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Fibromyalgia Syndrome (FMS), Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) and Long COVID (LC) are similar multisymptom clinical syndromes but with difference in dominant symptoms in each individual. There is existing and emerging literature on possible functional alterations of the central nervous system in these conditions. This review aims to synthesise and appraise the literature on resting-state quantitative EEG (qEEG) in FMS, ME/CFS and LC, drawing on previous research on FMS and ME/CFS to help understand neuropathophysiology of the new condition LC. A systematic search of MEDLINE, Embase, CINHAL, PsycINFO and Web of Science databases for articles published between December 1994 and September 2023 was performed. Out of the initial 2510 studies identified, 17 articles were retrieved that met all the predetermined selection criteria, particularly of assessing qEEG changes in one of the three conditions compared to healthy controls. All studies scored moderate to high quality on the Newcastle-Ottawa scale. There was a general trend for decreased low-frequency EEG band activity (delta, theta, and alpha) and increased high-frequency EEG beta activity in FMS, differing to that found in ME/CFS. The limited LC studies included in this review focused mainly on cognitive impairments and showed mixed findings not consistent with patterns observed in FMS and ME/CFS. Our findings suggest different patterns of qEEG brainwave activity in FMS and ME/CFS. Further research is required to explore whether there are phenotypes within LC that have EEG signatures similar to FMS or ME/CFS. This could inform identification of reliable diagnostic markers and possible targets for neuromodulation therapies tailored to each clinical syndrome., (Copyright © 2024 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.)

Objective: To remove signal contamination in electroencephalogram (EEG) traces coming from ocular, motion, and muscular artifacts which degrade signal quality. To do this in real-time, with low computational overhead, on a mobile platform in a channel count independent manner to enable portable Brain-Computer Interface (BCI) applications., Methods: We propose a Deep AutoEncoder (DAE) neural network for single-channel EEG artifact removal, and implement it on a smartphone via TensorFlow Lite. Delegate based acceleration is employed to allow real-time, low computational resource operation. Artifact removal performance is quantified by comparing corrupted and ground-truth clean EEG data from public datasets for a range of artifact types. The on-phone computational resources required are also measured when processing pre-saved data., Results: DAE cleaned EEG shows high correlations with ground-truth clean EEG, with average Correlation Coefficients of 0.96, 0.85, 0.70 and 0.79 for clean EEG reconstruction, and EOG, motion, and EMG artifact removal respectively. On-smartphone tests show the model processes a 4 s EEG window within 5 ms, substantially outperforming a comparison FastICA artifact removal algorithm., Conclusion: The proposed DAE model shows effectiveness in single-channel EEG artifact removal. This is the first demonstration of a low-computational resource deep learning model for mobile EEG in smartphones with hardware/software acceleration., Significance: This work enables portable BCIs which require low latency real-time artifact removal, and potentially operation with a small number of EEG channels for wearability. It makes use of the artificial intelligence accelerators found in modern smartphones to improve computational performance compared to previous artifact removal approaches.

Light exposure is a vital regulator of physiology and behavior 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 [Formula: see text] -optic equivalent daylight illuminance (EDI) measures for how the eye responds to light. This article reports a wearable light sensor node that can be incorporated into the IoT to provide monitoring of EDI exposure in real-world settings. We present the system design, electronic performance testing, and accuracy of EDI 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 [Formula: see text]-opic EDI 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., (2327-4662 © 2024 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See https://www.ieee.org/publications/rights/index.html for more information.)

Rapidly intensifying global warming and water pollution calls for more efficient and continuous environmental monitoring methods. Biohybrid systems connect mechatronic components to living organisms and this approach can be used to extract data from the organisms. Compared to conventional monitoring methods, they allow for a broader data collection over long periods, minimizing the need for sampling processes and human labour. We aim to develop a methodology for creating various bioinspired entities, here referred to as 'biohybrids', designed for long-term aquatic monitoring. Here, we test several aspects of the development of the biohybrid entity: autonomous power source, lifeform integration and partial biodegradability. An autonomous power source was supplied by microbial fuel cells which exploit electron flows from microbial metabolic processes in the sediments. Here, we show that by stacking multiple cells, sufficient power can be supplied. We integrated lifeforms into the developed bioinspired entity which includes organisms such as the zebra mussel Dreissena polymorpha and water flea Daphnia spp. The setups developed allowed for observing their stress behaviours. Through this, we can monitor changes in the environment in a continuous manner. The further development of this approach will allow for extensive, long-term aquatic data collection and create an early-warning monitoring system., (Creative Commons Attribution license.)

Experimental and interventional studies show that light can regulate sleep timing and sleepiness while awake by setting the phase of circadian rhythms and supporting alertness. The extent to which differences in light exposure explain variations in sleep and sleepiness within and between individuals in everyday life remains less clear. Here, we establish a method to address this deficit, incorporating an open-source wearable wrist-worn light logger (SpectraWear) and smartphone-based online data collection. We use it to simultaneously record longitudinal light exposure (in melanopic equivalent daylight illuminance), sleep timing, and subjective alertness over seven days in a convenience sample of 59 UK adults without externally imposed circadian challenge (e.g., shift work or jetlag). Participants reliably had strong daily rhythms in light exposure but frequently were exposed to less light during the daytime and more light in pre-bedtime and sleep episodes than recommended [T. M. Brown et al. , PLoS Biol. 20 , e3001571 (2022)]. Prior light exposure over several hours was associated with lower subjective sleepiness with, in particular, brighter light in the late sleep episode and after wake linked to reduced early morning sleepiness (sleep inertia). Higher pre-bedtime light exposure was associated with longer sleep onset latency. Early sleep timing was correlated with more reproducible and robust daily patterns of light exposure and higher daytime/lower night-time light exposure. Our study establishes a method for collecting longitudinal sleep and health/performance data in everyday life and provides evidence of associations between light exposure and important determinants of sleep health and performance.

