Emerging Methods to Objectively Assess Pruritus in Atopic Dermatitis.

INTRODUCTION:Atopic dermatitis (AD) is an inflammatory skin disease with a chronic, relapsing course. Clinical features of AD vary by age, duration, and severity but can include papules, vesicles, erythema, exudate, xerosis, scaling, and lichenification. However, the most defining and universal symptom of AD is pruritus. Pruritus or itch, defined as an unpleasant urge to scratch, is problematic for many reasons, particularly its negative impact on quality of life. Despite the profoundly negative impact of pruritus on patients with AD, clinicians and researchers lack standardized and validated methods to objectively measure pruritus. The purpose of this review is to discuss emerging methods to assess pruritus in AD by describing objective patient-centered tools developed or enhanced over the last decade that can be utilized by clinicians and researchers alike. METHODS:This review is based on a literature search in Medline, Embase, and Web of Science databases. The search was performed in February 2019. The keywords were used "pruritus," "itch," "atopic dermatitis," "eczema," "measurements," "tools," "instruments," "accelerometer," "wrist actigraphy," "smartwatch," "transducer," "vibration," "brain mapping," "magnetic resonance imaging," and "positron emission tomography." Only articles written in English were included, and no restrictions were set on study type. To focus on emerging methods, prioritization was given to results from the last decade (2009-2019). RESULTS:The search yielded 49 results in PubMed, 134 results in Embase, and 85 results in Web of Science. Each result was independently reviewed in a standardized manner by two of the authors (M.S., K.L.), and disagreements between reviewers were resolved by consensus. Relevant findings were categorized into the following sections: video surveillance, acoustic surveillance, wrist actigraphy, smart devices, vibration transducers, and neurological imaging. Examples are provided along with descriptions of how each technology works, instances of use in research or clinical practice, and as applicable, reports of validation studies and correlation with other methods. CONCLUSION:The variety of new and improved methods to evaluate pruritus in AD is welcomed by clinicians, researchers, and patients alike. Future directions include next-generation smart devices as well as exploring new territories, such as identifying biomarkers that correlate to itch and machine-learning programs to identify itch processing in the brain. As these efforts continue, it will be essential to remain patient-centered by developing techniques that minimize discomfort, respect privacy, and provide accurate data that can be used to better manage itch in AD

