Jessica Sena defended this afternoon (18), her dissertation “Human Activity Recognition based on Wearable Sensors using Multiscale DCNN Ensemble”, obtaining her Master’s degree from the Graduate Program in Computer Science of the Federal University of Minas Gerais (UFMG). The main results of her research were presented two months ago at the European Signal Processing Conference (EUSIPCO), in Rome, Italy. Sena was recently accepted in the Ph.D.… Read more
SensorCap is an Android tool that captures sensor data in user-defined configurations. The purpose is to allow researchers and developers to quickly save sensor data for research, testing and prototyping. The sensors are broadly defined to include “motion”, “position” and “environment” device sensors.
An intuitive user interface facilitates tasks such as setting up a sensor, entering capture details and sharing sensor data.… Read more
This page contains the source code and data used in our paper “Human Activity Recognition Based on Wearable Sensor Data: A Standardization of the State-of-the-Art“. In this paper, we implement and evaluate several state-of-the-art approaches, ranging from methods based on handcrafted features to convolutional neural networks. Also, we standardize a large number of datasets, which vary in terms of sampling rate, number of sensors, activities, and subjects.… Read more