Abnormal foot postures during gait are common sources of pain and pathologies of the
lower limbs. Measurements of foot plantar pressures in both dynamic and static conditions can detect
these abnormal foot postures and prevent possible pathologies. In this work, a plantar pressure
measurement system is developed to identify areas with higher or lower pressure load. This system
is composed of an embedded system placed in the insole and a user application. The instrumented
insole consists of a low-power microcontroller, seven pressure sensors and a low-energy bluetooth
module. The user application receives and shows the insole pressure information in real-time and,
finally, provides information about the foot posture. In order to identify the different pressure states
and obtain the final information of the study with greater accuracy, a Deep Learning neural network
system has been integrated into the user application. The neural network can be trained using a
stored dataset in order to obtain the classification results in real-time. Results prove that this system
provides an accuracy over 90% using a training dataset of 3000+ steps from 6 different users.Ministerio de Economía y Competitividad TEC2016-77785-