International deployment of remote monitoring and virtual care (RMVC) technologies would efficiently harness their positive impact on outcomes. Since Canada and the United Kingdom have similar populations, health care systems, and digital health landscapes, transferring digital health innovations between them should be relatively straightforward. Yet examples of successful attempts are scarce. In a workshop, we identified 6 differences that may complicate RMVC transfer between Canada and the United Kingdom and provided recommendations for addressing them. These key differences include (1) minority groups, (2) physical geography, (3) clinical pathways, (4) value propositions, (5) governmental priorities and support for digital innovation, and (6) regulatory pathways. We detail 4 broad recommendations to plan for sustainability, including the need to formally consider how highlighted country-specific recommendations may impact RMVC and contingency planning to overcome challenges; the need to map which pathways are available as an innovator to support cross-country transfer; the need to report on and apply learnings from regulatory barriers and facilitators so that everyone may benefit; and the need to explore existing guidance to successfully transfer digital health solutions while developing further guidance (eg, extending the nonadoption, abandonment, scale-up, spread, sustainability framework for cross-country transfer). Finally, we present an ecosystem readiness checklist. Considering these recommendations will contribute to successful international deployment and an increased positive impact of RMVC technologies. Future directions should consider characterizing additional complexities associated with global transfer., (©Quynh Pham, David Wong, Kaylen J Pfisterer, Dionne Aleman, Nick Bansback, Joseph A Cafazzo, Alexander J Casson, Brian Chan, William Dixon, Gerasimos Kakaroumpas, Claudia Lindner, Niels Peek, Henry WW Potts, Barbara Ribeiro, Emily Seto, Charlotte Stockton-Powdrell, Alexander Thompson, Sabine van der Veer. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 01.08.2023.)

Aim: To explore the user experiences of pre-sleep alpha entrainment via a smartphone-enabled audio or visual stimulation program for people with chronic pain and sleep disturbance. Materials & methods: Semi-structured interviews were held with 27 participants completing a feasibility study of pre-sleep entrainment use for 4 weeks. Transcriptions were subject to template analysis. Results: Five top-level themes generated from this analysis are presented. These report on participants' impressions of the pain-sleep relationship, their previous experiences of strategies for these symptoms, their expectations and their experience of use and perceived impact on symptoms of audiovisual alpha entrainment. Conclusion: Pre-sleep audiovisual alpha entrainment was acceptable to individuals with chronic pain and sleep disturbance and perceived to have symptomatic benefits.

Introduction: Chronic pain and sleep disturbance are bi-directionally related. Cortical electrical activity in the alpha frequency band can be enhanced with sensory stimulation via the phenomenon of entrainment, and may reduce pain perception. A smartphone based programme which delivers 10 Hz stimulation through flickering light or binaural beats was developed for use at night, pre-sleep, with the aim of improving night time pain and sleep and thereby subsequent pain and related daytime symptoms. The aim of this study was to assess the feasibility and give an indication of effect of this programme for individuals with chronic pain and sleep disturbance., Materials and Methods: In a non-controlled feasibility study participants used audio or visual alpha entrainment for 30 min pre-sleep each night for 4 weeks, following a 1 week baseline period. The study was pre-registered at ClinicalTrials.gov with the ID NCT04176861., Results: 28 participants (79% female, mean age 45 years) completed the study with high levels of data completeness (86%) and intervention adherence (92%). Daily sleep diaries showed an increase compared to baseline in total sleep time of 29 min ( p  = 0.0033), reduction in sleep onset latency of 13 min ( p  = 0.0043), and increase in sleep efficiency of 4.7% ( p  = 0.0009). Daily 0-10 numerical rating scale of average pain at night improved by 0.5 points compared to baseline ( p  = 0.027). Standardised questionnaires showed significant within-participant improvements in sleep quality (change in median Global PSQI from 16 to 12.5), pain interference (change in median BPI Pain Interference from 7.5 to 6.8), fatigue (change in median MFI total score from 82.5 to 77), and depression and anxiety (change in median HADS depression score from 12 to 10.5 and anxiety from 13.5 to 11)., Discussion: Pre-sleep use of a smartphone programme for alpha entrainment by audio or visual stimulation was feasible for individuals with chronic pain and sleep disturbance. The effect on symptoms requires further exploration in controlled studies., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (© 2023 Halpin, Casson, Tang, Jones, O'Connor and Sivan.)

Recent studies have shown that slow oscillations (SOs) can be driven by rhythmic auditory stimulation, which deepens slow-wave sleep (SWS) and improves memory and the immune-supportive hormonal milieu related to this sleep stage. While different attempts have been made to optimise the driving of the SOs by changing the number of click stimulations, no study has yet investigated the impact of applying more than five clicks in a row. Likewise, the importance of the type of sounds in eliciting brain responses is presently unclear. In a study of 12 healthy young participants (10 females; aged 18-26 years), we applied an established closed-loop stimulation method, which delivered sequences of 10 pink noises, 10 pure sounds (B note of 247 Hz), 10 pronounced "a" vowels, 10 sham, 10 variable sounds, and 10 "oddball" sounds on the up phase of the endogenous SOs. By analysing area under the curve, amplitude, and event related potentials, we explored whether the nature of the sound had a differential effect on driving SOs. We showed that every stimulus in a 10-click sequence, induces a SO response. Interestingly, all three types of sounds that we tested triggered SOs. However, pink noise elicited a more pronounced response compared to the other sounds, which was explained by a broader topographical recruitment of brain areas. Our data further suggest that varying the sounds may partially counteract habituation., (© 2022 The Authors. Journal of Sleep Research published by John Wiley & Sons Ltd on behalf of European Sleep Research Society.)

Wearable e-textiles have gained huge tractions due to their potential for non-invasive health monitoring. However, manufacturing of multifunctional wearable e-textiles remains challenging, due to poor performance, comfortability, scalability, and cost. Here, we report a fully printed, highly conductive, flexible, and machine-washable e-textiles platform that stores energy and monitor physiological conditions including bio-signals. The approach includes highly scalable printing of graphene-based inks on a rough and flexible textile substrate, followed by a fine encapsulation to produce highly conductive machine-washable e-textiles platform. The produced e-textiles are extremely flexible, conformal, and can detect activities of various body parts. The printed in-plane supercapacitor provides an aerial capacitance of ∼3.2 mFcm -2 (stability ∼10,000 cycles). We demonstrate such e-textiles to record brain activity (an electroencephalogram, EEG) and find comparable to conventional rigid electrodes. This could potentially lead to a multifunctional garment of graphene-based e-textiles that can act as flexible and wearable sensors powered by the energy stored in graphene-based textile supercapacitors., Competing Interests: The authors declare no competing Interests., (© 2022 The Authors.)

