A database of physical therapy exercises with variability of execution collected by wearable sensors

This document introduces the PHYTMO database, which contains data from physical therapies recorded with inertial sensors, including information from an optical reference system. PHYTMO includes the recording of 30 volunteers, aged between 20 and 70 years old. A total amount of 6 exercises and 3 gait variations were recorded. The volunteers performed two series with a minimum of 8 repetitions in each one. PHYTMO includes magneto-inertial data, together with a highly accurate location and orientation in the 3D space provided by the optical system. The files were stored in CSV format to ensure its usability. The aim of this dataset is the availability of data for two main purposes: the analysis of techniques for the identification and evaluation of exercises using inertial sensors and the validation of inertial sensor-based algorithms for human motion monitoring. Furthermore, the database stores enough data to apply Machine Learning-based algorithms. The participants' age range is large enough to establish age-based metrics for the exercises evaluation or the study of differences in motions between different groups

Estudio de sistemas inerciales en el seguimiento de terapias rehabilitadoras basadas en Machine Learning

Este trabajo ha desarrollado y caracterizado una herramienta para monitorizar ejercicios físicos de terapias pautadas empleando los datos obtenidos de cuatro unidades de medida inercial (IMUs). La monitorización incluye la identificación del ejercicio entre un catálogo y su evaluación, entre bien o mal. Dicha clasificación se ha realizado mediante algoritmos de Machine Learning. Para este fin, se optimiza la posición y el número de IMUs empleadas. Además, se determina K-Nearest Neighbours como el clasificador más adecuado y el número de IMUs óptimo en dos, una por extremidad. Con ello, se obtienen exactitudes en identificación y evaluación del 99, 5 %.This work has developed and characterized a tool to monitor physical exercises of paused therapies using the data obtained from four units of inertial measurement (IMUs). Monitoring includes identifying the exercise between a catalog and evaluating it, right or wrong. This classification was done using Machine Learning algorithms. For this purpose, the position and number of IMUs used is optimized. In addition, K-Nearest Neighbours is determined as the most suitable classifier and the optimal number of IMUs in two, one per limb. This results in accuracies in identification and evaluation of 99,5 %.Grado en Ingeniería en Tecnologías de Telecomunicació