-de-la-Herran, Begonya Garcia-Zapirain, & Amaia Mendez-Zorrilla, (2014), 'Gait Analysis Methods: An Overview of Wearable and Non-Wearable Systems, Highlighting Clinical Applications', Sensors 14(2), 3362-3394.
4. Bijan Najafi, Tahir Khan, Adam Fleischer, & James Wrobel, (2013), 'The impact of footwear and walking distance on gait stability in diabetic patients with peripheral neuropathy', Journal of the American Podiatric Medical Association 103(3), 165-173.
5. Céline Bonnyaud, Didier Pradon, Raphael Zory, Djamel Bensmail, Nicolas Vuillerme, & Nicolas Roche, (2013), 'Does a Single Gait Training Session Performed Either Overground or on a Treadmill Induce Specific Short-Term Effects on Gait Parameters in Patients with Hemiparesis? a Randomized Controlled Study', Topics in Stroke Rehabilitation 20(6), 509-518.
6. Catherine A. Macleod, Bernard A. Conway, David B. Allan, & Sujay S. Galen, (2014), 'Development and validation of a low-cost, portable and wireless gait assessment tool', Medical Engineering & Physics 36(4), 541-546.
7. E. D. de Bruin, M. Hubli, P. Hofer, P. Wolf, K. Murer, & W. Zijlstra, (2012), 'Validity and reliability of accelerometer-based gait assessment in patients with diabetes on challenging surfaces', Journal of Aging Research 2012.
8. F. Ferrarello, V.A. Bianchi, M. Baccini, G. Rubbieri, E. Mossello, M.C. Cavallini, N. Marchionni, & M. Di Bari, (2013), 'Tools for observational gait analysis in patients with stroke: A systematic review', Physical Therapy 93(12), 1673-850.
9. Gavriel Salvendy, (2012), Handbook of Human Factors and Ergonomics, John Wiley & Sons.
10. Geruza P Bella, Nádia BB Rodrigues, Paola J Valenciano, Luciana MAE Silva, & Regina CT Souza, (2012), 'Correlation among the visual gait assessment scale, Edinburgh visual gait scale and observational gait scale in children with spastic diplegic cerebral palsy', Brazilian Journal of Physical Therapy 16(2), 134-140.
11. I Maidan, T Freedman, R Tzemah, N Giladi, A Mirelman, & JM Hausdorff, (2014), 'Introducing a new definition of a near fall: Intra-rater and inter-rater reliability', Gait & Posture 39(1), 645-647.
12. John G Buckley, Alan R De Asha, Louise Johnson, & Clive B Beggs, (2013), 'Understanding adaptive gait in lower-limb amputees: Insights from multivariate analyses', Journal of Neuroengineering and Rehabilitation 10(1), 98.
13. Kosuke Okusa, & Toshinari Kamakura, (2013), 'Gait Parameter and Speed Estimation from the Frontal View Gait Video Data Based on the Gait Motion and Spatial Modeling', IAENG International Journal of Applied Mathematics 43(1), 37-44.
14. M. Baritz, D. Cotoros, L. Cristea, & L. Rogozea, (2010), 'Assessment of human bio-behavior during gait process using LifeMod software', BRAIN. Broad Research in Artificial Intelligence and Neuroscience 1, 169-177.
15. T Gibson, RS Jeffery, & AMO Bakheit, (2006), 'Comparison of three definitions of the mid-stance and mid-swing events of the gait cycle in children', Disability & Rehabilitation 28(10), 625-628.
Este artículo se puede citar en LaTeX utilizando la siguiente referencia bibliográfica de BibTeX:
@ARTICLE{RCEv37n2a12,
AUTHOR = {Teknomo, Kardi and Estuar, Maria Regina},
TITLE = {{Visualizing Gait Patterns of Able bodied Individuals and Transtibial Amputees with the Use of Accelerometry in Smart Phones}},
JOURNAL = {Revista Colombiana de Estadística},
YEAR = {2014},
volume = {37},
number = {2},
pages = {471-488}
}
References
Baritz, M., Cotoros, D., Cristea, L. & Rogozea, L. (2010), ‘Assessment of human bio-behavior during gait process using lifemod software’, BRAIN. Broad Research in Artificial Intelligence and Neuroscience 1, 169–177.
