Wearables, smartphones, and artificial intelligence for digital phenotyping and health

Ubiquitous progress in wearable sensing and mobile computing technologies, alongside growing diversity in sensor modalities, has created new pathways for the collection of health and well-being data outside of laboratory settings, in a longitudinal fashion. Wearable and mobile devices have the potential to provide low-cost, objective measures of physical activity, clinically relevant data for patient assessment, and scalable behavior monitoring in large populations. These data can be used in both interventional and observational studies to derive insights regarding the links between behavior, health. and disease, as well as to advance the personalization and effectiveness of commercial wellness applications. Today, over 400,000 participants have had their behavior tracked prospectively using accelerometers for epidemiological studies across the globe. Traditionally, epidemiologists and clinicians have relied upon self-report measures of physical activity and sleep which, while valuable in the absence of alternatives, are subject to bias and often provide partial, incomplete information Physical behavior data extracted from wearable devices are being used to derive sensor-assessed, objective measures of physical behaviors, overcoming the limitations of self-report with the aim of relating these to clinical endpoints and eventually applying the findings to preventive and predictive medicine. Moreover, the application of artificial intelligence (AI), sensor fusion, and signal processing to wearable sensor data has led to improved human activity recognition and behavioral phenotyping. Here, we review the state of the art in wearable and mobile sensing technology in epidemiology and clinical medicine and discuss how AI is changing the field

Sequence multi-task learning to forecast mental wellbeing from sparse self-reported data

Smartphones have started to be used as self reporting tools for mental health state as they accompany individuals during their days and can therefore gather temporally fine grained data. However, the analysis of self reported mood data offers challenges related to non-homogeneity of mood assessment among individuals due to the complexity of the feeling and the reporting scales, as well as the noise and sparseness of the reports when collected in the wild. In this paper, we propose a new end-to-end ML model inspired by video frame prediction and machine translation, that forecasts future sequences of mood from previous self-reported moods collected in the real world using mobile devices. Contrary to traditional time series forecasting algorithms, our multi-task encoder-decoder recurrent neural network learns patterns from different users, allowing and improving the prediction for users with limited number of self-reports. Unlike traditional feature-based machine learning algorithms, the encoder-decoder architecture enables to forecast a sequence of future moods rather than one single step. Meanwhile, multi-task learning exploits some unique characteristics of the data (mood is bi-dimensional), achieving better results than when training single-task networks or other classifiers. Our experiments using a real-world dataset of 33, 000 user-weeks revealed that (i) 3 weeks of sparsely reported mood is the optimal number to accurately forecast mood, (ii) multi-task learning models both dimensions of mood –valence and arousal– with higher accuracy than separate or traditional ML models, and (iii) mood variability, personality traits and day of the week play a key role in the performance of our model. We believe this work provides psychologists and developers of future mobile mental health applications with a ready-to-use and effective tool for early diagnosis of mental health issues at scale.This work was supported by the Embiricos Trust Scholarship of Jesus College Cambridge, EPSRC through Grants DTP (EP/N509620/1) and UBHAVE (EP/I032673/1), and Nokia Bell Labs through the Centre of Mobile, Wearable Systems and Augmented Intelligence

SelfHAR: Improving Human Activity Recognition through Self-training with Unlabeled Data

Machine learning and deep learning have shown great promise in mobile sensing applications, including Human Activity Recognition. However, the performance of such models in real-world settings largely depends on the availability of large datasets that captures diverse behaviors. Recently, studies in computer vision and natural language processing have shown that leveraging massive amounts of unlabeled data enables performance on par with state-of-the-art supervised models. In this work, we present SelfHAR, a semi-supervised model that effectively learns to leverage unlabeled mobile sensing datasets to complement small labeled datasets. Our approach combines teacher-student self-training, which distills the knowledge of unlabeled and labeled datasets while allowing for data augmentation, and multi-task self-supervision, which learns robust signal-level representations by predicting distorted versions of the input. We evaluated SelfHAR on various HAR datasets and showed state-of-the-art performance over supervised and previous semi-supervised approaches, with up to 12% increase in F1 score using the same number of model parameters at inference. Furthermore, SelfHAR is data-efficient, reaching similar performance using up to 10 times less labeled data compared to supervised approaches. Our work not only achieves state-of-the-art performance in a diverse set of HAR datasets, but also sheds light on how pre-training tasks may affect downstream performance