Wearable devices are having a transformative impact on personalised monitoring and care. However, they frequently have limited battery life, requiring charging every few days; a major source of user frustration. Kinetic energy harvesting may help overcome this, collecting energy from the user's motion to allow the device to self-charge. While there are many works which have investigated wearable energy harvesting potential, none have incorporated socio-economic factors which affect activity, such as occupation type, on energy harvesting potential. We use the UK Biobank free-living accelerometer dataset to investigate the impact of occupational patterns on energy harvesting potential for the first time. We identify that those following shift patterns have a different distribution of when power is available, with those who work shifts having the most power intense period spread over a longer period of the day compared to controls. When stratifying into day or night shift work, we identify that those who work night shifts have a large variation between participants, as their most energy dense period is spread over the entire 24-hour period. This is compared to day shift workers who have the most power concentrated within a substantially smaller window, typically in the morning. Considering these socio-economic factors may affect system design of wearable energy harvesters.

Human Activity Recognition (HAR), using machine learning to identify times spent (for example) walking, sitting, and standing, is widely used in health and wellness wearable devices, in ambient assistant living devices, and in rehabilitation. In this paper, a stacked Long Short-Term Memory (LSTM) structure is designed for HAR to be implemented on a smartphone. The use of an edge device for the processing means that the raw collected data does not need to be passed to the cloud for processing, mitigating potential bandwidth, power consumption, and privacy concerns. Our offline prototype model achieves 92.8% classification accuracy when classifying 6 activities using a public dataset. Quantization techniques are shown to reduce the model's weight representations to achieve a >30x model size reduction for improved use on a smartphone. The end result is an on-phone HAR model with accuracy of 92.7% and a memory footprint of 27 KB.

Detecting viruses, which have significant impact on health and the economy, is essential for controlling and combating viral infections. In recent years there has been a focus towards simpler and faster detection methods, specifically through the use of electronic-based detection at the point-of-care. Point-of-care sensors play a particularly important role in the detection of viruses. Tests can be performed in the field or in resource limited regions in a simple manner and short time frame, allowing for rapid treatment. Electronic based detection allows for speed and quantitative detection not otherwise possible at the point-of-care. Such approaches are largely based upon voltammetry, electrochemical impedance spectroscopy, field effect transistors, and similar electrical techniques. Here, we systematically review electronic and electrochemical point-of-care sensors for the detection of human viral pathogens. Using the reported limits of detection and assay times we compare approaches both by detection method and by the target analyte of interest. Compared to recent scoping and narrative reviews, this systematic review which follows established best practice for evidence synthesis adds substantial new evidence on 1) performance and 2) limitations, needed for sensor uptake in the clinical arena. 104 relevant studies were identified by conducting a search of current literature using 7 databases, only including original research articles detecting human viruses and reporting a limit of detection. Detection units were converted to nanomolars where possible in order to compare performance across devices. This approach allows us to identify field effect transistors as having the fastest median response time, and as being the most sensitive, some achieving single-molecule detection. In general, we found that antigens are the quickest targets to detect. We also observe however, that reports are highly variable in their chosen metrics of interest. We suggest that this lack of systematisation across studies may be a major bottleneck in sensor development and translation. Where appropriate, we use the findings of the systematic review to give recommendations for best reporting practice., Competing Interests: The authors have declared that no competing interests exist.

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., (© 2021 The Authors. Healthcare Technology Letters published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.)

Wearable and mobile technology provides new opportunities to manage health conditions remotely and unobtrusively. For example, healthcare providers can repeatedly sample a person's condition to monitor progression of symptoms and intervene if necessary. There is usually a utility-tolerability trade-off between collecting information at sufficient frequencies and quantities to be useful, and over-burdening the user or the underlying technology, particularly when active input is required from the user. Selecting the next sampling time adaptively using previous responses, so that people are only sampled at high frequency when necessary, can help to manage this trade-off. We present a novel approach to adaptive sampling using clustered continuous-time hidden Markov models. The model predicts, at any given sampling time, the probability of moving to an 'alert' state, and the next sample time is scheduled when this probability has exceeded a given threshold. The clusters, each representing a distinct sub-model, allow heterogeneity in states and state transitions. The work is illustrated using longitudinal mental-health symptom data in 49 people collected using ClinTouch, a mobile app designed to monitor people with a diagnosis of schizophrenia. Using these data, we show how the adaptive sampling scheme behaves under different model parameters and risk thresholds, and how the average sampling can be substantially reduced whilst maintaining a high sampling frequency during high-risk periods.

One-third of the population in the UK and worldwide struggle with chronic pain. Entraining brain alpha activity through noninvasive visual stimulation has been shown to reduce experimental pain in healthy volunteers. Neural oscillations entrainment offers a potential noninvasive and nonpharmacological intervention for patients with chronic pain, which can be delivered in the home setting and has the potential to reduce use of medications. However, evidence supporting its use in patients with chronic pain is lacking. This study explores whether (a) alpha entrainment increase alpha power in patients and (b) whether this increase in alpha correlates with analgesia. In total, 28 patients with chronic pain sat in a comfortable position and underwent 4-min visual stimulation using customised goggles at 10 Hz (alpha) and 7 Hz (control) frequency blocks in a randomised cross-over design. 64-channel electroencephalography and 11-point numeric rating scale pain intensity and pain unpleasantness scores were recorded before and after stimulation. Electroencephalography analysis revealed frontal alpha power was significantly higher when stimulating at 10 Hz when compared to 7 Hz. There was a significant positive correlation between increased frontal alpha and reduction in pain intensity (r = 0.33; P < 0.05) and pain unpleasantness (r = 0.40; P < 0.05) in the 10 Hz block. This study provides the first proof of concept that changes in alpha power resulting from entrainment correlate with an analgesic response in patients with chronic pain. Further studies are warranted to investigate dose-response parameters and equivalence to analgesia provided by medications., (Copyright © 2021 Wolters Kluwer Health, Inc. All rights reserved.)

