Visualizing Gait Patterns of Able bodied Individuals and Transtibial Amputees with the Use of Accelerometry in Smart Phones
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 wi...
- Autores:
-
Teknomo, Kardi
Estuar, Maria Regina
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2014
- Institución:
- Universidad Nacional de Colombia
- Repositorio:
- Universidad Nacional de Colombia
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.unal.edu.co:unal/66565
- Acceso en línea:
- https://repositorio.unal.edu.co/handle/unal/66565
http://bdigital.unal.edu.co/67593/
- Palabra clave:
- 51 Matemáticas / Mathematics
31 Colecciones de estadística general / Statistics
Decision Tree Analysis
Feature Selection
Gait Monitoring
Transtibial Amputees
Wireless Sensors
Análisis de árboles de decisión
Discapacitados
Monitores de paso
Selección de característica
Sensores inalámbricos.
- Rights
- openAccess
- License
- Atribución-NoComercial 4.0 Internacional
| Summary: | 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. |
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