Title: A Multiple Source Framework for the Identification of Activities of Daily Living Based on Mobile Device Data,
Abstract: The monitoring of the lifestyles may be performed based on a system for the
recognition of Activities of Daily Living (ADL) and their environments,
combining the results obtained with the user agenda. The system may be
developed with the use of the off-the-shelf mobile devices commonly used,
because they have several types of sensors available, including motion,
magnetic, acoustic, and location sensors. Data acquisition, data processing,
data fusion, and artificial intelligence methods are applied in different
stages of the system developed, which recognizes the ADL with pattern
recognition methods. The motion and magnetic sensors allow the recognition of
activities with movement, but the acoustic sensors allow the recognition of the
environments. The fusion of the motion, magnetic and acoustic sensors allows
the differentiation of other ADL. On the other hand, the location sensors
allows the recognition of ADL with large movement, and the combination of these
sensors with the other sensors increases the number of ADL recognized by the
system. This study consists on the comparison of different types of ANN for
choosing the best methods for the recognition of several ADL, which they are
implemented in a system for the recognition of ADL that combines the sensors
data with the users agenda for the monitoring of the lifestyles. Conclusions
point to the use of Deep Neural Networks (DNN) with normalized data for the
identification of ADL with 85.89% of accuracy, the use of Feedforward neural
networks with non-normalized data for the identification of the environments
with 86.50% of accuracy, and the use of DNN with normalized data for the
identification of standing activities with 100% of accuracy, proving the
reliability of the framework presented in this study.