1,168 research outputs found
Real-time human ambulation, activity, and physiological monitoring:taxonomy of issues, techniques, applications, challenges and limitations
Automated methods of real-time, unobtrusive, human ambulation, activity, and wellness monitoring and data analysis using various algorithmic techniques have been subjects of intense research. The general aim is to devise effective means of addressing the demands of assisted living, rehabilitation, and clinical observation and assessment through sensor-based monitoring. The research studies have resulted in a large amount of literature. This paper presents a holistic articulation of the research studies and offers comprehensive insights along four main axes: distribution of existing studies; monitoring device framework and sensor types; data collection, processing and analysis; and applications, limitations and challenges. The aim is to present a systematic and most complete study of literature in the area in order to identify research gaps and prioritize future research directions
CaloriNet: From silhouettes to calorie estimation in private environments
We propose a novel deep fusion architecture, CaloriNet, for the online
estimation of energy expenditure for free living monitoring in private
environments, where RGB data is discarded and replaced by silhouettes. Our
fused convolutional neural network architecture is trainable end-to-end, to
estimate calorie expenditure, using temporal foreground silhouettes alongside
accelerometer data. The network is trained and cross-validated on a publicly
available dataset, SPHERE_RGBD + Inertial_calorie. Results show
state-of-the-art minimum error on the estimation of energy expenditure
(calories per minute), outperforming alternative, standard and single-modal
techniques.Comment: 11 pages, 7 figure
Continuous monitoring of vital parameters for clinically valid assessment of human health status
Tese de mestrado integrado, Engenharia BiomĂ©dica e BiofĂsica (Sinais e Imagens MĂ©dicas) Universidade de Lisboa, Faculdade de CiĂŞncias, 2019The lack of devices suitable for acquiring accurate and reliable measures of patients' physiolog-ical signals in a remote and continuous manner together with the advances in data acquisition technol-ogies during the last decades, have led to the emergence of wearable devices for healthcare. Wearable devices enable remote, continuous and long-term health monitoring in unattended setting. In this con-text, the Swiss Federal Laboratories for Material Science and Technology (Empa) developed a wearable system for long-term electrocardiogram measurements, referred to as textile belt. It consists of a chest strap with two embroidered textile electrodes. The validity of Empa’s system for electrocardiogram monitoring has been proven in a clinical setting. This work aimed to assess the validity of the textile belt for electrocardiogram monitoring in a home setting and to supplement the existing system with sensors for respiratory monitoring. Another objective was to evaluate the suitability of the same weara-ble, as a multi-sensor system, for activity monitoring. A study involving 12 patients (10 males and 2 females, interquartile range for age of 48–59 years and for body mass indexes of 28.0–35.5 kg.m-2) with suspected sleep apnoea was carried out. Overnight electrocardiogram was measured in a total of 28 nights. The quality of recorded signals was assessed using signal-to-noise ratio, artefacts detection and PoincarĂ© plots. Study data were compared to data from the same subjects, acquired in the clinical setting. For respiratory monitoring, optical fibre-based sensors of different geometries were integrated into the textile belt. Signal processing algorithms for breathing rate and tidal volume estimation based on respiratory signals acquired by the sensors were developed. Pilot studies were conducted to compare the different approaches for respiratory monitoring. The quality of respiratory signals was determined based on signal segments “sinusoidality”, evaluated through the calculation of the cross-correlation between signal segments and segment-specific reference waves. A method for accelerometry-based lying position recognition was proposed, and the proof of concept of activity intensity classification through the combination of subjects’ inertial acceleration, heart rate and breathing rate data, was presented. Finally, a study with three participants (1 male and 2 females, aged 21 ± 2 years, body mass index of 20.3 ± 1.5 kg.m-2) was conducted to assess the validity of the textile belt for respiratory and activity monitoring. Electrocardiogram signals acquired by the textile belt in the home setting were found to have better quality than the data acquired by the same device in the clinical setting. Although a higher artefact percentage was found for the textile belt, signal-to-noise ratio of