Diabetic foot ulcers (DFUs) are a life-changing complication of diabetes that can lead to amputation. There is increasing evidence that long-term management with wearables can reduce incidence and recurrence of this condition. Temperature asymmetry measurements can alert to DFU development, but measurements of dynamic information, such as rate of temperature change, are under investigated. We present a new wearable device for temperature monitoring at the foot that is personalised to account for anatomical variations at the foot. We validate this device on 13 participants with diabetes (no neuropathy) (group name D) and 12 control participants (group name C), during sitting and standing. We extract dynamic temperature parameters from four sites on each foot to compare the rate of temperature change. During sitting the time constant of temperature rise after shoe donning was significantly ( p < 0.05) faster at the hallux ( p = 0.032, 370.4 s (C), 279.1 s (D)) and 5th metatarsal head ( p = 0.011, 481.9 s (C), 356.6 s (D)) in participants with diabetes compared to controls. No significant differences at the other sites or during standing were identified. These results suggest that temperature rise time is faster at parts of the foot in those who have developed diabetes. Elevated temperatures are known to be a risk factor of DFUs and measurement of time constants may provide information on their development. This work suggests that temperature rise time measured at the plantar surface may be an indicative biomarker for differences in soft tissue biomechanics and vascularisation during diabetes onset and progression.

Objective: Alpha-neurofeedback (α-NFB) is a novel therapy which trains individuals to volitionally increase their alpha power to improve pain. Learning during NFB is commonly measured using static parameters such as mean alpha power. Considering the biphasic nature of alpha rhythm (high and low alpha), dynamic parameters describing the time spent by individuals in high alpha state and the pattern of transitioning between states might be more useful. Here, we quantify the changes during α-NFB for chronic pain in terms of dynamic changes in alpha states., Methods: Four chronic pain and four healthy participants received five NFB sessions designed to increase frontal alpha power. Changes in pain resilience were measured using visual analogue scale (VAS) during repeated cold-pressor tests (CPT). Changes in alpha state static and dynamic parameters such as fractional occupancy (time in high alpha state), dwell time (length of high alpha state) and transition probability (probability of moving from low to high alpha state) were analyzed using Friedman's Test and correlated with changes in pain scores using Pearson's correlation., Results: There was no significant change in mean frontal alpha power during NFB. There was a trend of an increase in fractional occupancy, mean dwell duration and transition probability of high alpha state over the five sessions in chronic pain patients only. Significant correlations were observed between change in pain scores and fractional occupancy ( r = -0.45, p = 0.03), mean dwell time ( r = -0.48, p = 0.04) and transition probability from a low to high state ( r = -0.47, p = 0.03) in chronic pain patients but not in healthy participants., Conclusion: There is a differential effect between patients and healthy participants in terms of correlation between change in pain scores and alpha state parameters. Parameters providing a more precise description of the alpha power dynamics than the mean may help understand the therapeutic effect of neurofeedback on chronic pain., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Patel, Henshaw, Sutherland, Taylor, Casson, Lopez-Diaz, Brown, Jones, Sivan and Trujillo-Barreto.)

Background and Objective: Neurofeedback (NFB) provides real-time feedback about neurophysiological signals to patients, thereby encouraging modulation of pain-associated brain activity. This review aims to evaluate the effectiveness and safety of NFB in alleviating pain and pain-associated symptoms in chronic pain patients., Methods: MEDLINE, PUBMED, Web of Science and PsycINFO databases were searched using the strategy: ("Neurofeedback" OR "EEG Biofeedback" OR "fMRI Biofeedback") AND ("Pain" or "Chronic Pain"). Clinical trials reporting changes in pain following electroencephalogram (EEG) or functional magnetic resonance imaging (fMRI) NFB in chronic pain patients were included. Only Randomized-controlled trials (RCT), non-randomized controlled trials (NRCT) and case series were included. Effect size was pooled for all RCTs in a meta-analysis., Results: Twenty-one studies were included. Reduction in pain following NFB was reported by one high-quality RCT, five of six low-quality RCT or NRCT and 13 of 14 case-series. Pain reduction reported by studies ranged from 6% to 82%, with 10 studies reporting a clinically significant reduction in pain of >30%. The overall effect size was medium (cohen's d -0.76, 95% confidence interval -1.31 to -0.20). Studies were highly heterogeneous (Q [df = 5] = 18.46, p = .002, I 2  = 73%). Improvements in depression, anxiety, fatigue and sleep were also seen in some studies. Common side-effects included headache, nausea and drowsiness. These generally did not lead to withdrawal of therapy except in one study., Conclusions: Neurofeedback is a safe and effective therapy with promising but largely low-quality evidence supporting its use in chronic pain. Further high-quality trials comparing different protocols is warranted to determine the most efficacious way to deliver NFB., Significance: Neurofeedback is a novel neuromodulatory approach which can be used to reduce the severity of pain and pain-associated symptoms such as sleep disturbances, mood disturbances, fatigue and anxiety in a number of chronic pain conditions. It has a potential to provide integrative non-pharmacological management for chronic pain patients with pain refractory to pharmacological agents with high side-effect profiles. Further high-quality double-blinded randomized sham-controlled trials are needed in order to fully explore the potential of this therapy., (© 2020 The Authors. European Journal of Pain published by John Wiley & Sons Ltd on behalf of European Pain Federation EFIC ®.)