Emission spectroscopy for the temperature measurement of salt-water ice during hypervelocity impact

Current understanding of icy Solar System bodies, such as Europa and Enceladus, suggests they may contain favourable environmental conditions to synthesise biologically significant molecules. Laboratory impact flash measurements from icy targets can be utilised to constrain the temperatures required for the shock-synthesis of these biologically important species. This abstract details temperature measurement of salt-water ice during hypervelocity impact using in-situ emission spectroscopy. This preliminary study utilises different projectile speeds and materials to assess the range of generated temperatures during impact. The relative intensities of the averaged Na 589 nm and 819 nm doublet emission lines originating from the target ice were used to determine approximate peak temperatures for each impact experiment using a Boltzmann distribution calculation. All determined temperatures using this method were between 3000 K and 3420 K. Furthermore, shots with similar impacts speeds showed a small temperature difference of 140 K despite the distinctly different projectile material properties

Exploring Automatic Diagnosis of COVID-19 from Crowdsourced Respiratory Sound Data

Audio signals generated by the human body (e.g., sighs, breathing, heart, digestion, vibration sounds) have routinely been used by clinicians as indicators to diagnose disease or assess disease pro- gression. Until recently, such signals were usually collected through manual auscultation at scheduled visits. Research has now started to use digital technology to gather bodily sounds (e.g., from dig- ital stethoscopes) for cardiovascular or respiratory examination, which could then be used for automatic analysis. Some initial work shows promise in detecting diagnostic signals of COVID-19 from voice and coughs. In this paper we describe our data analysis over a large-scale crowdsourced dataset of respiratory sounds collected to aid diagnosis of COVID-19. We use coughs and breathing to under- stand how discernible COVID-19 sounds are from those in asthma or healthy controls. Our results show that even a simple binary machine learning classifier is able to classify correctly healthy and COVID-19 sounds. We also show how we distinguish a user who tested positive for COVID-19 and has a cough from a healthy user with a cough, and users who tested positive for COVID-19 and have a cough from users with asthma and a cough. Our models achieve an AUC of above 80% across all tasks. These results are preliminary and only scratch the surface of the potential of this type of data and audio-based machine learning. This work opens the door to further investigation of how automatically analysed respiratory patterns could be used as pre-screening signals to aid COVID-19 diagnosis.ER

What’s trending in Breathlessness research? Proceedings from the 8th Annual Meeting of the Breathlessness Research Interest Group

Breathlessness remains a challenging symptom, common to a multitude of malignant and non-malignant diseases, for which there are limited effective therapies once disease control is optimised. The American Thoracic Society (ATS) statement on dyspnoea reports that: i)Progress in dyspnoea management has not matched progress in elucidating underlying mechanisms; ii)There is a critical need for interdisciplinary translational research to connect dyspnoea mechanisms with treatments; iii)There is a need to validate dyspnoea measures as patient-reported outcomes for clinical trials. Research into the many dimensions of breathlessness and its significance to patients and their carers has increased in recent years. This meeting is convened yearly to bring together researchers across various disciplines including respiratory medicine, anaesthetics, medical humanities, engineering and palliative care, to further understanding of the symptom, discuss new techniques and advances in research, and pave the way forward for future studies and interventions. The presentations generated much vibrant discussion amongst the multidisciplinary attendees and highlighted areas where care for breathless patients could be improved. This is a positive time for breathlessness research, with several ATS research priorities being addressed and it is clear that further studies and ensuing interventions are on the horizon.This is the author accepted manuscript. The final version is available from Maney at http://www.maneyonline.com/toc/ppc/current

Multifunctional Devices and Logic Gates With Undoped Silicon Nanowires

We report on the electronic transport properties of multiple-gate devices fabricated from undoped silicon nanowires. Understanding and control of the relevant transport mechanisms was achieved by means of local electrostatic gating and temperature dependent measurements. The roles of the source/drain contacts and of the silicon channel could be independently evaluated and tuned. Wrap gates surrounding the silicide-silicon contact interfaces were proved to be effective in inducing a full suppression of the contact Schottky barriers, thereby enabling carrier injection down to liquid-helium temperature. By independently tuning the effective Schottky barrier heights, a variety of reconfigurable device functionalities could be obtained. In particular, the same nanowire device could be configured to work as a Schottky barrier transistor, a Schottky diode or a p-n diode with tunable polarities. This versatility was eventually exploited to realize a NAND logic gate with gain well above one.Comment: 6 pages, 5 figure

