Wavelet PCA for automatic identification of walking with and without an exoskeleton on a treadmill using pressure and accelerometer sensors.Nowadays portable devices with more number of sensors are used for gait assessment and monitoring for elderly and disabled.  Hence, this study uses wavelet PCA as a signal processing technique to separate the original sensor signal from the signal obtained from the sensors through the integrated unit to compare the two types of walking (with and without an exoskeleton).  This comparison using wavelet PCA will enable the researchers to obtain accurate sensor data and compare and analyze the data in order to further improve the design of compact portable devices used to monitor and assess the gait in stroke or paralyzed subjects. 
Wavelet PCA
Wavelet PCA for automatic identification of walking with and without an exoskeleton on a treadmill using pressure and accelerometer sensors.Nowadays portable devices with more number of sensors are used for gait assessment and monitoring for elderly and disabled.  Hence, this study uses wavelet PCA as a signal processing technique to separate the original sensor signal from the signal obtained from the sensors through the integrated unit to compare the two types of walking (with and without an exoskeleton).  This comparison using wavelet PCA will enable the researchers to obtain accurate sensor data and compare and analyze the data in order to further improve the design of compact portable devices used to monitor and assess the gait in stroke or paralyzed subjects. 
PCA
Wavelet PCA for automatic identification of walking with and without an exoskeleton on a treadmill using pressure and accelerometer sensors.Nowadays portable devices with more number of sensors are used for gait assessment and monitoring for elderly and disabled.  Hence, this study uses wavelet PCA as a signal processing technique to separate the original sensor signal from the signal obtained from the sensors through the integrated unit to compare the two types of walking (with and without an exoskeleton).  This comparison using wavelet PCA will enable the researchers to obtain accurate sensor data and compare and analyze the data in order to further improve the design of compact portable devices used to monitor and assess the gait in stroke or paralyzed subjects. 
PCA