Entraining alpha activity with rhythmic visual, auditory, and electrical stimulation can reduce experimentally induced pain. However, evidence for alpha entrainment and pain reduction in patients with chronic pain is limited. This feasibility study investigated whether visual alpha stimulation can increase alpha power in patients with chronic musculoskeletal pain and, secondarily, if chronic pain was reduced following stimulation. In a within-subject design, 20 patients underwent 4-min periods of stimulation at 10 Hz (alpha), 7 Hz (high-theta, control), and 1 Hz (control) in a pseudo-randomized order. Patients underwent stimulation both sitting and standing and verbally rated their pain before and after each stimulation block on a 0-10 numerical rating scale. Global alpha power was significantly higher during 10 Hz compared to 1 Hz stimulation when patients were standing ( t = -6.08, p < 0.001). On a more regional level, a significant increase of alpha power was found for 10 Hz stimulation in the right-middle and left-posterior region when patients were sitting. With respect to our secondary aim, no significant reduction of pain intensity and unpleasantness was found. However, only the alpha stimulation resulted in a minimal clinically important difference in at least 50% of participants for pain intensity (50%) and unpleasantness ratings (65%) in the sitting condition. This study provides initial evidence for the potential of visual stimulation as a means to enhance alpha activity in patients with chronic musculoskeletal pain. The brief period of stimulation was insufficient to reduce chronic pain significantly. This study is the first to provide evidence that a brief period of visual stimulation at alpha frequency can significantly increase alpha power in patients with chronic musculoskeletal pain. A further larger study is warranted to investigate optimal dose and individual stimulation parameters to achieve pain relief in these patients., (Copyright © 2020 Arendsen, Henshaw, Brown, Sivan, Taylor, Trujillo-Barreto, Casson and Jones.)

This paper presents a smartphone based system for presenting light and sound stimulation to a user for neuromodulation experiments. A smartphone platform was used to increase ease of use and enable out-of-the-clinic experiments. The created Android app provides both visual and auditory entrainment stimuli, along with a real-time extraction of ongoing electroencephalogram (EEG) phase using a Phase Locked Loop (PLL) for enabling closed loop simulations. Both visual and auditory stimulation is provided accurately in the 1-20 Hz range using the smartphone class hardware. Test results for both gave R squared fit values of 1 between a fitted line and the measured stimulation frequencies. Data from 13 subjects showed the PLL could track EEG phase in the slow oscillation band with an error of 14.83±38.47°, with an average of 0.04±0.20 ms processing overhead per sample.

Parkinson's disease is diagnosed based on expert clinical observation of movements. One important clinical feature is decrement, whereby the range of finger motion decreases over the course of the observation. This decrement has been assumed to be linear but has not been examined closely.We previously developed a method to extract a time series representation of a finger-tapping clinical test from 137 smart- phone video recordings. Here, we show how the signal can be processed to visualize archetypal progression of decrement. We use k-means with features derived from dynamic time warping to compare similarity of time series. To generate the archetypal time series corresponding to each cluster, we apply both a simple arithmetic mean, and dynamic time warping barycenter averaging to the time series belonging to each cluster.Visual inspection of the cluster-average time series showed two main trends. These corresponded well with participants with no bradykinesia and participants with severe bradykinesia. The visualizations support the concept that decrement tends to present as a linear decrease in range of motion over time.Clinical relevance- Our work visually presents the archetypal types of bradykinesia amplitude decrement, as seen in the Parkinson's finger-tapping test. We found two main patterns, one corresponding to no bradykinesia, and the other showing linear decrement over time.

Study Objectives: Closed-loop auditory stimulation (CLAS) is a method for enhancing slow oscillations (SOs) through the presentation of auditory clicks during sleep. CLAS boosts SOs amplitude and sleep spindle power, but the optimal timing for click delivery remains unclear. Here, we determine the optimal time to present auditory clicks to maximize the enhancement of SO amplitude and spindle likelihood., Methods: We examined the main factors predicting SO amplitude and sleep spindles in a dataset of 21 young and 17 older subjects. The participants received CLAS during slow-wave-sleep in two experimental conditions: sham and auditory stimulation. Post-stimulus SOs and spindles were evaluated according to the click phase on the SOs and compared between and within conditions., Results: We revealed that auditory clicks applied anywhere on the positive portion of the SO increased SO amplitudes and spindle likelihood, although the interval of opportunity was shorter in the older group. For both groups, analyses showed that the optimal timing for click delivery is close to the SO peak phase. Click phase on the SO wave was the main factor determining the impact of auditory stimulation on spindle likelihood for young subjects, whereas for older participants, the temporal lag since the last spindle was a better predictor of spindle likelihood., Conclusions: Our data suggest that CLAS can more effectively boost SOs during specific phase windows, and these differ between young and older participants. It is possible that this is due to the fluctuation of sensory inputs modulated by the thalamocortical networks during the SO., (© Sleep Research Society 2019. Published by Oxford University Press on behalf of the Sleep Research Society.)

Autism is a developmental condition currently identified by experts using observation, interview, and questionnaire techniques and primarily assessing social and communication deficits. Motor function and movement imitation are also altered in autism and can be measured more objectively. In this study, motion and eye tracking data from a movement imitation task were combined with supervised machine learning methods to classify 22 autistic and 22 non-autistic adults. The focus was on a reliable machine learning application. We have used nested validation to develop models and further tested the models with an independent data sample. Feature selection was aimed at selection stability to assure result interpretability. Our models predicted diagnosis with 73% accuracy from kinematic features, 70% accuracy from eye movement features and 78% accuracy from combined features. We further explored features which were most important for predictions to better understand movement imitation differences in autism. Consistent with the behavioural results, most discriminative features were from the experimental condition in which non-autistic individuals tended to successfully imitate unusual movement kinematics while autistic individuals tended to fail. Machine learning results show promise that future work could aid in the diagnosis process by providing quantitative tests to supplement current qualitative ones.

This study investigated whether reduced visual attention to an observed action might account for altered imitation in autistic adults. A total of 22 autistic and 22 non-autistic adults observed and then imitated videos of a hand producing sequences of movements that differed in vertical elevation while their hand and eye movements were recorded. Participants first performed a block of imitation trials with general instructions to imitate the action. They then performed a second block with explicit instructions to attend closely to the characteristics of the movement. Imitation was quantified according to how much participants modulated their movement between the different heights of the observed movements. In the general instruction condition, the autistic group modulated their movements significantly less compared to the non-autistic group. However, following instructions to attend to the movement, the autistic group showed equivalent imitation modulation to the non-autistic group. Eye movement recording showed that the autistic group spent significantly less time looking at the hand movement for both instruction conditions. These findings show that visual attention contributes to altered voluntary imitation in autistic individuals and have implications for therapies involving imitation as well as for autistic people's ability to understand the actions of others.

