Toward a Brain-Computer Interface- and Internet of Things-Based Smart Ward Collaborative System Using Hybrid Signals

Abstract

This study proposes a brain-computer interface (BCI)- and Internet of Things (IoT)-based smart ward collaborative system using hybrid signals. The system is divided into hybrid asynchronous electroencephalography (EEG)-, electrooculography (EOG)- and gyro-based BCI control system and an IoT monitoring and management system. The hybrid BCI control system proposes a GUI paradigm with cursor movement. The user uses the gyro to control the cursor area selection and uses blink-related EOG to control the cursor click. Meanwhile, the attention-related EEG signals are classified based on a support-vector machine (SVM) to make the final judgment. The judgment of the cursor area and the judgment of the attention state are reduced, thereby reducing the false operation rate in the hybrid BCI system. The accuracy in the hybrid BCI control system was 96.65 ± 1.44%, and the false operation rate and command response time were 0.89 ± 0.42 events/min and 2.65 ± 0.48 s, respectively. These results show the application potential of the hybrid BCI control system in daily tasks. In addition, we develop an architecture to connect intelligent things in a smart ward based on narrowband Internet of Things (NB-IoT) technology. The results demonstrate that our system provides superior communication transmission quality.

Automation of diagnosis, stratification, and treatment of the paroxysmal sympathetic hyperactivity syndrome in the smart ward environment

“…Smart wards, as a rule, combine the functionality of a smart home and a medical cyber-physical system, which differ in the range of tasks performed, additional requirements and restrictions. For example, traditional human-machine interaction interfaces may not be available for patients with disabilities [7], but interaction scenarios with a smart ward can be implemented using wearable devices [8]. A number of scenarios for the functioning of medical cyber-physical systems in the interaction of patients and medical personnel are considered in the article [9].…”

“…Additional diagnostic criteria for PSH.Based on the sum of the scores of the mandatory and additional criteria, a decision is made on the possibility of stating a PSH diagnosis: unlikely (less than 8 points); possible(8)(9)(10)(11)(12)(13)(14)(15)(16); probably (more than 17). The technique is shown in Figure1.…”