Gert Mertes

Purpose: Step count is an intuitive measure of physical activity frequently quantified in health-related studies; however, accurate step counting is difficult in the free-living environment, with error routinely above 20% in wrist-worn devices against camera-annotated ground truth. This study aimed to describe the development and validation of step count derived from a wrist-worn accelerometer and assess its association with cardiovascular and all-cause mortality in a large prospective cohort. Methods: We developed and externally validated a self-supervised machine learning step detection model, trained on an open-source and step-annotated free-living dataset. Thirty-nine individuals will free-living ground-truth annotated step counts were used for model development. An open-source dataset with 30 individuals was used for external validation. Epidemiological analysis was performed using 75,263 UK Biobank participants without prevalent cardiovascular disease (CVD) or cancer. Cox regression was used to test the association of daily step count with fatal CVD and all-cause mortality after adjustment for potential confounders. Results: The algorithm substantially outperformed reference models (free-living mean absolute percent error of 12.5% vs 65%–231%). Our data indicate an inverse dose–response association, where taking 6430–8277 daily steps was associated with 37% (25%–48%) and 28% (20%–35%) lower risk of fatal CVD and all-cause mortality up to 7 yr later, compared with those taking fewer steps each day. Conclusions: We have developed an open and transparent method that markedly improves the measurement of steps in large-scale wrist-worn accelerometer datasets. The application of this method demonstrated expected associations with CVD and all-cause mortality, indicating excellent face validity. This reinforces public health messaging for increasing physical activity and can help lay the groundwork for the inclusion of target step counts in future public health guidelines.

Digital measures of health status captured during daily life could greatly augment current in-clinic assessments for rheumatoid arthritis (RA), to enable better assessment of disease progression and impact. This work presents results from weaRAble-PRO, a 14-day observational study, which aimed to investigate how digital health technologies (DHT), such as smartphones and wearables, could augment patient reported outcomes (PRO) to determine RA status and severity in a study of 30 moderate-to-severe RA patients, compared to 30 matched healthy controls (HC). Sensor-based measures of health status, mobility, dexterity, fatigue, and other RA specific symptoms were extracted from daily iPhone guided tests (GT), as well as actigraphy and heart rate sensor data, which was passively recorded from patients’ Apple smartwatch continuously over the study duration. We subsequently developed a machine learning (ML) framework to distinguish RA status and to estimate RA severity. It was found that daily wearable sensor-outcomes robustly distinguished RA from HC participants (F1, 0.807). Furthermore, by day 7 of the study (half-way), a sufficient volume of data had been collected to reliably capture the characteristics of RA participants. In addition, we observed that the detection of RA severity levels could be improved by augmenting standard patient reported outcomes with sensor-based features (F1, 0.833) in comparison to using PRO assessments alone (F1, 0.759), and that the combination of modalities could reliability measure continuous RA severity, as determined by the clinician-assessed RAPID-3 score at baseline (r², 0.692; RMSE, 1.33). The ability to measure the impact of the disease during daily life—through objective and remote digital outcomes—paves the way forward to enable the development of more patient-centric and personalised measurements for use in RA clinical trials.