The millimeter-wave band is an ideal part of the electromagnetic radiation to diagnose human skin conditions because this radiation interacts only with tissue down to a depth of a millimetre or less over the band range from 30 GHz to 300 GHz. In this paper, radiometry is used as a non-contact sensor for measuring the human skin reflectance under normal and wet skin conditions. The mean reflectance of the skin of a sample of 50 healthy participants over the (80-100) GHz band was found to be ~0.615 with a standard deviation of ~0.088, and an experimental measurement uncertainty of ±0.005. The thinner skin regions of the back of the hand, the volar forearms and the inner wrist had reflectances 0.068, 0.068 and 0.062 higher than the thicker skin regions of the palm of the hand, the dorsal forearm and the outer wrist skin. Experimental measurements of human skin reflectance in a normal and a wet state on the back of the hand and the palm of the hand regions indicated that the mean differences in the reflectance before and after the application of water were ~0.078 and ~0.152, respectively. These differences were found to be statistically significant as assessed using t -tests (34 paired t -tests and six independent t -tests were performed to assess the significance level of the mean differences in the reflectance of the skin). Radiometric measurements in this paper show the quantitative variations in the skin reflectance between locations, sexes, and individuals. The study reveals that these variations are related to the skin thickness and water content, a capability that has the potential to allow radiometry to be used as a non-contact sensor to detect and monitor skin conditions such as eczema, psoriasis, malignancy, and burn wounds.

Objective: Electroencephalography (EEG) recorded during transcranial alternating current simulation (tACS) is highly desirable in order to investigate brain dynamics during stimulation, but is corrupted by large amplitude stimulation artefacts. Artefact removal algorithms have been presented previously, but with substantial debates on their performance, utility, and the presence of any residual artefacts. This paper investigates whether machine learning can be used to validate artefact removal algorithms. The postulation is that residual artefacts in the EEG after cleaning would be independent of the experiment performed, making it impossible to differentiate between different parts of an EEG+tACS experiment, or between different behavioural tasks performed., Approach: Ten participates undertook two tasks (nBack and backwards digital recall) during simultaneous EEG+tACS, exercising different aspects of working memory. Stimulations during no task and sham conditions were also performed. A previously reported tACS artefact removal algorithm from our group was used to clean the EEG and a linear discriminant analysis was trained on the cleaned EEG to differentiate different parts of the experiment., Main Results: Baseline, baseline during tACS, working memory task without tACS, and working memory task with tACS data segments could be differentiated with accuracies ranging from 65%-94%, far exceeding chance levels. EEG from the nBack and backwards digital recall tasks could be separated during stimulation, with an accuracy exceeding 72%. If residual tACS artefacts remained after the EEG cleaning these did not dominate the classification process., Significance: This helps in building confidence that true EEG information is present after artefact removal. Our methodology presents a new approach to validating tACS artefact removal approaches.

Advances in neuroimaging, genomic, motion tracking, eye-tracking and many other technology-based data collection methods have led to a torrent of high dimensional datasets, which commonly have a small number of samples because of the intrinsic high cost of data collection involving human participants. High dimensional data with a small number of samples is of critical importance for identifying biomarkers and conducting feasibility and pilot work, however it can lead to biased machine learning (ML) performance estimates. Our review of studies which have applied ML to predict autistic from non-autistic individuals showed that small sample size is associated with higher reported classification accuracy. Thus, we have investigated whether this bias could be caused by the use of validation methods which do not sufficiently control overfitting. Our simulations show that K-fold Cross-Validation (CV) produces strongly biased performance estimates with small sample sizes, and the bias is still evident with sample size of 1000. Nested CV and train/test split approaches produce robust and unbiased performance estimates regardless of sample size. We also show that feature selection if performed on pooled training and testing data is contributing to bias considerably more than parameter tuning. In addition, the contribution to bias by data dimensionality, hyper-parameter space and number of CV folds was explored, and validation methods were compared with discriminable data. The results suggest how to design robust testing methodologies when working with small datasets and how to interpret the results of other studies based on what validation method was used., Competing Interests: The authors have declared that no competing interests exist.

We present a new wearable electrooculogram (EOG) monitor for measuring eye movements. We fabricated conductive and flexible graphene-based textiles from nylon to use as a sensing electrode, which we then integrated into a commercially available eye mask held in place only with the standard elastic strap. We tested this mask on 4 participants to quantify the noise floor and show that we can detect eye blinks to a high SNR of over 16 dB. We also identify that the material can detect other eye movements in cases when the noise floor is low. As our system is held in place with only an elastic strap it offers the same level of comfort as when wearing a normal eye mask. Our sensors offer an increased level of comfort over conventional gelled electrodes traditionally used in EOG monitoring and may be of use for comfortable eye movement experiments. This is particularly important during sleep studies where the EOG is routinely monitored, but using bulky instrumentation.

Serious Mental Illnesses (SMIs) including schizophrenia and bipolar disorder are long term conditions which place major burdens on health and social care services. Locomotor activity is altered in many cases of SMI, and so in the long term wearable activity trackers could potentially aid in the early detection of SMI relapse, allowing early and targeted intervention. To move towards this goal, in this paper we use accelerometer activity tracking data collected from the UK Biobank to classify people as being either in a self-reported SMI group or an age and gender matched control group. Using an ensemble dense neural network algorithm we exploited hourly and average derived features from the wearable activity data and the created model obtained an accuracy of 91.3%.

