Data acquisition of timed-up and go test with older adults: accelerometer, magnetometer, electrocardiography and electroencephalography sensors’ data

Abstract

We present a dataset related to the acquisition of different sensors data during the performance of the Timed-Up and Go test with the mobile device positioned in a waistband for the acquisition of accelerometer and magnetometer data, and a BITalino device positioned in a chest band for the acquisition of Electrocardiography and Electroencephalography for further processing. The data acquired from the BITalino device is acquired simultaneously by a Bluetooth connection with the same mobile application. The data was acquired in five institutions, including Centro Comunitário das Lameiras, Lar Nossa Senhora de Fátima, Centro Comunitário das Minas da Panasqueira, Lar da Misericórdia da Santa Casa da Misericórdia do Fundão, and Lar da Aldeia de Joanes da Santa Casa da Misericórdia do Fundão from Fundão and Covilhã municipalities (Portugal). This article describes the data acquired from a several subjects from the different institutions for the acquisition of accelerometer and magnetometer data, where each person performed the Timed-Up and Go test three times, where each output from the sensors was acquired with a sampling rate of 100 Hz. Related to the data acquired by the sensors connected to the BITalino device, 31 persons performed the different experiments related to the Timed-Up and Go Test. Following the data acquired from Electroencephalography and Electrocardiography sensors, only the data acquired from 14 individuals was considered valid. The data acquired by a BITalino device has a sampling rate of 100 Hz. These data can be reused for testing machine learning methods for the evaluation of the performance of the Timed-Up and Go test with older adults.

ELEKTRA: ELEKTRokardiomatrix application to biometric identification with convolutional neural networks

“…The above result implies that the proposed system maintains its workability with noisy signals. Note that noisy signals are widespread when the EKG records are captured with lowcost EKG sensors [164]. In addition, Gaussian Noise can in fact be used to simulate noise from a real-world application as it is stated by the Central Limit Theorem in Section 7.1.5.…”