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نتیجه جستجو - wearable sensors

تعداد مقالات یافته شده: 10
ردیف عنوان نوع
1 A conceptual IoT-based early-warning architecture for remote monitoring of COVID-19 patients in wards and at home
یک معماری مفهومی هشدار اولیه مبتنی بر اینترنت اشیا برای نظارت از راه دور بیماران COVID-19 در بخش ها و در خانه-2022
Due to the COVID-19 pandemic, health services around the globe are struggling. An effective system for monitoring patients can improve healthcare delivery by avoiding in-person contacts, enabling early-detection of severe cases, and remotely assessing patients’ status. Internet of Things (IoT) technologies have been used for monitoring patients’ health with wireless wearable sensors in different scenarios and medical conditions, such as noncommunicable and infectious diseases. Combining IoT-related technologies with early-warning scores (EWS) commonly utilized in infirmaries has the potential to enhance health services delivery significantly. Specifically, the NEWS-2 has been showing remarkable results in detecting the health deterioration of COVID-19 patients. Although the literature presents several approaches for remote monitoring, none of these studies proposes a customized, complete, and integrated architecture that uses an effective early-detection mechanism for COVID-19 and that is flexible enough to be used in hospital wards and at home. Therefore, this article’s objective is to present a comprehensive IoT-based conceptual architecture that addresses the key requirements of scalability, interoperability, network dynamics, context discovery, reliability, and privacy in the context of remote health monitoring of COVID-19 patients in hospitals and at home. Since remote monitoring of patients at home (essential during a pandemic) can engender trust issues regarding secure and ethical data collection, a consent management module was incorporated into our architecture to provide transparency and ensure data privacy. Further, the article details mechanisms for supporting a configurable and adaptable scoring system embedded in wearable devices to increase usefulness and flexibility for health care professions working with EWS.
keywords: نظارت از راه دور | کووید-۱۹ | اخبار-2 | معماری | رضایت | اینترنت اشیا | Remote monitoring | COVID-19 | NEWS-2 | Architecture | Consent | IoT
مقاله انگلیسی
2 A real-time tennis level evaluation and strokes classification system based on the Internet of Things
یک سیستم ارزیابی سطح تنیس در زمان واقعی و طبقه بندی ضربه ها بر اساس اینترنت اشیا-2022
In this study a single wearable inertial measurement unit (IMU) and machine learning method- ologies were used to conduct player level evaluation and classification five prototype tennis strokes in real-time. The International Tennis Number (ITN) test was used to verify the accuracy of this IoT system in evaluating participant level. We conducted the ITN test on thirty-six par- ticipants and conducted one-way ANOVA on the ITN test results using IBM SPSS 26. The IMU in this study contained a tri-axis accelerometer (± 16 g) and tri-axis gyroscope (± 2000◦ /s) worn on the participants’ wrist connected to a wireless low-energy Bluetooth smart-phone with data sent to the computer terminal by cloud storage. Data processing including preprocessing, segmenta- tion, feature extraction, dimensionality reduction and classification using Support Vector Ma- chines (SVM), K-nearest neighbor (K-NN) and Naive Bayes (NB) algorithms. One-way ANOVA analysis predicting participants’ ITN level and ITN field test scores yielded p < 0.001 at the three different skill levels tested. SVM (MinMax), SVM (Standardiser) and SVM (MaxAbsScaler) clas- sified unique tennis strokes precision and recall factors at the three different skill levels reliably yielded in f1-scores above 0.90 for serve, forehand and backhand, with f1-scores for forehand and backhand volley scores falling below that. The results of this study suggest using a single six-axial 50 Hz IMU in combination with SVM and SVM + PCA represents a significant step towards a more reliable wearable tennis stroke performance and skill level real-time evaluation and feedback technology.