In recent years there has been substantial interest in wearable devices that measure heart rate via photoplethysmography (PPG) sensors placed at the wrist. This is challenging as the wrist PPG signal is severely corrupted by artefacts during motion, and although a number of algorithms are now available commercially and academically there is still a need for improved performance, especially when examining physical activities other than running. To date, algorithms for motion artefact removal from the PPG have focused on the use of a co-located accelerometer to record the motion. In this work, we introduce co-located accelerometer, gyroscope and magnetometer sensors to allow three, six and nine degrees of freedom estimates of the motion present. Assessed during a bike riding task the results show that the heart rate estimation is improved by up 0.57 beats per minute by using the additional information from these new sensors.

Autism is a developmental condition primarily identified by social and communication deficits. However, over 70% of autistic individuals also show motor function deficits, which are evident even when simple stereotyped movements are performed. In this study, we have asked 24 autistic and 22 non-autistic adults to perform pointing movements between two markers 30 cm apart as quickly and as accurately as they can for 10 seconds. Motion tracking was employed to collect data and calculate kinematic features of the movement and aiming accuracy. At the group level, the results showed that autistic individuals performed pointing movements slower but more accurately compared to non-autistic individuals. At the individual level, we have used Machine Learning methods to predict autism diagnosis. Nested result Cross-Validation was used, which in contrast to commonly used K-fold Cross-Validation avoids pooling training and testing data and provides robust performance estimates. Our developed models achieved a statistically significant classification accuracy of 71% and showed that even a simple and short motor task enables discrimination between autistic and non-autistic individuals.

For electroencephalography (EEG) in haired regions of the head, finger-based electrodes have been proposed in order to part the hair and make a direct contact with the scalp. Previous work has demonstrated 3D-printed fingered electrodes to allow personalisation and different configurations of electrodes to be used for different people or for different parts of the head. This paper presents flexible 3D-printed EEG electrodes for the first time. A flexible 3D printing element is now used, with three different base mechanical structures giving differently-shaped electrodes. To obtain improved sensing performance, the silver coatings used previously have been replaced with a silver/silver-chloride coating. This results in reduced electrode contact impedance and reduced contact noise. Detailed electro-mechanical testing is presented to demonstrate the performance of the operation of the new electrodes, particularly with regards to changes in conductivity under compression, together with on-person tests to demonstrate the recording of EEG signals.

Transcranial electrical stimulation is a widely used non-invasive brain stimulation approach. To date, EEG has been used to evaluate the effect of transcranial Direct Current Stimulation (tDCS) and transcranial Alternating Current Stimulation (tACS), but most studies have been limited to exploring changes in EEG before and after stimulation due to the presence of stimulation artifacts in the EEG data. This paper presents two different algorithms for removing the gross tACS artifact from simultaneous EEG recordings. These give different trade-offs in removal performance, in the amount of data required, and in their suitability for closed loop systems. Superposition of Moving Averages and Adaptive Filtering techniques are investigated, with significant emphasis on verification. We present head phantom testing results for controlled analysis, together with on-person EEG recordings in the time domain, frequency domain, and Event Related Potential (ERP) domain. The results show that EEG during tACS can be recovered free of large scale stimulation artifacts. Previous studies have not quantified the performance of the tACS artifact removal procedures, instead focusing on the removal of second order artifacts such as respiration related oscillations. We focus on the unresolved challenge of removing the first order stimulation artifact, presented with a new multi-stage validation strategy.

The electroencephalogram (EEG) is a widely used non-invasive method for monitoring the brain. It is based upon placing conductive electrodes on the scalp which measure the small electrical potentials that arise outside of the head due to neuronal action within the brain. Historically this has been a large and bulky technology, restricted to the monitoring of subjects in a lab or clinic while they are stationary. Over the last decade much research effort has been put into the creation of "wearable EEG" which overcomes these limitations and allows the long term non-invasive recording of brain signals while people are out of the lab and moving about. This paper reviews the recent progress in this field, with particular emphasis on the electrodes used to make connections to the head and the physical EEG hardware. The emergence of conformal "tattoo" type EEG electrodes is highlighted as a key next step for giving very small and socially discrete units. In addition, new recommendations for the performance validation of novel electrode technologies are given, with standards in this area seen as the current main bottleneck to the wider take up of wearable EEG. The paper concludes by considering the next steps in the creation of next generation wearable EEG units, showing that a wide range of research avenues are present., Competing Interests: The author has no conflicts of interest to declare.All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.Informed consent was obtained from all individual participants included in the study.

Wearable devices have the potential to improve healthcare, but suffer from significant barriers to adoption, including the need for constant recharging. Harvesting energy from the ambient environment to top-up batteries can overcome this, but the actual energy available is very small, and hence it is critical that the whole system is highly optimized. This paper presents an investigation into the optimization of inertial energy harvesters for placement at the human foot. Lower body locations have previously been shown to be very energy dense, however previous energy harvester modeling has focused on the lower leg rather than the foot itself for ease of device placement. We show that the typical energy density can be almost double at the foot compared with lower leg positions, with substantially more energy concentrated in a smaller bandwidth. There is thus a dual benefit of placing a harvester at the foot: there is more energy due to the larger movement of the foot, and more efficient (higher Q) harvesters can be used to increase the collected energy. We place these results in context by analyzing the power demands of a typical wearable, and identify that with appropriate harvester tuning the peak current requirements of the electronics can be fitted into the energy peaks generated from each footstep.

Wearable sensors placed behind-the-ear are emerging as being very promising for unobtrusive long term monitoring. Factors such as gait, electroencephalography (EEG), and ballistocardiography (BCG) can all be measured from behind-the-ear in a socially acceptable hearing aid based form factor. Previous works have investigated the recording of electrocardiography (ECG) from the ear, but generally with one electrode placed some distance away from the ear itself. This paper uses recently introduced tattoo electrodes to investigate whether ECG components can indeed be measured from behind a single ear. Compared to a reference photophelsmography (PPG) device we show that the fundamental heart beat frequency is present in behind-the-ear ECG only in half of the cases considered. In contrast the second harmonic is present in all records and could allow the extraction of heart rate to within a few beats-per-minute accuracy. Further signal processing work is required to allow the automated extraction of this, particularly when working with short time windows of data, but our results characterize the signal and demonstrate the principle of behind-the-ear ECG collected from a single ear.

