# Intelligent Health Services Based on Biomedical Smart Sensors
## 2 Summary of the Contributions in This Special Issue  
The first article is entitled \"Smart Footwear Insole for Recognition of Foot Pronation and Supination Using Neural Networks\" [1], by [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON]. This research focuses on preventing possible pathologies with regard to foot postures, by measuring of foot plantar pressures in both dynamic and static conditions. To this end, the authors have developed a plantar pressure measurement system to identify areas with higher or lower pressure load, which is composed of an embedded system (low-power consumption microcontroller, some force sensor resistances and a low-energy bluetooth module) placed in the insole and a user graphical application. The software receives and shows the insole pressure information in real-time and provides information about the foot posture. The accuracy and usefulness of this information comes from a deep learning neural network system that identifies the different pressure states. This neural network is trained by using a stored dataset in order to obtain the classification results in real-time. As a result, the plantar pressure measurement system provides an accuracy over 90%, considering a training dataset of 3000 steps collected from 6 different users. The second article is entitled \"PISIoT: A Machine Learning and IoT-Based Smart Health Platform for Overweight and Obesity Control\" [2], by [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON]. The authors discuss the limitations of the Internet of Things (IoT) objects used for collecting data and analyzing health conditions with regard to overweight and obesity, in order to generate recommendations to prevent them. Therefore, the authors propose an alternative based on machine learning to this end. They have developed a machine learning and IoT-based smart health platform for the prevention, detection, treatment, and control of overweight and obesity, and other associated conditions or health problems. The system pursues three main purposes: to identify critical variables, classify patients, and generate medical recommendations. The proposed system was applied to the prevention of myocardial infarction in elderly patients with obesity by monitoring biomedical variables. The third article is entitled \"Evaluating Information-Retrieval Models and Machine-Learning Classifiers for Measuring the Social Perception towards Infectious Diseases\" [3], by [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON]. This research tries to improve the early-detection systems of infectious diseases, specifically in the field of infodemiology, which analyzes online information regarding public health that aims to complement traditional surveillance methods. Infodemiology processes big data by means of techniques for handling natural language efficiently. However, the authors do not find comprehensive studies that compare the accuracy of these techniques. To this end, the authors have conducted an infodemiology-based study to extract positive or negative utterances related to infectious diseases. First, they have used Twitter to compile and label a balanced corpus of infectious diseases, with 6164 utterances written in Spanish and collected from Central America. Next, they compared two statistical-models: word-grams and char-grams. The research focuses on analysing different gram sizes by using two machine learning classifiers: random forest and sequential minimal optimization. The char-grams model with five-char-gram sequences reached an accuracy of 90.80%. The fourth article is entitled \"Wearable Accelerometer and sEMG-Based Upper Limb BSN for Tele-Rehabilitation\" [4], by [PERSON], [PERSON], [PERSON] and [PERSON]. In this research, the data sources are wearable sensors (particularly, accelerometers) and surface electromyography, and the objective is tele-rehabilitation in human locomotion for people with chronic diseases. The authors propose new positions for the sensors and compare the measured signals to similar existing techniques.