Detecting stress when performing physical activities is an interesting field that has received relatively little research interest to date. In this paper, we took a first step towards redressing this, through a comprehensive review and the design of a low-cost body area network (BAN) made of a set of wearables that allow physiological signals and human movements to be captured simultaneously. We used four different wearables: OpenBCI and three other open-hardware custom-made designs that communicate via bluetooth low energy (BLE) to an external computer—following the edge-computingconcept—hosting applications for data synchronization and storage. We obtained a large number of physiological signals (electroencephalography (EEG), electrocardiography (ECG), breathing rate (BR), electrodermal activity (EDA), and skin temperature (ST)) with which we analyzed internal states in general, but with a focus on stress. The findings show the reliability and feasibility of the proposed body area network (BAN) according to battery lifetime (greater than 15 h), packet loss rate (0% for our custom-made designs), and signal quality (signal-noise ratio (SNR) of 9.8 dB for the ECG circuit, and 61.6 dB for the EDA). Moreover, we conducted a preliminary experiment to gauge the main ECG features for stress detection during rest.

1) Background: Some people with severe disabilities are confined in a state in which communication is virtually impossible, being reduced to communicating with their eyes or using sophisticated systems that translate thoughts into words. EyeTrackers and BCI are suitable systems for those people but their main drawback is their cost. More affordable devices are capable of detecting voluntary blinks and translating them into a binary signal that allows the selection, for example, of an ideogram on a communication board. 2) Method: We tested four different systems based on infrared, bioelectrical signals (EOG and EEG) and video processing. Experiments were performed by people with/without disabilities and analyzed the systems’ performances, usability and method of voluntary blinking (long blinks or sequence of two short blinks). 3) Results: The best accuracy (99.3%) was obtained using IR-OG and the worst with the EEG headset (85.9%) and there was a statistical influence of the technology on accuracy. Regarding the method of voluntary blinking, the use of long or double blinks had no statistical influence on accuracy, excluding EOG, and the time taken to perform double blinks was shorter, resulting in a potentially much faster interface. People with disabilities obtained similar values but with greater variability. The preferred technology and blinking method were VOG and long blinks respectively. 4) Conclusions: Several OSHW devices have been developed and a new algorithm for detecting voluntary blinks has also been proposed, which outperforms most of the published papers in the reviewed literature.

The analysis of cardiac activity is one of the most common elements for evaluating the state of a subject, either to control possible health risks, sports performance, stress levels, etc. This activity can be recorded using different techniques, with electrocardiogram and photoplethysmogram being the most common. Both techniques make significantly different waveforms, however the first derivative of the photoplethysmographic data produces a signal structurally similar to the electrocardiogram, so any technique focusing on detecting QRS complexes, and thus heartbeats in electrocardiogram, is potentially applicable to photoplethysmogram. In this paper, we develop a technique based on the wavelet transform and envelopes to detect heartbeats in both electrocardiogram and photoplethysmogram. The wavelet transform is used to enhance QRS complexes with respect to other signal elements, while the envelopes are used as an adaptive threshold to determine their temporal location. We compared our approach with three other techniques using electrocardiogram signals from the Physionet database and photoplethysmographic signals from the DEAP database. Our proposal showed better performances when compared to others. When the electrocardiographic signal was considered, the method had an accuracy greater than 99.94%, a true positive rate of 99.96%, and positive prediction value of 99.76%. When photoplethysmographic signals were investigated, an accuracy greater than 99.27%, a true positive rate of 99.98% and positive prediction value of 99.50% were obtained. These results indicate that our proposal can be adapted better to the recording technology.

In this article we present a new low-cost physiological signal acquisition platform in compliance with OSHW philosophy. The device contains an ECG circuit; two EDA channels that implement a time-multiplexing technique to remove inter-channel coupling interference; and a skin temperature module. These features make the platform suitable for experiments involving AC. We have assessed the performance of this device in terms of SNR and BNL, an adapted MPEG7 feature for ECG signals. This platform outperforms other commercial devices and most similar state-of-art studies on SNR. Results show values of 11.96, 30.01, 68.40 dB for ECG, EDA and T respectively for SNR, and -28.31 dB for BNL in ECG. Both features were obtained by sampling physiological signals at a frequency of 256 Hz.

This paper is the first of a two-part study aiming at building a low-cost visible-light eye tracker (ET) for people with amyotrophic lateral sclerosis (ALS). The whole study comprises several phases: (1) analysis of the scientific literature, (2) selection of the studies that better fit the main goal, (3) building the ET, and (4) testing with final users. This document basically contains the two first phases, in which more than 500 studies, from different scientific databases (IEEE Xplore, Scopus, SpringerLink, etc.), fulfilled the inclusion criteria, and were analyzed following the guidelines of a scoping review. Two researchers screened the searching results and selected 44 studies (-value = 0.86, Kappa Statistic). Three main methods (appearance-, feature- or model- based) were identified for visible-light ETs, but none significantly outperformed the others according to the reported accuracy -p = 0.14, Kruskal–Wallis test (KW)-. The feature-based method is abundant in the literature, although the number of appearance-based studies is increasing due to the use of deep learning techniques. Head movements worsen the accuracy in ETs, and only a very few numbers of studies considered the use of algorithms to correct the head pose. Even though head movements seem not to be a big issue for people with ALS, some slight head movements might be enough to worsen the ET accuracy. For this reason, only studies that did not constrain the head movements with a chinrest were considered. Five studies fulfilled the selection criteria with accuracies less than $$2^{\circ }$$2 ∘ , and one of them is illuminance invariant.

A typical routine in many scientific studies consists of recording data from devices and identifying which segment of data corresponds with an experimental interval. However, many current applications have been designed to obtain and save data from one single device, and synchronizing data with the markers that delimit the test phases can be difficult. To address this issue, we have developed LSRec, which is based on Lab-Streaming Layer, a C++ library that allows data synchronization. LSRec is an easy-to-use, open-source, multi-platform, recording system developed on Java that can save data from several devices at the same time, while maintaining synchronization with the experimental phase markers. It supports three explicit sync methods: the first uses one-integer-channel input Lab-Streaming Layer streams. The others use TCP/UDP socket messages and allow any existing software that generates sync markers through TCP/UDP messages to be used under the same conditions. In LSRec, the markers are saved together with input data, facilitating the process of linking data with test stages. A prerequisite for recording software is to guarantee suitable timing performance, with no data loss and an easy user interface. These features were assessed for LSRec, with no data loss detected for 20 hours (25 sessions). We evaluated performance by measuring timestamp deviations for sync marker and input data. The sync methods exhibited an average of deviations of around –34μs (which is more than acceptable, e.g., in studies involving living beings), whereas the absolute value of deviation for one-channel data was lower than 40μs. Finally, we assessed the system’s usability with the technology acceptance model 3 survey (6 volunteers). Subjects saw the software as useful, and intended to use it in the future. In conclusion, LSRec is a useful tool for those who need to record data from multiple sources, and maintain synchronization with experimental phases with low delay.