Background Step count is an intuitive measure of physical activity frequently quantified in a range of health-related studies; however, accurate quantification of step count can be difficult in the free-living environment, with step counting error routinely above 20% in both consumer and research-grade wrist-worn devices. This study aims to describe the development and validation of step count derived from a wrist-worn accelerometer and to assess its association with cardiovascular and all-cause mortality in a large prospective cohort study. Methods We developed and externally validated a hybrid step detection model that involves self-supervised machine learning, trained on a new ground truth annotated, free-living step count dataset (OxWalk, n=39, aged 19-81) and tested against other open-source step counting algorithms. This model was applied to ascertain daily step counts from raw wrist-worn accelerometer data of 75,493 UK Biobank participants without a prior history of cardiovascular disease (CVD) or cancer. Cox regression was used to obtain hazard ratios and 95% confidence intervals for the association of daily step count with fatal CVD and all-cause mortality after adjustment for potential confounders. Findings The novel step algorithm demonstrated a mean absolute percent error of 12.5% in free-living validation, detecting 98.7% of true steps and substantially outperforming other recent wrist-worn, open-source algorithms. Our data are indicative of an inverse dose-response association, where, for example, taking 6,596 to 8,474 steps per day was associated with a 39% [24-52%] and 27% [16-36%] lower risk of fatal CVD and all-cause mortality, respectively, compared to those taking fewer steps each day. Interpretation An accurate measure of step count was ascertained using a machine learning pipeline that demonstrates state-of-the-art accuracy in internal and external validation. The expected associations with CVD and all-cause mortality indicate excellent face validity. This algorithm can be used widely for other studies that have utilised wrist-worn accelerometers and an open-source pipeline is provided to facilitate implementation. Funding Acknowledgements This research has been conducted using the UK Biobank Resource under Application Number 59070. This research was funded in whole or in part by the Wellcome Trust [223100/Z/21/Z]. For the purpose of open access, the author has applied a CC-BY public copyright licence to any author accepted manuscript version arising from this submission. AD and SS are supported by the Wellcome Trust. AD and DM are supported by Swiss Re, while AS is an employee of Swiss Re. AD, SC, RW, SS, and SK are supported by HDR UK, an initiative funded by UK Research and Innovation, Department of Health and Social Care (England) and the devolved administrations. AD, DB, GM, and SC are supported by NovoNordisk. AD is supported by the BHF Centre of Research Excellence (grant number RE/18/3/34214). SS is supported by the University of Oxford Clarendon Fund. DB is further supported by the Medical Research Council (MRC) Population Health Research Unit. DC holds a personal academic fellowship from EPSRC. AA, AC and DC are supported by GlaxoSmithKline. SK is supported by Amgen and UCB BioPharma outside of the scope of this work. Computational aspects of this research were funded from the National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (BRC) with additional support from Health Data Research (HDR) UK and the Wellcome Trust Core Award [grant number 203141/Z/16/Z]. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health.

Objective Heart failure, respiratory failure and kidney failure are three severe organ failures (OF) that have high mortalities and are most prevalent in intensive care units. The objective of this work is to offer insights on OF clustering from the aspects of graph neural network and diagnosis history. Methods This paper proposes a neural network-based pipeline to cluster three types of organ failure patients by incorporating embedding pre-train using an ontology graph of International Classification of Diseases (ICD) codes. We employ an autoencoder-based deep clustering architecture jointly trained with a K-means loss, and a non-linear dimension reduction is performed to obtain patient clusters on the MIMIC-III dataset. Results The clustering pipeline shows superior performance on a public-domain image dataset. For MIMIC-III, the model gives two distinct clusters that are related to the severity of the diseases. The learnt ICD embeddings present strong power in identifying the OF type in supervised learning. Conclusion Our proposed pipeline gives stable clusters, however, they do not correspond to the type of OF which indicates these OF share significant hidden characteristics in diagnosis. These clusters can be used to signal possible complications and severity of illness. Significance We are the first to apply an unsupervised approach to offer insights from a biomedical engineering perspective on these three types of organ failure, and publish the pre-trained embeddings for future transfer learning.

Upper limb motor impairment after stroke has been linked with reduced food intake and affected patients are at risk for malnutrition. The inability to consume food is often proportional to the severity of impairment. Being able to measure the effect of impairment on food intake can lead to additional insights in eating behavior of patients after stroke. In this paper, we propose the use of a smart plate containing weight sensors to extract eating behavior related parameters from individual bites during meals. Data was recorded with the smart plate from 6 patients that had difficulty eating due to impaired arm movement following a stroke. The results indicate that parameters such as bite size and pickup time can be linked to the patient's eating behavior and level of impairment.

In this work, a novel plate system that can detect weight and location of individual bites during meals is presented. The system consists of a base station with sensors and a detachable off-the-shelf polymer plate with three com- partments. By combining data from multiple weight sensors, the weight of individual bites can be accurately measured and localized on the plate to determine the compartment from which they were taken. With prior knowledge of the weight of the food in each compartment at the start of the meal, the system can estimate the nutritional value of the consumed food. In a test conducted in a controlled home environment, the system was able to measure the weight of consumed food in each compartment with a maximum relative error of 1.4%. The goal of the system is to replace traditional monitoring tools and to automatically monitor the amount of consumption.

Fall incidents remain an important health hazard for older adults. Fall detection systems can reduce the consequences of a fall incident by insuring that timely aid is given. Currently fall detection algorithms however suffer a reduction in accuracy when introduced in real-life situations. In this paper a late fusion technique is proposed that will improve the accuracy of existing fall detection systems. It combines the confidence levels of different single camera fall detection systems. Four different aggregation methods are compared to each other based on the Area Under the Curve (AUC) of precision-recall curves. Calculating the median of the confidence levels of five cameras an increase of 218% in the AUC of the precision-recall-curves is achieved compared to the AUC of the single camera fall detector. These results show that significant improvements can be made to the accuracy of single camera fall detectors in a relatively easy way.