1,922 research outputs found

    Real-time human ambulation, activity, and physiological monitoring:taxonomy of issues, techniques, applications, challenges and limitations

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    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

    Sports Heart Monitors as Reliable Diagnostic Tools for Training Control and Detecting Arrhythmias in Professional and Leisure-Time Endurance Athletes: An Expert Consensus Statement

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    There are countless types of portable heart rate monitoring medical devices used variously by leisure-time exercisers, professional athletes, and chronically ill patients. Almost all the currently used heart rate monitors are capable of detecting arrhythmias, but this feature is not widely known or used among their millions of consumers. The aims of this paper were as follows: (1) to analyze the currently available sports heart rate monitors and assess their advantages and disadvantage in terms of heart rate and rhythm monitoring in endurance athletes; (2) to discuss what types of currently available commercial heart rate monitors are most convenient/adjustable to the needs of different consumers (including occasionally physically active adults and cardiac patients), bearing in mind the potential health risks, especially heart rhythm disturbances connected with endurance training; (3) to suggest a set of "optimal" design features for next-generation smart wearable devices based on the consensus opinion of an expert panel of athletes, coaches, and sports medicine doctors. Ninety-two experts aged 20 years and over, involved in endurance sports on a daily basis, were invited to participate in consensus-building discussions, including 56 long-distance runners, 18 cyclists, nine coaches, and nine physicians (sports medicine specialists, cardiologists, and family medicine doctors). The overall consensus endorsed by these experts indicates that the "optimal" sports heart rate monitor should be a one-piece device of the smartwatch type (with two or more electrodes), with integrated smartphone features, and able to collect and continually transmit data without exhibiting artifacts. It should continuously record at least a single-lead electrocardiography, send an alert after an unexpected fall, be of reasonable weight, come at an affordable price, and be user friendly

    Wearable sensors for respiration monitoring: a review

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    This paper provides an overview of flexible and wearable respiration sensors with emphasis on their significance in healthcare applications. The paper classifies these sensors based on their operating frequency distinguishing between high-frequency sensors, which operate above 10 MHz, and low-frequency sensors, which operate below this level. The operating principles of breathing sensors as well as the materials and fabrication techniques employed in their design are addressed. The existing research highlights the need for robust and flexible materials to enable the development of reliable and comfortable sensors. Finally, the paper presents potential research directions and proposes research challenges in the field of flexible and wearable respiration sensors. By identifying emerging trends and gaps in knowledge, this review can encourage further advancements and innovation in the rapidly evolving domain of flexible and wearable sensors.This work was supported by the Spanish Government (MICINN) under Projects TED2021-131209B-I00 and PID2021-124288OB-I00.Peer ReviewedPostprint (published version

    Updates of Wearing Devices (WDs) In Healthcare, And Disease Monitoring

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     With the rising pervasiveness of growing populace, aging and chronic illnesses consistently rising medical services costs, the health care system is going through a crucial change from the conventional hospital focused system to an individual-focused system. Since the twentieth century, wearable sensors are becoming widespread in medical care and biomedical monitoring systems, engaging consistent estimation of biomarkers for checking of the diseased condition and wellbeing, clinical diagnostics and assessment in biological fluids like saliva, blood, and sweat. Recently, the improvements have been centered around electrochemical and optical biosensors, alongside advances with the non-invasive monitoring of biomarkers, bacteria and hormones, etc. Wearable devices have created with a mix of multiplexed biosensing, microfluidic testing and transport frameworks incorporated with flexible materials and body connections for additional created wear ability and effortlessness. These wearables hold guarantee and are fit for a higher understanding of the relationships between analyte focuses inside the blood or non-invasive biofluids and feedback to the patient, which is fundamentally significant in ideal finding, therapy, and control of diseases. In any case, cohort validation studies and execution assessment of wearable biosensors are expected to support their clinical acceptance. In the current review, we discussed the significance, highlights, types of wearables, difficulties and utilizations of wearable devices for biological fluids for the prevention of diseased conditions and real time monitoring of human wellbeing. In this, we sum up the different wearable devices that are developed for health care monitoring and their future potential has been discussed in detail

