This work presents a novel Deep Autoencoder neural network which is developed for removing EOG, EMG, and motion artifacts from single-channel EEG signal. The proposed algorithm has been demonstrated to have a desirable perfromance on EEG artifact removal. More importantly, this model has been also implemented into Android smartphone fo the first time via TensorFlow Lite Library, with a fast signal processing speed after enabling hardware/software acceleration on smartphones, which outperforms the gold standard ICA algorithm from the perspectives of computation speed and power consumption, showing promising applications in future mobile EEG and Brain-Computer Interfaces.

Light exposure is a vital regulator of physiology and behaviour in humans. However, monitoring of light exposure is not included in current wearable Internet-of-Things (IoT) devices, and only recently have international standards defined alpha-opic equivalent daylight illuminance measures for how the eye responds to light. This paper reports a wearable light sensor node that can be incorporated into the IoT to provide monitoring of equivalent daylight illuminance exposure in real-world settings. We present the system design, electronic performance testing, and accuracy of equivalent daylight illuminance measurements when compared to a calibrated spectral source. This includes consideration of the directional response of the sensor, and a comparison of performance when placed on different parts of the body, and a demonstration of practical use over 7 days. Our device operates for 3.5 days between charges, with a sampling period of 30 s. It has 10 channels of measurement, over the range 415-910 nm, balancing accuracy and cost considerations. Measured alpha-opic Equivalent Daylight Illuminance results for 13 devices show a mean absolute error of less than 0.07 log lx, and a minimum between device correlation of 0.99. These findings demonstrate that accurate light sensing is feasible, including at wrist worn locations. We provide an experimental platform for use in future investigations in real-world light exposure monitoring and IoT based lighting control.

Electroencephalography (EEG) is a widely used non-invasive brain monitoring technique that records the electrical signals generated within the brain, with applications ranging from epilepsy to Brain-Computer Interfaces (BCI). The electrode connecting the EEG instrumentation to the user’s scalp is a key part of this system which determines the overall performance. Traditionally, disc electrodes, or fingered electrodes to pass through hair, have been used, but with a very limited number of sizes and shapes available which do not reflect all users and head-hair types. Recently, 3D-printed electrodes have been proposed for allowing personalized manufacturing and more inclusive EEG. Current 3D-printed electrodes can be physically flexible for comfort, and allow recording without a conductive gel being added. However, they are formed by printing a base structure which is then coated with Silver/Silver-Chloride to make it suitable for non-invasive brain recording. This paper presents novel 3D-printed EEG electrodes with that can be made using a directly conductive flexible filament. The resulting electrodes are gel free, coating free, can be personalized, have reduced manufacturing time, and cost less compared to previous electrodes. Our electrodes are characterized in terms of contact impedance, contact noise, on-phantom signal recording, mechanical strength, and the recording of Steady-State Visual Evoked Potentials (SSVEPs) from volunteers. They have much higher contact impedance present compared to Silver/Silver-Chloride coated electrodes, resulting in higher contact noise and more susceptibility to motion artifacts, but offer a wide range of benefits for low cost personalized electroencephalography.

Energy harvesting from human motion can reduce reliance on battery recharging in wearable devices and lead to improved adherence. However, to date, studies estimating energy harvesting potential have largely focused on small scale, healthy, population groups in laboratory settings rather than free-living environments with population level participant numbers. Here, we present the largest scale investigation into energy harvesting potential by utilising the activity data collected in the UK Biobank from over 67,000 participants. This paper presents detailed stratification into how the day of the week and participant age affect harvesting potential, as well as how the presence of conditions (such as diabetes, which we investigate here), may affect the expected energy harvester output. We process accelerometery data using a kinetic energy harvester model to investigate power output at a high temporal resolution. Our results identify key differences between the times of day when the power is available and an inverse relationship between power output and participant age. We also identify that the presence of diabetes substantially reduces energy harvesting output, by over 21%. The results presented highlight a key challenge in wearable energy harvesting: that wearable devices aim to monitor health and wellness, and energy harvesting aims to make devices more energy autonomous, but the presence of medical conditions may lead to substantially lower energy harvesting potential. The findings indicate how it is challenging to meet the required power budget to monitor diseases when energy autonomy is a goal.

This paper presents a new active electrode design for electroencephalogram (EEG) and electrocardiogram (ECG) sensors based on inertial measurement units to remove motion artefacts during signal acquisition. Rather than measuring motion data from a single source for the entire recording unit, inertial measurement units are attached to each individual EEG or ECG electrode to collect local movement data. This data is then used to remove the motion artefact by using normalised least mean square adaptive filtering. Results show that the proposed active electrode design can reduce motion contamination from EEG and ECG signals in chest movement and head swinging motion scenarios. However, it is found that the performance varies, necessitating the need for the algorithm to be paired with more sophisticated signal processing to identify scenarios where it is beneficial in terms of improving signal quality. The new instrumentation hardware allows data driven artefact removal to be performed, providing a new data driven approach compared to widely used blind-source separation methods, and helps enable in the wild EEG recordings to be performed.