# Smart Footwear Insole for Recognition of Foot Pronation and Supination Using Neural Networks
## 1 Introduction  
Nowadays, foot and ankle pain and related disabilities are very common, with some studies indicating that around 24% of people aged over 45 years report frequent foot pain [1]. It has also been shown that abnormal foot posture is associated with foot pain [2] as well as with lower limb injuries and pathologies [3].  
Also, problems associated with abnormal gait include Fractures, Ankle Sprain, Pimple Pain or Plantar Fasciiitis, among others [4]. Hence, it is important to characterize foot posture (static) and foot function (how the foot moves when walking), with different methods available in the literature [5].  
Abnormal foot postures have recently been experimentally related to the pressure at the base of the foot during the walk [3, 6]. Therefore, a podiatrist (or physiotherapist) can perform a walking study of the patient's footprint, in order to detect these problems, prevent related injuries and indicate exercises to correct them. The classic gait study consists of walking in a straight line through a sensorized surface that emulates a path of several meters length: this surface records the measures obtained for each step to analyze it later. The main problem with this analysis mechanism is the psychological component: the patient knows that he/she is being analyzed and tries to walk in the best possible way.  
With this, in many cases the information received does not correspond to a 100% with the patient's way of walking [7].  
In this work, we try to avoid this psychological component by developing a smart instrumented footwear insole. Some advances have been done in this field [8; 9; 10], demonstrating that these devices may have multiple applications such as in the orthopedic field, for the study of foot function; in the orthoprosthetic field, for the design of footwear, insoles and prostheses; in the biomechanical field, for the study of normal gait or pathological; or in sports medicine, for the study of the most appropriate athletic footwear in each athletic modality.  
These instrumented footwear insoles are adjusted to the biomechanical anatomy of the foot. The aim of these devices is to obtain tangible data in order to analyze the gait with freedom of movement and without space limitation. Several ways for collecting this data have been implemented in the bibliography but, in this work, we combine the instrumented footwear insole with a Real-Time Deep Learning data analysis system, whose main goal is the gait classification between pronator, spinator and neutral. The main goal of this work is to study the feasibility of the proposed mechanism: the dataset used to train the system will be increased in future works (now it is based on the footsteps from six different people), but in this work we focus on describing the process and its functionality.  
Although there is currently no consensus on the terminology, in this paper we will use the common terms \"pronation\" to indicate when the foot undergoes greater lowering of the medial longitudinal arch and more medial distribution of plantar loading during gait and \"supination\" when the foot undergoes greater elevation of the medial longitudinal arch and more lateral distribution of plantar loading during gait [2].  
The rest of the paper is divided in the following way: first, the data acquisition is described deeply, presenting the sensors placement study, the embedded system and the user application used. Subsequently, the neural network implementation is described. Next, the training dataset and the classification results are shown. Finally, conclusions are presented.