keywords: اینترنت اشیا | جمع آوری داده ها | پردازش داده ها | یادگیری ماشین | اپلیکیشن موبایل | تنیس | سنسورهای پوشیدنی | ارتباطات بی سیم | Internet of Things | Data collection | Data processing | Machine learning | Mobile application | Tennis | Wearable sensors | Wireless communication
مقاله انگلیسی
3 GaitCode: Gait-based continuous authentication using multimodal learning and wearable sensors
GaitCode: احراز هویت پیوسته مبتنی بر راه رفتن با استفاده از یادگیری چند حالته و حسگرهای پوشیدنی-2021
The ever-growing threats of security and privacy loss from unauthorized access to mobile devices have led to the development of various biometric authentication methods for easier and safer data access. Gait-based authentication is a popular biometric authentication as it utilizes the unique patterns of human locomotion and it requires little cooperation from the user. Existing gait-based biometric authentication methods however suffer from degraded performance when using mobile devices such as smart phones as the sensing device, due to multiple reasons, such as increased accelerometer noise, sensor orientation and positioning, and noise from body movements not related to gait. To address these drawbacks, some researchers have adopted methods that fuse information from multiple accelerometer sensors mounted on the human body at different lo- cations. In this work we present a novel gait-based continuous authentication method by applying multimodal learning on jointly recorded accelerometer and ground contact force data from smart wearable devices. Gait cycles are extracted as a basic authentication element, that can continuously authenticate a user. We use a network of auto-encoders with early or late sensor fusion for feature extraction and SVM and soft max for classification. The effectiveness of the proposed approach has been demonstrated through extensive experiments on datasets collected from two case studies, one with commercial off-the-shelf smart socks and the other with a medical-grade research prototype of smart shoes. The evaluation shows that the proposed approach can achieve a very low Equal Error Rate of 0.01% and 0.16% for identification with smart socks and smart shoes respectively, and a False Acceptance Rate of 0.54%–1.96% for leave-one-out authentication.
Keywords: Biometric authentication | Gait authentication | Autoencoders | Sensor fusion | Multimodal learning | Wearable sensors
مقاله انگلیسی
4 A review of multimodal human activity recognition with special emphasis on classification, applications, challenges and future directions
مروری بر به رسمیت شناختن فعالیتهای چندمنظوره انسان با تأکید ویژه بر طبقه بندی ، کاربردها ، چالشها و جهت های آینده-2021
Human activity recognition (HAR) is one of the most important and challenging problems in the computer vision. It has critical application in wide variety of tasks including gaming, human– robot interaction, rehabilitation, sports, health monitoring, video surveillance, and robotics. HAR is challenging due to the complex posture made by the human and multiple people interaction. Various artifacts that commonly appears in the scene such as illuminations variations, clutter, occlusions, background diversity further adds the complexity to HAR. Sensors for multiple modalities could be used to overcome some of these inherent challenges. Such sensors could include an RGB-D camera, infrared sensors, thermal cameras, inertial sensors, etc. This article introduces a comprehensive review of different multimodal human activity recognition methods where different types of sensors being used along with their analytical approaches and fusion methods. Further, this article presents classification and discussion of existing work within seven rational aspects: (a) what are the applications of HAR; (b) what are the single and multi-modality sensing for HAR; (c) what are different vision based approaches for HAR; (d) what and how wearable sensors based system contributes to the HAR; (e) what are different multimodal HAR methods; (f) how a combination of vision and wearable inertial sensors based system contributes to the HAR; and (g) challenges and future directions in HAR. With a more and comprehensive understanding of multimodal human activity recognition, more research in this direction can be motivated and refined.© 2021 Elsevier B.V. All rights reserved.
Keywords: Activity recognition | Computer vision | Wearable sensors | Fusion of vision and inertial sensors | Smart-shoes | Multimodality
مقاله انگلیسی
5 Real-time ECG monitoring using compressive sensing on a heterogeneous multicore edge-device
نظارت بر زمان واقعی نوار قلب با استفاده از سنجش فشاری در دستگاه لبه چند هسته ای ناهمگن-2020
In a typical ambulatory health monitoring systems, wearable medical sensors are deployed on the hu- man body to continuously collect and transmit physiological signals to a nearby gateway that forward the measured data to the cloud-based healthcare platform. However, this model often fails to respect the strict requirements of healthcare systems. Wearable medical sensors are very limited in terms of battery lifetime, in addition, the system reliance on a cloud makes it vulnerable to connectivity and la- tency issues. Compressive sensing (CS) theory has been widely deployed in electrocardiogramme ECG monitoring application to optimize the wearable sensors power consumption. The proposed solution in this paper aims to tackle these limitations by empowering a gateway-centric connected health solution, where the most power consuming tasks are performed locally on a multicore processor. This paper ex- plores the efficiency of real-time CS-based recovery of ECG signals on an IoT-gateway embedded with ARM’s big. little TM multicore for different signal dimension and allocated computational resources. Ex- perimental results show that the gateway is able to reconstruct ECG signals in real-time. Moreover, it demonstrates that using a high number of cores speeds up the execution time and it further optimizes energy consumption. The paper identifies the best configurations of resource allocation that provides the optimal performance. The paper concludes that multicore processors have the computational capacity and energy efficiency to promote gateway-centric solution rather than cloud-centric platforms.
