Physical activity based classification of serious mental illness group participants in the UK Biobank using ensemble dense neural networks

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%

Estimation of heart rate from foot worn photoplethysmography sensors during fast bike exercise

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

Design and implementation of a convolutional neural network on an edge computing smartphone for human activity recognition

Edge computing aims to integrate computing into everyday settings, enabling the system to be context-aware and private to the user. With the increasing success and popularity of deep learning methods, there is an increased demand to leverage these techniques in mobile and wearable computing scenarios. In this paper, we present an assessment of a deep human activity recognition system’s memory and execution time requirements, when implemented on a mid-range smartphone class hardware and the memory implications for embedded hardware. This paper presents the design of a convolutional neural network (CNN) in the context of human activity recognition scenario. Here, layers of CNN automate the feature learning and the influence of various hyper-parameters such as the number of filters and filter size on the performance of CNN. The proposed CNN showed increased robustness with better capability of detecting activities with temporal dependence compared to models using statistical machine learning techniques. The model obtained an accuracy of 96.4% in a five-class static and dynamic activity recognition scenario. We calculated the proposed model memory consumption and execution time requirements needed for using it on a mid-range smartphone. Per-channel quantization of weights and per-layer quantization of activation to 8-bits of precision post-training produces classification accuracy within 2% of floating-point networks for dense, convolutional neural network architecture. Almost all the size and execution time reduction in the optimized model was achieved due to weight quantization. We achieved more than four times reduction in model size when optimized to 8-bit, which ensured a feasible model capable of fast on-device inference

Electronic and electrochemical viral detection for point-of-care use: A systematic review

From PLOS via Jisc Publications RouterHistory: collection 2021, received 2021-07-05, accepted 2021-09-15, epub 2021-09-30Publication status: PublishedFunder: EPSRC Graphene NowNano CDT; Grant(s): EP/L01548X/1Funder: Dame Kathleen Ollerenshaw FellowshipDetecting 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