Identificador persistente para citar o vincular este elemento: http://hdl.handle.net/10553/77407
Título: Analyzing gait symmetry with automatically synchronized wearable sensors in daily life
Autores/as: Steinmetzer, Tobias 
Wilberg, Sandro
Bönninger, Ingrid
Travieso González, Carlos Manuel 
Clasificación UNESCO: 3307 Tecnología electrónica
Palabras clave: Synchronization
Gait analysis
Inertial sensors
Dynamic time warping
Convolutional neural networks, et al.
Fecha de publicación: 2020
Publicación seriada: Microprocessors and Microsystems 
Resumen: Gait deviations such as asymmetry are one of the characteristic symptoms of motor dysfunctions that contribute to the risk of falls. Our objective is to measure gait abnormalities such as asymmetry of the lower limbs in order to evaluate the diagnosis more objectively. For the measurement we use inertial measurement unit (IMU) sensors and force sensors, which are integrated in wristbands and insoles. To extend the battery life of wearable devices, we only save data of the activity gait within the wearables. Therefore we perform activity recognition with a smartphone. Using convolutional neural network (CNN) we achieved an accuracy of 94.7 % of the activity gait recognition. Before recording we synchronize the wearable sensors and reach a maximum latencies of 3 ms . Before the analysis of the symmetry we detect the strides by using a CNN with an accuracy of 98.8 %. For the symmetry evaluation we used dynamic time warping (DTW). The DTW enables us to calculate symmetry of the complete time series of human gait.
URI: http://hdl.handle.net/10553/77407
ISSN: 0141-9331
DOI: 10.1016/j.micpro.2020.103118
Fuente: Microprocessors and Microsystems [ISSN 0141-9331], v. 77, 103118, (Septiembre 2020)
Colección:Artículos
miniatura
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