electrocardiogram signals recorded by the textile belt in the home setting was similar to that of signals acquired by the gel electrodes in the clinical setting. A good agreement was found between the RR-intervals derived from signals recorded in home and clinical settings. Besides, for artefact percentages greater than 3%, visual assessment of PoincarĂ© plots proved to be effective for the determination of the primary source of artefacts (noise or ectopic beats). Acceleration data allowed posture recognition (i.e. lying or standing/sitting, lying position) with an accuracy of 91% and positive predictive value of 80%. Lastly, preliminary results of physical activity intensity classification yielded high accuracy, showing the potential of the proposed method. The textile belt proved to be appropriate for long-term, remote and continuous monitoring of subjects’ physical and physiological parameters. It can monitor not only electrocardiogram, but also breathing rate, body posture and physical activity intensity, having the potential to be used as tool for disease prediction and diagnose support.Contexto: A falta de dispositivos adequados para a monitorização de sinais fisiolĂłgicos de um modo remoto e contĂnuo, juntamente com avanços tecnolĂłgicos na área de aquisição de dados nas Ăşltimas dĂ©cadas, levaram ao surgimento de wearable devices, i.e. dispositivos vestĂveis, no sector da saĂşde. Wearable devices possibilitam a monitorização do estado de saĂşde, de uma forma remota, contĂnua e de longa duração. Quando feito em ambiente domiciliar, este tipo de monitorização (i.e. contĂnua, remota e de longa duração) tem várias vantagens: diminui a pressĂŁo posta sobre o sistema de saĂşde, reduz despesas associadas ao internamento e acelera a resposta a emergĂŞncias, permitindo deteção precoce e prevenção de condições crĂłnicas. Neste contexto, a Empa, LaboratĂłrios Federais SuĂços de CiĂŞncia e Tecnologia de Materiais, desenvolveu um sistema vestĂvel para a monitorização de eletrocardiograma de longa duração. Este sistema consiste num cinto peitoral com dois elĂ©trodos tĂŞxteis integrados. Os elĂ©trodos tĂŞxteis sĂŁo feitos de fio de polietileno tereftalato revestido com prata e uma ultrafina camada de titânio no topo. De modo a garantir a aquisição de sinais de alta qualidade, o cinto tem nele integrado um reservatĂłrio de água que liberta vapor de água para humidificar os elĂ©trodos. Este reservatĂłrio per-mite a monitorização contĂnua de eletrocardiograma por 5 a 10 dias, sem necessitar de recarga. A vali-dade do cinto para a monitorização de eletrocardiograma em ambiente clĂnico já foi provada. Objetivo: Este trabalho teve por objetivo avaliar a validade do cinto para a monitorização de eletrocar-diograma em ambiente domiciliar e complementar o sistema existente com sensores para monitorização respiratĂłria. Um outro objetivo foi analisar a adequação do cinto, como um sistema multisensor, para monitorização da atividade fĂsica. MĂ©todos: Um estudo com 12 pacientes com suspeita de apneia do sono (10 homens e 2 mulheres, am-plitude interquartil de 48–59 anos para a idade e de 28.0–35.5 kg.m-2 para o Ăndice de massa corporal) foi conduzido para avaliar a qualidade do sinal de eletrocardiograma medido em ambiente domiciliar. O sinal de eletrocardiograma dos pacientes foi monitorizado continuamente, num total de 28 noites. A qualidade dos sinais adquiridos foi analisada atravĂ©s do cálculo da razĂŁo sinal-ruĂdo; da deteção de ar-tefactos, i.e., intervalos RR com um valor inviável de um ponto de vista fisiolĂłgico; e de gráficos de PoincarĂ©, um mĂ©todo de análise nĂŁo linear da distribuição dos intervalos RR registados. Os dados ad-quiridos neste estudo foram comparados com dados dos mesmos pacientes, adquiridos em ambiente hospitalar. Para a monitorização respiratĂłria, sensores feitos de fibra Ăłptica foram integrados no cinto. Al-gorĂtmicos para a estimar a frequĂŞncia respiratĂłria e o volume corrente dos sujeitos tendo por base o sinal medido pelas fibras Ăłpticas foram desenvolvidos neste trabalho. As diferentes abordagens foram comparadas atravĂ©s de estudos piloto. Diferentes mĂ©todos para avaliação da qualidade do sinal adquirido foram sugeridos. Um mĂ©todo de reconhecimento da postura corporal atravĂ©s do cálculo de ângulos de orientação com base na aceleração medida foi proposto. A prova de conceito da determinação da intensidade da atividade fĂsica pela combinação de informações relativas á aceleração inercial e frequĂŞncias cardĂaca e respiratĂłria dos sujeitos, Ă© tambĂ©m apresentada neste trabalho. Um estudo foi conduzido para avaliar a validade do cinto para monitorização da respiração e da atividade fĂsica. O estudo contou com 10 parti-cipantes, dos quais 3 vestiram o cinto para monitorização da respiração (1 homem e 2 mulheres, idade 21 ± 2 anos, Ăndice de massa corporal 20.3 ± 1.5 kg.m-2). Resultados: O estudo feito com pacientes