Sleep detection with photoplethysmography for wearable-based health monitoring

Remote health monitoring has gained increasing attention in the recent years. Detecting sleep patterns provides users with insights on their personal health issues, and can help in the diagnosis of various sleep disorders. Conventional methods are focused on the acceleration data, or are not suitable for continuous monitoring, like the polysomnography. Wearable devices enable a way to continuously measure photoplethysmography signal. Photoplethysmography signal contains information on multiple physiological systems, and can be used to detect sleep patterns. Sleep detection using wearable-based photoplethysmography signal offers a convenient and easy way to monitor health. In this thesis, a photoplethysmography-based sleep detection method for wearable-based health monitoring is described. This technique aims to separate wakefulness and asleep states with adequate accuracy. To examine the importance of good quality data in sleep detection, the quality of the signal is assessed. The proposed method uses statistical and heart rate based features extracted from the photoplethysmography signal. Using the most relevant features, various supervised learning algorithms are trained, compared and evaluated. These algorithms are logistic regression, decision tree, random forest, support vector machine, k-nearest neighbors, and Naive Bayes. The best performance is obtained by the random forest classifier. The method received an overall accuracy of 81 percent. It was able to detect the sleep periods with 86 percent accuracy and the awake periods with 74 percent accuracy. Motion artifacts occurring during the awake time caused distortion to the signal. Features related to the shape of the signal improved the accuracy of sleep detection, since signal distortion was associated with the awake time. It is concluded that photoplethysmography signal provides a good alternative for wearable-based sleep detection. Future studies with more comprehensive sleep level analysis could be conducted to provide valuable information on the quality of sleep.Viime vuosina etänä tapahtuva terveyden seuranta on saanut yhä enemmän huomiota. Unen tunnistaminen antaa käyttäjille tietoa heidän henkilökohtaisista terveysongelmistaan ja voi auttaa erilaisten unihäiriöiden diagnosoinnissa. Tavanomaiset menetelmät käyttävät kiihtyvyyteen perustuvaa dataa, tai eivät ole soveltuvia jatkuvaan seurantaan, kuten polysomnografia. Puettavan teknologian avulla fotopletysmografiasignaalin jatkuva mittaus on mahdollista. Fotopletysmografiasignaali sisältää tietoa useista fysiologisista järjestelmistä ja sitä voidaan käyttää unen tunnistamiseen. Puettavan teknologian avulla mitatun fotopletysmografiasignaalin käyttö unen tunnistuksessa tarjoaa kätevän ja helpon tavan seurata terveyttä. Tässä diplomityössä kuvataan fotopletysmografiaan perustuva unenhavaitsemismenetelmä, joka soveltuu puettavaa teknologiaa hyödyntävään terveyden seurantaan. Tekniikalla pyritään erottamaan hereillä olo ja uni riittävän tarkasti. Signaalin laatu arvioidaan, jotta voidaan tutkia datan laadun tärkeys unen tunnistuksessa. Kehitetty menetelmä käyttää tilastollisia ja sykkeeseen perustuvia ominaisuuksia, jotka on erotettu fotopletysmografiasignaalista. Tärkeimpiä ominaisuuksia käyttämällä erilaisia valvottuja oppimisalgoritmeja koulutetaan, vertaillaan ja arvioidaan. Käytetyt algoritmit ovat logistinen regressio, päätöspuu, satunnainen metsä, tukivektorikone, k-lähimmät naapurit ja Naive Bayes. Paras tulos saadaan käyttämällä satunnainen metsä -algoritmia. Menetelmällä saavutetaan 81 prosentin kokonaistarkkuus. Uni pystytään tunnistamaan 86 prosentin tarkkuudella ja hereillä olo 74 prosentin tarkkuudella. Hereillä ollessa liikkeestä johtuvat häiriöt aiheuttavat vääristymää signaaliin. Signaalin muotoon liittyvät ominaisuudet paransivat unentunnistuksen tarkkuutta, koska signaalin vääristyminen yhdistettiin hereilläoloaikaan. Tutkimuksen tuloksista voidaan tehdä johtopäätös, että fotopletysmografiasignaali tarjoaa hyvän vaihtoehdon puettavaa teknologiaa hyödyntävään unen tunnistamiseen. Tulevaisuudessa unen eri vaiheita voitaisiin tutkia kattavammin, jolloin saataisiin arvokasta tietoa unen laadusta

Adversarial Unsupervised Representation Learning for Activity Time-Series

Sufficient physical activity and restful sleep play a major role in the prevention and cure of many chronic conditions. Being able to proactively screen and monitor such chronic conditions would be a big step forward for overall health. The rapid increase in the popularity of wearable devices provides a significant new source, making it possible to track the user's lifestyle real-time. In this paper, we propose a novel unsupervised representation learning technique called activity2vec that learns and "summarizes" the discrete-valued activity time-series. It learns the representations with three components: (i) the co-occurrence and magnitude of the activity levels in a time-segment, (ii) neighboring context of the time-segment, and (iii) promoting subject-invariance with adversarial training. We evaluate our method on four disorder prediction tasks using linear classifiers. Empirical evaluation demonstrates that our proposed method scales and performs better than many strong baselines. The adversarial regime helps improve the generalizability of our representations by promoting subject invariant features. We also show that using the representations at the level of a day works the best since human activity is structured in terms of daily routinesComment: Accepted at AAAI'19. arXiv admin note: text overlap with arXiv:1712.0952

