This paper presents a simple wearable, non-intrusive affordable mobile framework
that allows remote patient monitoring during gait rehabilitation by doctors and physiotherapists.
The system includes a set of 2 Shimmer3 9DoF Inertial Measurement Units (IMUs), an Android
smartphone and a developed app for collecting, primary processing of data and for persistence
of data in a remote PostgreSQL database, which is available in a remote server and where further
data processing is performed. This framework provides gait features classifier by invoking an
implemented REST API available in the remote server. Low computational load algorithms
based on Euler angles and filtered signals were developed and used for the classification and
identification of several gait disturbances. These algorithms include the alignment of IMUs
sensors data by means of a common temporal reference as well as heel strike and stride detection
algorithms. After segmentation of the remotely collected signals for gait strides identification
relevant features were extracted to feed, train and test a classifier for prediction of gait abnormalities using supervised machine learning type and Extremely Randomized Trees method.