com suspeita de apneia do sono revelou que os sinais eletro-cardiográficos adquiridos pelo cinto em ambiente domiciliar foram de melhor qualidade que os sinais adquiridos pelo mesmo dispositivo em ambiente hospitalar. Uma percentagem de artefacto de 2.87% ±4.14% foi observada para os dados adquiridos pelos elĂ©trodos comummente usados em ambiente hospi-talar, 7.49% ± 10.76% para os dados adquiridos pelo cinto em ambiente domiciliar e 9.66% ± 14.65% para os dados adquiridos pelo cinto em ambiente hospitalar. Embora tenham tido uma maior percenta-gem de artefacto, a razĂŁo sinal-ruĂdo dos sinais eletrocardiográficos adquiridos pelo cinto em ambiente domiciliar foi semelhante á dos sinais adquiridos pelos elĂ©trodos de gel em ambiente hospitalar. Resul-tados sugerem uma boa concordância entre os intervalos RR calculados com base nos eletrocardiogra-mas registados em ambientes hospitalar e domiciliar. AlĂ©m disso, para sinais com percentagem de arte-facto superior a 3%, a avaliação visual dos gráficos de PoincarĂ© provou ser um bom mĂ©todo para a determinação da fonte primária de artefactos (batimentos irregulares ou ruĂdo). A monitorização da aceleração dos sujeitos permitiu o reconhecimento da postura corporal (isto Ă©, deitado ou sentado/em pĂ©) com uma exatidĂŁo de 91% e valor preditivo positivo de 80%. Por fim, a classificação da intensidade da atividade fĂsica baseado na aceleração inercial e frequĂŞncias cardĂaca e respiratĂłria revelou elevada exatidĂŁo, mostrando o potencial desta tĂ©cnica. ConclusĂŁo: O cinto desenvolvido pela Empa provou ser apropriado para monitorização de longa-dura-ção de variáveis fĂsicas e fisiolĂłgicos, de uma forma remota e contĂnua. O cinto permite nĂŁo sĂł monito-rizar eletrocardiograma, mas tambĂ©m frequĂŞncia respiratĂłria, postura corporal e intensidade da atividade fĂsica. Outros estudos devem ser conduzidos para corroborar os resultados e conclusões deste trabalho. Outros sensores poderĂŁo ser integrados no cinto de modo a possibilitar a monitorização de outras vari-áveis fisiolĂłgicas de relevância clĂnica. Este sistema tem o potencial de ser usado como uma ferramenta para predição de doenças e apoio ao diagnĂłstico
Cardiorespiratory fitness estimation using wearable sensors: laboratory and free-living analysis of context-specific submaximal heart rates
In this work, we propose to use pattern recognition methods to determine submaximal heart rate (HR) during specific contexts, such as walking at a certain speed, using wearable sensors in free-living, and use context-specific HR to estimate cardiorespiratory fitness (CRF). CRF of 51 participants was assessed by a maximal exertion test (VO2max). Participants wore a combined accelerometer and HR monitor during a laboratory based simulation of activities of daily living and for two weeks in free-living. Anthropometrics, HR while lying down and walking at predefined speeds in laboratory settings were used to estimate CRF. Explained variance (R2) was 0.64 for anthropometrics, and increased up to 0.74 for context-specific HR (0.73 to 0.78 when including fat-free mass). Then, we developed activity recognition and walking speed estimation algorithms to determine the same contexts (i.e. lying down and walking) in free-living. Context-specific HR in free-living was highly correlated with laboratory measurements (Pearson's r = 0.71-0.75). R2 for CRF estimation was 0.65 when anthropometrics were used as predictors, and increased up to 0.77 when including free-living context-specific HR (i.e. HR while walking at 5.5 km/h). R2 varied between 0.73 and 0.80 when including fat-free mass among the predictors. RMSE was reduced from 354.7 ml/min to 281.0 ml/min by the inclusion of context-specific HR parameters (21% error reduction). We conclude that pattern recognition techniques can be used to contextualize HR in free-living and estimated CRF with accuracy comparable to what can be obtained with laboratory measurements of HR response to walking
Wearable technology: role in respiratory health and disease
In the future, diagnostic devices will be able to monitor a patient's physiological or biochemical parameters continuously, under natural physiological conditions and in any environment through wearable biomedical sensors. Together with apps that capture and interpret data, and integrated enterprise and cloud data repositories, the networks of wearable devices and body area networks will constitute the healthcare's Internet of Things. In this review, four main areas of interest for respiratory healthcare are described: pulse oximetry, pulmonary ventilation, activity tracking and air quality assessment. Although several issues still need to be solved, smart wearable technologies will provide unique opportunities for the future or personalised respiratory medicine
Fall Prediction and Prevention Systems: Recent Trends, Challenges, and Future Research Directions.
