ADVANTAGES AND OBSTACLES OF APPLYING PHYSIOLOGICAL COMPUTING IN REAL WORLD: LESSONS LEARNED FROM SIMULATOR BASED MARITIME TRAINING

While simulator based maritime training is widely implemented under international maritime organization (IMO) convention and model courses, troublesome issues such as objective evaluation of training effectiveness remain unsolved. Physiological computing system (PhyCS) refers to an innovative bidirectional human computer interaction which is achieved by monitoring, analysing, and responding to operators’ psychophysiological activities in real-time. With the development of wearable devices, it becomes promising to apply PhyCS, which was considered as a laboratory technology, in real-world scenarios. In our experience utilizing view tracker, portable heart beat sensor, electroencephalogram device, and web-cameras in simulator based maritime training, PhyCS shows potential for advanced applications in operator performance assessment, usability tests, and adaptive training. However, ambulatory working environment, body movement artefact, and model verification are intricate obstacles that constrain its applications in the real world. By examining the advantages and obstacles, this paper aims to develop guidelines to apply PhyCS in the real-world.

Magnetic Field Mapping and Correction for Moving OP-MEG

Background: Optically pumped magnetometers (OPMs) have made moving, wearable magnetoencephalography (MEG) possible. The OPMs typically used for MEG require a low background magnetic field to operate, which is achieved using both passive and active magnetic shielding. However, the background magnetic field is never truly zero Tesla, and so the field at each of the OPMs changes as the participant moves. This leads to position and orientation dependent changes in the measurements, which manifest as low frequency artefacts in MEG data. Objective: We modelled the spatial variation in the magnetic field and used the model to predict the movement artefact found in a dataset. Methods: We demonstrate a method for modelling this field with a triaxial magnetometer, then showed that we can use the same technique to predict the movement artefact in a real OPM-based MEG (OP-MEG) dataset. Results: Using an 86-channel OP-MEG system, we found that this modelling method maximally reduced the power spectral density of the data by 26.2 ± 0.6 dB at 0 Hz, when applied over 5 s non-overlapping windows. Conclusion: The magnetic field inside our state-of-the art magnetically shielded room can be well described by low-order spherical harmonic functions. We achieved a large reduction in movement noise when we applied this model to OP-MEG data. Significance: Real-time implementation of this method could reduce passive shielding requirements for OP-MEG recording and allow the measurement of low-frequency brain activity during natural participant movement.

Validation of the modified Microlife blood pressure monitor in patients with paroxysmal atrial fibrillation

Aims Undiagnosed atrial fibrillation (AF) accounts for 6% of all strokes, therefore early detection and treatment of the arrhythmia are paramount. Previous research has illustrated that the Microlife WatchBPO3 AFIB, an automated blood pressure (BP) monitor with an inbuilt AF algorithm, accurately detects permanent AF. Currently, limited data exist on whether the modified BP monitor is able to detect paroxysmal AF (PAF). Therefore, this study aims to assess the accuracy of the Microlife WatchBPO3 AFIB monitor to detect PAF against a pacemaker reference standard over a 24-h period. Methods and results Forty-eight patients with a pacemaker implanted for sick sinus syndrome and previously documented fast AF participated. Sensitivity of the atrial pacemaker lead was set to allow detection of signals of ≥ 0.5 mV. Patients engaged in their normal daily routine whilst wearing the modified BP monitor. The modified BP monitor demonstrated an overall sensitivity of 76.0% and specificity of 80.8% for detecting PAF. This sensitivity and specificity increased to 100% and 83.1%, respectively, for patients that achieved more than 80% successful BP readings. Compared to day-time readings, night-time readings also demonstrated a lower proportion of movement artefact (14.4% vs. 3.4%), and therefore, a higher sensitivity and specificity of 100% and 84.9%, respectively, for detecting PAF. Conclusion The Microlife WatchBPO3 AFIB device has an acceptable diagnostic accuracy to detect PAF; however, movement artefact affects the accuracy of the readings. This modified BP monitor may potentially be useful as a screening tool for AF in patients at high risk of developing stroke.

EEG for the assessment of neurological function in newborn infants immediately after birth

ObjectiveTo assess the neurological function of newborn infants in the first minutes after birth using EEG.Design and patientsWe obtained electroencephalography (EEG) recordings in term infants following elective caesarean section. After delivery, disposable EEG electrodes were attached to the infants’ scalp over the frontal and central regions bilaterally and EEG was recorded for 10 min. Both visual and quantitative measures were used to analyse the EEGs.SettingThe operative delivery theatre of Cork University Maternity Hospital, Ireland.ResultsForty-nine infants had EEG recordings over the frontal and central regions. The median (IQR) age at time of initial EEG recording was 3.0 (2.5–3.8) min. While movement artefact contaminated parts of many recordings, good-quality EEG, with mixed-frequency activity with a range of 25–50 μV, was observed in all infants. The majority of EEG spectral power was within the delta band: the median (IQR) relative delta power was 87.8% (83.7%–90%). Almost all (95%) spectral power was below a median (IQR) of 7.56 Hz (6.17–9.76 Hz).ConclusionsEEG recording is very feasible in the immediate newborn period. This study provides valuable objective information about neurological function during this transitional period.

Advantages and Obstacles of Applying Physiological Computing in Real World: Lessons Learned from Simulator Based Maritime Training

While simulator based maritime training is widely implemented under international maritime organization (IMO) convention and model courses, troublesome issues such as objective evaluation of training effectiveness remain unsolved. Physiological computing system (PhyCS) refers to an innovative bidirectional human computer interaction which is achieved by monitoring, analysing, and responding to operators’ psychophysiological activities in real-time. With the development of wearable devices, it becomes promising to apply PhyCS, which was considered as a laboratory technology, in real-world scenarios. In our experience utilizing view tracker, portable heart beat sensor, electroencephalogram device, and web-cameras in simulator based maritime training, PhyCS shows potential for advanced applications in operator performance assessment, usability tests, and adaptive training. However, ambulatory working environment, body movement artefact, and model verification are intricate obstacles that constrain its applications in the real world. By examining the advantages and obstacles, this paper aims to develop guidelines to apply PhyCS in the real-world.