Bella, G. P., Rodrigues, N. B., Valenciano, P. J., Silva, L. M. & Souza, R. C. (2012), ‘Correlation among the visual gait assessment scale, Edinburgh visual gait scale and observational gait scale in children with spastic diplegic cerebral palsy’, Brazilian Journal of Physical Therapy 16(2), 134–140.
Bonnyaud, C., Pradon, D., Zory, R., Bensmail, D., Vuillerme, N. & Roche, N. (2013), ‘Does a single gait training session performed either overground or on a treadmill induce specific short-term effects on gait parameters in patients with hemiparesis? a randomized controlled study’, Topics in Stroke Rehabilitation 20(6), 509–518.
Buckley, J. G., De Asha, A. R., Johnson, L. & Beggs, C. B. (2013), ‘Understanding adaptive gait in lower-limb amputees: Insights from multivariate analyses’, Journal of Neuroengineering and Rehabilitation 10(1), 98.
de Bruin, E. D., Hubli, M., Hofer, P., Wolf, P., Murer, K. & Zijlstra, W. (2012), ‘Validity and reliability of accelerometer-based gait assessment in patients with diabetes on challenging surfaces’, Journal of Aging Research 2012.
Ferrarello, F., Bianchi, V., Baccini, M., Rubbieri, G., Mossello, E., Cavallini, M., Marchionni, N. & Di Bari, M. (2013), ‘Tools for observational gait analysis in patients with stroke: A systematic review’, Physical Therapy 93(12), 1673– 850.
Gibson, T., Jeffery, R. & Bakheit, A. (2006), ‘Comparison of three definitions of the mid-stance and mid-swing events of the gait cycle in children’, Disability & Rehabilitation 28(10), 625–628.
Howell, A. M., Kobayashi, T., Hayes, H. A., Foreman, K. B. & Bamberg, S. J. M. (2013), ‘Kinetic gait analysis using a low-cost insole’, Biomedical Engineering, IEEE Transactions on 60(12), 3284–3290.
Khan, M. (2010), ‘Analysis of gait of amputees’.
*http://ausimkhan.blogspot.com/posted
Kishner, S. & Monroe, J. (2013), ‘Gait analysis after amputation’.
*http://emedicine.medscape.com/article/1237638-overview
Macleod, C. A., Conway, B. A., Allan, D. B. & Galen, S. S. (2014), ‘Development and validation of a low-cost, portable and wireless gait assessment tool’, Medical Engineering & Physics 36(4), 541–546.
Maidan, I., Freedman, T., Tzemah, R., Giladi, N., Mirelman, A. & Hausdorff, J. (2014), ‘Introducing a new definition of a near fall: Intra-rater and inter-rater reliability’, Gait & Posture 39(1), 645–647.
Muro-de-la Herran, A., Garcia-Zapirain, B. & Mendez-Zorrilla, A. (2014), ‘Gait analysis methods: An overview of wearable and non-wearable systems, highlighting clinical applications’, Sensors 14(2), 3362–3394.
Najafi, B., Khan, T., Fleischer, A. & Wrobel, J. (2013), ‘The impact of footwear and walking distance on gait stability in diabetic patients with peripheral neuropathy’, Journal of the American Podiatric Medical Association 103(3), 165–173.
Okusa, K. & Kamakura, T. (2013), ‘Gait parameter and speed estimation from the frontal view gait video data based on the gait motion and spatial modeling’, IAENG International Journal of Applied Mathematics 43(1), 37–44.
Salvendy, G. (2012), Handbook of Human Factors and Ergonomics, John Wiley & Sons.
Sawers, A., Hahn, M., Kelly, V., Czerniecki, J. & Kartin, D. (2012), ‘Beyond componentry: How principles of motor learning can enhance locomotor rehabilitation of individuals with lower limb loss–A review’, Journal of Rehabilitation Research & Development 40(10), 1431–1442.
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