Serum Levels of Surfactant Proteins in Patients with Combined Pulmonary Fibrosis and Emphysema (CPFE)

Introduction Emphysema and idiopathic pulmonary fibrosis (IPF) present either per se or coexist in combined pulmonary fibrosis and emphysema (CPFE). Serum surfactant proteins (SPs) A, B, C and D levels may reflect lung damage. We evaluated serum SP levels in healthy controls, emphysema, IPF, and CPFE patients and their associations to disease severity and survival. Methods 122 consecutive patients (31 emphysema, 62 IPF, and 29 CPFE) and 25 healthy controls underwent PFTs, ABG-measurements, 6MWT and chest HRCT. Serum levels of SPs were measured. Patients were followed-up for 1-year. Results SP-A and SP-D levels differed between groups (p = 0.006 and p= 26 ng/mL) presented a weak association with reduced survival (p = 0.05). Conclusion In conclusion, serum SP-A and SP-D levels were higher where fibrosis exists or coexists and related to disease severity, suggesting that serum SPs relate to alveolar damage in fibrotic lungs and may reflect either local overproduction or overleakage. The weak association between high levels of SP-B and survival needs further validation in clinical trials

Material-specific gap function in the high-temperature superconductors

We present theoretical arguments and experimental support for the idea that high-Tc superconductivity can occur with s-wave, d-wave, or mixed-wave pairing in the context of a magnetic mechanism. The size and shape of the gap is different for different materials. The theoretical arguments are based on the t-J model as derived from the Hubbard model so that it necessarily includes three-site terms. We argue that this should be the basic minimal model for high-Tc systems. We analyze this model starting with the dilute limit which can be solved exactly, passing then to the Cooper problem which is numerically tractable, then ending with a mean field approach. It is found that the relative stability of s-wave and d-wave depends on the size and the shape of the Fermi surface. We identify three striking trends. First, materials with large next-nearest-neighbor hopping (such as YBa(2)Cu(3)O(7-x)) are nearly pure d-wave, whereas nearest-neighbor materials (such as La(2-x)Sr(x)CuO(4)) tend to be more s-wave-like. Second, low hole doping materials tend to be pure d-wave, but high hole doping leads to s-wave. Finally, the optimum hole doping level increases as the next-nearest-neighbor hopping increases. We examine the experimental evidence and find support for this idea that gap function in the high-temperature superconductors is material-specific.Comment: 20 pages; requires revtex.sty v3.0, epsf.sty; includes 6 EPS figures; Postscript version also available at http://lifshitz.physics.wisc.edu/www/koltenbah/papers/gapfunc2.ps . This version contains an extensive amount of new work including theoretical background, an additional mean field treatment with new figures, and a more thorough experimental surve

Transparency and Trust in Human-AI-Interaction: The Role of Model-Agnostic Explanations in Computer Vision-Based Decision Support

Computer Vision, and hence Artificial Intelligence-based extraction of information from images, has increasingly received attention over the last years, for instance in medical diagnostics. While the algorithms' complexity is a reason for their increased performance, it also leads to the "black box" problem, consequently decreasing trust towards AI. In this regard, "Explainable Artificial Intelligence" (XAI) allows to open that black box and to improve the degree of AI transparency. In this paper, we first discuss the theoretical impact of explainability on trust towards AI, followed by showcasing how the usage of XAI in a health-related setting can look like. More specifically, we show how XAI can be applied to understand why Computer Vision, based on deep learning, did or did not detect a disease (malaria) on image data (thin blood smear slide images). Furthermore, we investigate, how XAI can be used to compare the detection strategy of two different deep learning models often used for Computer Vision: Convolutional Neural Network and Multi-Layer Perceptron. Our empirical results show that i) the AI sometimes used questionable or irrelevant data features of an image to detect malaria (even if correctly predicted), and ii) that there may be significant discrepancies in how different deep learning models explain the same prediction. Our theoretical discussion highlights that XAI can support trust in Computer Vision systems, and AI systems in general, especially through an increased understandability and predictability