Smart garments for invisible health sensing have been available for a number of years, with heart sensing typically performed using silver loaded conductive threads integrated into the fabric to pick up the electrocardiogram. Recent work has investigated printed graphene textiles as an alternative to this, which are potentially more environmentally friendly, cost-effective, and can be performed after garment manufacturing. This paper presents an exploration of second order factors on the performance of graphene textile electrodes for electrocardiogram measurements. We prepare graphenebased textile electrodes using a simple and highly scalable continuous padding method. We then analyze two metrics: the change in heart rate estimation error, and the changes in signal-to-noise ratio; under two separate conditions: an extended record length, and varying temperatures; to recreate the some of the conditions the material would experience when being worn in real-life. We report that neither the heart rate estimation error or the signal-to-noise ratio are significantly affected after a long record or with varying temperature. These tests indicate that graphene electrodes are suitable for electrocardiogram measurements in a wearable that will be subjected to these conditions.

In recent years machine learning methods for human activity recognition have been found very effective. These classify discriminative features generated from raw input sequences acquired from body-worn inertial sensors. However, it involves an explicit feature extraction stage from the raw data, and although human movements are encoded in a sequence of successive samples in time most state-of-the-art machine learning methods do not exploit the temporal correlations between input data samples. In this paper we present a Long-Short Term Memory (LSTM) deep recurrent neural network for the classification of six daily life activities from accelerometer and gyroscope data. Results show that our LSTM can processes featureless raw input signals, and achieves 92 % average accuracy in a multi-class-scenario. Further, we show that this accuracy can be achieved with almost four times fewer training epochs by using a batch normalization approach.

Objective: Recently numerous methods have been proposed for estimating average heart rate using photoplethysmography (PPG) during physical activity, overcoming the significant interference that motion causes in PPG traces. We propose a new algorithm framework for extracting instantaneous heart rate from wearable PPG and Electrocardiogram (ECG) signals to provide an estimate of heart rate variability during exercise., Methods: For ECG signals, we propose a new spectral masking approach which modifies a particle filter tracking algorithm, and for PPG signals constrains the instantaneous frequency obtained from the Hilbert transform to a region of interest around a candidate heart rate measure. Performance is verified using accelerometry and wearable ECG and PPG data from subjects while biking and running on a treadmill., Results: Instantaneous heart rate provides more information than average heart rate alone. The instantaneous heart rate can be extracted during motion to an accuracy of 1.75 beats per min (bpm) from PPG signals and 0.27 bpm from ECG signals., Conclusion: Estimates of instantaneous heart rate can now be generated from PPG signals during motion. These estimates can provide more information on the human body during exercise., Significance: Instantaneous heart rate provides a direct measure of vagal nerve and sympathetic nervous system activity and is of substantial use in a number of analyzes and applications. Previously it has not been possible to estimate instantaneous heart rate from wrist wearable PPG signals.

Electroencephalography (EEG) is a procedure that records brain activity in a non-invasive manner. The cost and size of EEG devices has decreased in recent years, facilitating a growing interest in wearable EEG that can be used out-of-the-lab for a wide range of applications, from epilepsy diagnosis, to stroke rehabilitation, to Brain-Computer Interfaces (BCI). A major obstacle for these emerging applications is the wet electrodes, which are used as part of the EEG setup. These electrodes are attached to the human scalp using a conductive gel, which can be uncomfortable to the subject, causes skin irritation, and some gels have poor long-term stability. A solution to this problem is to use dry electrodes, which do not require conductive gel, but tend to have a higher noise floor. This paper presents a novel methodology for the design and manufacture of such dry electrodes. We manufacture the electrodes using low cost desktop 3D printers and off-the-shelf components for the first time. This allows quick and inexpensive electrode manufacturing and opens the possibility of creating electrodes that are customized for each individual user. Our 3D printed electrodes are compared against standard wet electrodes, and the performance of the proposed electrodes is suitable for BCI applications, despite the presence of additional noise., Competing Interests: The authors declare no conflict of interest.

This paper presents a new method for estimating the average heart rate from a foot/ankle worn photoplethysmography (PPG) sensor during fast bike activity. Placing the PPG sensor on the lower half of the body allows more energy to be collected from energy harvesting in order to give a power autonomous sensor node, but comes at the cost of introducing significant motion interference into the PPG trace. We present a normalised least mean square adaptive filter and short-time Fourier transform based algorithm for estimating heart rate in the presence of this motion contamination. Results from 8 subjects show the new algorithm has an average error of 9 beats-per-minute when compared to an ECG gold standard.

Wearable electronics are revolutionizing personalized and preventative healthcare by allowing the easy, unobtrusive, and long term monitoring of a range of body parameters. Conformal electronics which attach directly to the skin in a very robust and long term manner are envisioned as the next generation of highly portable miniaturized computing devices, beyond wearables. In this paper we overview the state-of-the-art in conformal electronics created using silver nanoparticle inkjet printed techniques for home assistive and rehabilitative devices. The barriers to wider adaption, particularly the challenges of high performance antenna design when placed close to the body, are discussed in detail.

Ultra low power signal processing is an essential part of all sensor nodes, and particularly so in emerging wearable sensors for biomedical applications. Analog signal processing has an important role in these low power, low voltage, low frequency applications, and there is a key drive to decrease the power consumption of existing analog domain signal processing and to map more signal processing approaches into the analog domain. This paper presents an analog domain signal processing circuit which approximates the output of the Discrete Wavelet Transform (DWT) for use in ultra low power wearable sensors. Analog filters are used for the DWT filters and it is demonstrated how these generate analog domain DWT-like information that embeds information from Butterworth and Daubechies maximally flat mother wavelet responses. The Analog DWT is realised in hardware via g(m)C circuits, designed to operate from a 1.3 V coin cell battery, and provide DWT-like signal processing using under 115 nW of power when implemented in a 0.18 μm CMOS process. Practical examples demonstrate the effective use of the new Analog DWT on ECG (electrocardiogram) and EEG (electroencephalogram) signals recorded from humans.