Keywords: Ambulatory ECG monitoring | Heterogeneous multicore solution | Compressive sensing | Edge computing
مقاله انگلیسی
6 Machine Learning Groups Patients by Early Functional Improvement Likelihood Based on Wearable Sensor Instrumented Preoperative Timed-Up-and-Go Tests
گروه های یادگیری ماشینی بیماران براساس احتمال بهبود عملکرد زودهنگام بر اساس سنسورهای پوشیدنی ابزار تست شده به موقع قبل و بعد از عمل-2019
Background: Wearable sensors permit efficient data collection and unobtrusive systems can be used for instrumenting knee patients for objective assessment. Machine learning can be leveraged to parse the abundant information these systems provide and segment patients into relevant groups without specifying group membership criteria. The objective of this study is to examine functional parameters influencing favorable recovery outcomes by separating patients into functional groups and tracking them through clinical follow-ups. Methods: Patients undergoing primary unilateral total knee arthroplasty (n ¼ 68) completed instrumented timed-up-and-go tests preoperatively and at their 2-, 6-, and 12-week follow-up appointments. A custom wearable system extracted 55 metrics for analysis and a K-means algorithm separated patients into functionally distinguished groups based on the derived features. These groups were analyzed to determine which metrics differentiated most and how each cluster improved during early recovery. Results: Patients separated into 2 clusters (n ¼ 46 and n ¼ 22) with significantly different test completion times (12.6 s vs 21.6 s, P < .001). Tracking the recovery of both groups to their 12-week follow-ups revealed 64% of one group improved their function while 63% of the other maintained preoperative function. The higher improvement group shortened their test times by 4.94 s, (P ¼ .005) showing faster recovery while the other group did not improve above a minimally important clinical difference (0.87 s, P ¼.07). Features with the largest effect size between groups were distinguished as important functional parameters. Conclusion: This work supports using wearable sensors to instrument functional tests during clinical visits and using machine learning to parse complex patterns to reveal clinically relevant parameters.
Keywords: total knee arthroplasty | wearable sensors | machine learning | functional testing | early recovery
مقاله انگلیسی
7 Lifelogging Data Validation Model for Internet of Things Enabled Personalized Healthcare
مدل اعتبار سنجی داده های عمر برای اینترنت اشیاء فعال سلامت شخصی شده -2017
Internet of Things (IoT) technology offers opportunities to monitor lifelogging data by a variety of assets, like wearable sensors, mobile apps, etc. But due to heterogeneity of connected devices and diverse human life patterns in an IoT environment, lifelogging personal data contains huge uncertainty and are hardly used for healthcare studies. Effective validation of lifelogging personal data for longitudinal health assessment is demanded. In this paper, lifelogging physical activity (LPA) is taken as a target to explore how to improve the validity of lifelogging data in an IoT enabled healthcare system. A rule-based adaptive LPA validation (LPAV) model, LPAV-IoT, is proposed for eliminating irregular uncertainties (IUs) and estimating data reliability in IoT healthcare environments. A methodology specifying four layers and three modules in LPAV-IoT is presented for analyzing key factors impacting validity of LPA. A series of validation rules are designed with uncertainty threshold parameters and reliability indicators and evaluated through experimental investigations. Following LPAV-IoT, a case study on a personalized healthcare platform myhealthavatar connecting three state-of-the-art wearable devices and mobile apps are carried out. The results reflect that the rules provided by LPAV-IoT enable efficiently filtering at least 75% of IU and adaptively indicating the reliability of LPA data on certain condition of IoT environments.