Fall prediction is a multifaceted problem that involves complex interactions between physiological, behavioral, and environmental factors. Existing fall detection and prediction systems mainly focus on physiological factors such as gait, vision, and cognition, and do not address the multifactorial nature of falls. In addition, these systems lack efficient user interfaces and feedback for preventing future falls. Recent advances in internet of things (IoT) and mobile technologies offer ample opportunities for integrating contextual information about patient behavior and environment along with physiological health data for predicting falls. This article reviews the state-of-the-art in fall detection and prediction systems. It also describes the challenges, limitations, and future directions in the design and implementation of effective fall prediction and prevention systems
Application of data fusion techniques and technologies for wearable health monitoring
Technological advances in sensors and communications have enabled discrete integration into everyday objects, both in the home and about the person. Information gathered by monitoring physiological, behavioural, and social aspects of our lives, can be used to achieve a positive impact on quality of life, health, and well-being. Wearable sensors are at the cusp of becoming truly pervasive, and could be woven into the clothes and accessories that we wear such that they become ubiquitous and transparent. To interpret the complex multidimensional information provided by these sensors, data fusion techniques are employed to provide a meaningful representation of the sensor outputs. This paper is intended to provide a short overview of data fusion techniques and algorithms that can be used to interpret wearable sensor data in the context of health monitoring applications. The application of these techniques are then described in the context of healthcare including activity and ambulatory monitoring, gait analysis, fall detection, and biometric monitoring. A snap-shot of current commercially available sensors is also provided, focusing on their sensing capability, and a commentary on the gaps that need to be bridged to bring research to market
Design and Application of Wireless Body Sensors
Hörmann T. Design and Application of Wireless Body Sensors. Bielefeld: Universität Bielefeld; 2019
Predicting ambulatory energy expenditure in lower limb amputees using multi-sensor methods
PurposeTo assess the validity of a derived algorithm, combining tri-axial accelerometry and heart rate (HR) data, compared to a research-grade multi-sensor physical activity device, for the estimation of ambulatory physical activity energy expenditure (PAEE) in individuals with traumatic lower-limb amputation.MethodsTwenty-eight participants [unilateral (n = 9), bilateral (n = 10) with lower-limb amputations, and non-injured controls (n = 9)] completed eight activities; rest, ambulating at 5 progressive treadmill velocities (0.48, 0.67, 0.89, 1.12, 1.34m.s-1) and 2 gradients (3 and 5%) at 0.89m.s-1. During each task, expired gases were collected for the determination of and subsequent calculation of PAEE. An Actigraph GT3X+ accelerometer was worn on the hip of the shortest residual limb and, a HR monitor and an Actiheart (AHR) device were worn on the chest. Multiple linear regressions were employed to derive population-specific PAEE estimated algorithms using Actigraph GT3X+ outputs and HR signals (GT3X+HR). Mean bias±95% Limits of Agreement (LoA) and error statistics were calculated between criterion PAEE (indirect calorimetry) and PAEE predicted using GT3X+HR and AHR.ResultsBoth measurement approaches used to predict PAEE were significantly related (Pr = 0.92, bilateral; r = 0.93, and control; r = 0.91, and AHR; unilateral; r = 0.86, bilateral; r = 0.81, and control; r = 0.67). Mean±SD percent error across all activities were 18±14%, 15±12% and 15±14% for the GT3X+HR and 45±20%, 39±23% and 34±28% in the AHR model, for unilateral, bilateral and control groups, respectively.ConclusionsStatistically derived algorithms (GT3X+HR) provide a more valid estimate of PAEE in individuals with traumatic lower-limb amputation, compared to a proprietary group calibration algorithm (AHR). Outputs from AHR displayed considerable random error when tested in a laboratory setting in individuals with lower-limb amputation.</div
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