Activity Recognition based on Wearable Sensors

Human activity recognition based on wearable sensors has received great attention in areas such as healthcare, homeland security and smart environments, mainly because it enables easy data acquisition and processing. This task consists of assigning a category of activity to signals provided by wearable sensors such as accelerometers, gyroscopes and magnetometers.



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SensorCap: Sensor Data Capture

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

Benchmark on Activity Recognition based on Wearable Sensors

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