    Indutivo: Contact-Based, Object-Driven Interactions with Inductive Sensing

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    We present Indutivo, a contact-based inductive sensing technique for contextual interactions. Our technique recognizes conductive objects (metallic primarily) that are commonly found in households and daily environments, as well as their individual movements when placed against the sensor. These movements include sliding, hinging, and rotation. We describe our sensing principle and how we designed the size, shape, and layout of our sensor coils to optimize sensitivity, sensing range, recognition and tracking accuracy. Through several studies, we also demonstrated the performance of our proposed sensing technique in environments with varying levels of noise and interference conditions. We conclude by presenting demo applications on a smartwatch, as well as insights and lessons we learned from our experience

    A Survey on Multi-Resident Activity Recognition in Smart Environments

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    Human activity recognition (HAR) is a rapidly growing field that utilizes smart devices, sensors, and algorithms to automatically classify and identify the actions of individuals within a given environment. These systems have a wide range of applications, including assisting with caring tasks, increasing security, and improving energy efficiency. However, there are several challenges that must be addressed in order to effectively utilize HAR systems in multi-resident environments. One of the key challenges is accurately associating sensor observations with the identities of the individuals involved, which can be particularly difficult when residents are engaging in complex and collaborative activities. This paper provides a brief overview of the design and implementation of HAR systems, including a summary of the various data collection devices and approaches used for human activity identification. It also reviews previous research on the use of these systems in multi-resident environments and offers conclusions on the current state of the art in the field.Comment: 16 pages, to appear in Evolution of Information, Communication and Computing Systems (EICCS) Book Serie

    Incorporating Modular Arrangement of Predetermined Time Standard with a Wearable Sensing Glove

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    “Performance” – a common watchword in the present age, and that which is optimized through the most functional methodology of investigating the work procedure. This encompassed the auditing, updating of the tasks, while at the same time, applied automation and mechanization. The Modular Arrangement of Predetermined Time Standard (MODAPTS) is a useful application of a work measurement technique that allow a greater variety of work for manufacturing, engineering, and administrative service activities to be measured quickly with ease and accuracy. The MODAPTS, however, made it extremely difficult for engineers to use because it required an ample amount of time to analyze and code the raw data. A new design was proposed to help resolve the conventional system\u27s inadequacy because in MODAPTS, each task cycle of a minute required about 2 hours to calculate and document, and also, the judgment of the analysts varied for the same task. This study aimed to reduce the time taken for the traditional MODAPTS documentation usually took and produced unified results by integrating MODAPTS with a Sensing Wearable Glove while maintaining the same performance. The objective was to introduce an easy, cost-effective solution, and to compare the accuracy of coding between manual and automated calculated MODAPTS while maintaining consistent performance. This study discusses the glove and accompanying software design that detected movements using flex sensors, gyroscopes, microcontrollers, and pressure sensors. These movements were translated into analog data used to create MODAPTS codes as an output, which then sent the data wirelessly using the Bluetooth module. The device designed in this study is capable of sensing gestures for various operations, and the traditional method was compared to the proposed method. This was in turn, validated using the two-way ANOVA analysis. It was observed that the sensor-based glove provided efficient and reliable results, just like the traditional method results while maintaining the same performance

    IoT Enabled Smart Activity Recognition using Machine Learning Methods

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    Internet of Things (IoT) enabled architecture-based devices are becoming accessible worldwide irrespective of the area. But functional settings depend on Internet facilities. In this context, the Healthcare industry took a step forward to automate Human Activity Recognition related concepts using IoT and Machine learning methods. This research used a Nodemcu ESP8266 device to track and communicate human activities acquired using ADXL345 accelerometer sensors. Three volunteers participated in this research, and data were acquired using two accelerometer sensors placed on the hand, wrist, and ankle. Data shared to the cloud- thingspeak.com. Acquired data were analyzed and trained with the Random Forest algorithm and tested with the data, achieving 100% accuracy. This model can be helpful in various applications like elderly patient monitoring, I.C.U., dementia, Alzheimer's, etc
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