Experimental characterization and analysis of the BITalino platforms against a reference device

Abstract

Low-cost hardware platforms for biomedical engineering are becoming increasingly available, which empower the research community in the development of new projects in a wide range of areas related with physiological data acquisition. Building upon previous work by our group, this work compares the quality of the data acquired by means of two different versions of the multimodal physiological computing platform BITalino, with a device that can be considered a reference. We acquired data from 5 sensors, namely Accelerometry (ACC), Electrocardiography (ECG), Electroencephalography (EEG), Electrodermal Activity (EDA) and Electromyography (EMG). Experimental evaluation shows that ACC, ECG and EDA data are highly correlated with the reference in what concerns the raw waveforms. When compared by means of their commonly used features, EEG and EMG data are also quite similar across the different devices.

Identification of Diseases Based on the Use of Inertial Sensors: A Systematic Review

“…However, the accelerometer is available on different devices, including mobile devices and other specific types of equipment, such as the Bitalino device [71]. There are various problems related to the data acquisition that is mainly associated with the synchronism of the data transmission, the failures in the data acquisition, the sensitivity of the accelerometer used, positioning of the mobile device during the data acquisition, and other different hardware and software problems related to the devices used [72,73].…”

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

“… The data provide a set of data acquired during the performance of the Timed-Up and Go test [1] , [2] , [3] with the sensors available in a mobile [ 4 , 5 ] and a BITalino devices [6] , including accelerometer, magnetometer, Electroencephalography and Electrocardiography sensors; The data is important for the creation of solutions for automatic validation of Timed-Up and Go test, and, as we acquired Electroencephalography and Electrocardiography data, it will allows to the creation of patterns of different diseases [7] , [8] , [9] , [10] for further developments; The acquired data may be used for the recognition of different stages and activities during the Timed-Up and Go test, as well as the identification of diseases with machine learning techniques [10] , [11] , [12] ; The data are valid for the creation of disease patterns associated with movement, cardiac and brain frequency, and other problems related to walking activity, applying different techniques to reduce the artefacts [13] , [14] , [15] . It also allows further research with the sensors available in off-the-shelf mobile devices for further creation of Mobile Health solutions [ 16 , 17 ].…”

“…The data provide a set of data acquired during the performance of the Timed-Up and Go test [1] , [2] , [3] with the sensors available in a mobile [ 4 , 5 ] and a BITalino devices [6] , including accelerometer, magnetometer, Electroencephalography and Electrocardiography sensors;…”

Towards Designing Games for Experimental Protocols Investigating Human-Based Phenomena

“…Skin Conductance (SC) and Heart-Rate Variability (HRV) are one of the most common physiological modalities explored within literature [10,15,36,49]. They are the most cost efficient thanks to the wide availability of multi-modal physiological devices such as the BITalino [3] or Empatica E4 [40] which remain relatively inexpensive. Furthermore, the fact that they are easy to setup and are among the least intrusive "over-the-skin" devices available has definitely contributed to its extensive usage within literature.…”