Multimodal fusion of IMUs and EPS body-worn sensors for scratch recognition

In order to develop and evaluate the extent to which itching affects a person's daily life, it is useful to develop automated means to recognise the action of scratching. We present an investigation of sensors and algorithms to realise a wearable scratch detection device. We collected a dataset, where each user wore 4 inertial measurement unit (IMU) sensors and one electric potential sensor (EPS). Data were collected from nine users, where each user followed a 40-min protocol, which involved scratching different parts of head, shoulder, and leg, as well as other activities such as walking, drinking water, brushing teeth, and typing to a computer. The dataset contained 813 scratching instances and 5 h 15 min of recorded data. We investigated the trade-offs between the number of devices worn (comfort) and accuracy. We trained the k-NN and random forest algorithms by using between 1 and 5 features per channel. We concluded that a scratch could be detected with 80.7% accuracy by using the random forest algorithm on hand coordinates, which required four devices. However, an f1 score of 70% could be achieved with k-NN with IMU and EPS data, which only required one device. Moreover, the fusion of IMU data with EPS data improved the accuracy and reduced the deviation between the folds. This expanded the state-of-the-art method by opening up new trade-offs between accuracy and comfort for future researc

Automatic sleep staging of EEG signals: recent development, challenges, and future directions.

Modern deep learning holds a great potential to transform clinical studies of human sleep. Teaching a machine to carry out routine tasks would be a tremendous reduction in workload for clinicians. Sleep staging, a fundamental step in sleep practice, is a suitable task for this and will be the focus in this article. Recently, automatic sleep-staging systems have been trained to mimic manual scoring, leading to similar performance to human sleep experts, at least on scoring of healthy subjects. Despite tremendous progress, we have not seen automatic sleep scoring adopted widely in clinical environments. This review aims to provide the shared view of the authors on the most recent state-of-the-art developments in automatic sleep staging, the challenges that still need to be addressed, and the future directions needed for automatic sleep scoring to achieve clinical value

Advanced systems for head scratch detection

Itching is a condition that affects a substantial group of people. This condition may be caused by different conditions, such as scabies, atopic dermatitis, or kidney failure; it can also be a symptom of a malignant condition, such as lymphoma. So far, a scratch was being detected by manually counting the occurances or using a bone-conducting microphone, which is uncomfortably set up. Thus, there is a need for a next-generation system that allows detecting scratches on multiple people simultaneously without invading patients’ lives. Wearable sensors allow the ability to directly collect the data asynchronously from many people and detect activities by applying machine learning algorithms. In this thesis, we propose using multimodal wearable sensors and combining the data from Inertial Measurement Units (IMU), Electric Potential Sensor (EPS) and a microphone using machine learning-based fusion for scalp scratch detection. In this thesis, we describe the results on three problems: (1) the impact of fusing EPS and IMU for scratch detection, (2) the ambient microphone’s ability to detect scratch, (3) the future direction for next-generation scratch detection system. We evaluated the fusion of EPS and IMU on a constrained dataset that mimics an office worker’s daily activities, which we collected in the Wearable Technologies Lab at the University of Sussex. We showed that multimodal fusion is superior to using a wrist-worn IMU solely. For the (2) objective, we collected a small dataset from four people showing that an ambient microphone can be a powerful modality for scratch detection. Finally, we propose a clear direction for future research that involves a wide-scale dataset collection, novel hardware, and powerful Deep Learning algorithms to power the next generation scratch detection system

Introducing VTT-ConIot: A Realistic Dataset for Activity Recognition of Construction Workers Using IMU Devices

Sustainable work aims at improving working conditions to allow workers to effectively extend their working life. In this context, occupational safety and well-being are major concerns, especially in labor-intensive fields, such as construction-related work. Internet of Things and wearable sensors provide for unobtrusive technology that could enhance safety using human activity recognition techniques, and has the potential of improving work conditions and health. However, the research community lacks commonly used standard datasets that provide for realistic and variating activities from multiple users. In this article, our contributions are threefold. First, we present VTT-ConIoT, a new publicly available dataset for the evaluation of HAR from inertial sensors in professional construction settings. The dataset, which contains data from 13 users and 16 different activities, is collected from three different wearable sensor locations.Second, we provide a benchmark baseline for human activity recognition that shows a classification accuracy of up to 89% for a six class setup and up to 78% for a sixteen class more granular one. Finally, we show an analysis of the representativity and usefulness of the dataset by comparing it with data collected in a pilot study made in a real construction environment with real workers