Index Terms: Data validation | Internet of Things (IoT) | personalized healthcare | physical activity
مقاله انگلیسی
8 Survey of main challenges (security and privacy) in wireless body area networks for healthcare applications
بررسی چالش های اصلی (امنیت و حریم خصوصی) در شبکه های بی سیم بدن برای برنامه های بهداشت و درمان-2017
Wireless Body Area Network (WBAN) is a new trend in the technology that provides remote mechanism to monitor and collect patient’s health record data using wearable sensors. It is widely recognized that a high level of system security and privacy play a key role in protecting these data when being used by the healthcare professionals and during storage to ensure that patient’s records are kept safe from intruder’s danger. It is therefore of great interest to discuss security and privacy issues in WBANs. In this paper, we reviewed WBAN communication architecture, security and privacy requirements and security threats and the primary challenges in WBANs to these systems based on the latest standards and publications. This paper also covers the state-of-art security measures and research in WBAN. Finally, open areas for future research and enhancements are explored.© 2016 Production and hosting by Elsevier B.V. on behalf of Faculty of Computers and Information, Cairo University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).Contents
Keywords:Wireless Body Area Network (WBAN) | Security | Privacy | Threats | Attacks | Healthcare systems
مقاله انگلیسی
9 Energy-aware and quality-driven sensor management for green mobile crowd sensing
مدیریت سنسور انرژی آگاه و کیفیت محور برای سنجش سبز جمعیت سیار-2016
Mobile Crowd Sensing (MCS) is a novel class of Internet of Things applications which exploits the inherent mobility of wearable sensors and mobile devices to observe phenomena of common interest, typically over large geographical areas (e.g. traffic conditions, air pollution, noise in urban areas). Since MCS applications generate large amounts of sensed data which is collected and preprocessed by devices with limited energy supply, challenges arise with respect to sensor management to ensure an energy- aware and quality-driven data acquisition process. In this paper we present a framework for Green Mobile Crowd Sensing (G-MCS) which utilizes a quality-driven sensor management function to continuously select the k-best sensors for a predefined sensing task. Our G-MCS solution utilizes a cloud-based architecture centered around a publish/subscribe communication model to enable the interaction of mobile devices with the cloud for energy-aware MCS. In particular, it obviates redundant sensor activity while satisfying sensing coverage requirements and sensing quality, and consequently reduces the overall energy consumption of an MCS application. We present a model for G-MCS and evaluate its energy savings for different application requirements and geographical sensor distribution scenarios. Furthermore, our model evaluation on a real data set shows that in certain identified cases, significant energy consumption reductions can be achieved by utilizing the proposed framework, which opens the door for green solutions within the area of MCS applications.& 2015 Elsevier Ltd. All rights reserved.
Keywords: Mobile crowd sensing | Internet of Things | Energy-aware system
مقاله انگلیسی
10 A wireless potentiostat for mobile chemical sensing and biosensing
پتانسیواستات بی سیم برای سنجش شیمیایی همراه و biosensing-2015
Wireless chemical sensors are used as analytical devices in homeland defence, home-based healthcare, food logistics and more generally for the Sensor Internet of Things (SIoT). Presented here is a battery- powered and highly portable credit-card size potentiostat that is suitable for performing mobile and wearable amperometric electrochemical measurements with seamless wireless data transfer to mobile computing devices. The mobile electrochemical analytical system has been evaluated in the laboratory with a model redox system – the reduction of hexacyanoferrate(III) – and also with commercially available enzymatic blood-glucose test-strips. The potentiostat communicates wirelessly with mobile devices such as tablets or Smartphones by near-field communication (NFC) or with personal computers by radio-frequency identification (RFID), and thus provides a solution to the ‘missing link’ in connectivity that often exists between low-cost mobile and wearable chemical sensors and ubiquitous mobile com- puting products. The mobile potentiostat has been evaluated in the laboratory with a set of proof-of- concept experiments, and its analytical performance compared with a commercial laboratory po- tentiostat (R2 ¼ 0.9999). These first experimental results demonstrate the functionality of the wireless potentiostat and suggest that the device could be suitable for wearable and point-of-sample analytical measurements. We conclude that the wireless potentiostat could contribute significantly to the ad- vancement of mobile chemical sensor research and adoption, in particular for wearable sensors in healthcare and sport physiology, for wound monitoring and in mobile point-of-sample diagnostics as well as more generally as a part of the Sensor Internet of Things.& 2015 Elsevier B.V. All rights reserved.
Keywords: Potentiostat | Amperometry | Biosensor | Glucose test-strip | Radio-frequency identification | Near-field communication | Wireless sensor | Internet of things
مقاله انگلیسی
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