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Artigos de revistas sobre o assunto "Best Golf Smart Watches"
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Wittenberg, Th, R. Koch, N. Pfeiffer, N. Lang, M. Struck, O. Amft e B. Eskofier. "Evaluation of HRV estimation algorithms from PPG data using neural networks". Current Directions in Biomedical Engineering 6, n.º 3 (1 de setembro de 2020): 505–9. http://dx.doi.org/10.1515/cdbme-2020-3130.
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AbstractHeart rate variability (HRV) is a powerful measure to gain information on the activation of the central nervous system and is thus a strong indicator for the overall health and emotional state of a person. Currently, the gold standard for HRV analysis is the examination of R-peaks in electrocardiograms (ECG), which requires a placement of electrodes on the torso. This is often impracticable, especially for the use in daily routines or 24/7 measurements. Photoplethysmograms (PPG) are an alternative to ECG assessment and are easier to acquire, e.g. by using fitness trackers or smart watches. Nevertheless, PPG data is more susceptible to motion artifacts. Hence, goal of this work is to develop and evaluate an artificial neural network (ANN) approach to estimate the R-peak locations in complex PPG signals. Public data collections were used as benchmark to compare our ANN-based approach to state-of-the-art methods. Results show that ANNs can improve HRV estimation during motion. HRV estimations from baseline methods (decision-tree based and automatic multiscalebased peak detection) were compared with the best performing neural network (3L-GRU) using the TROIKA dataset with respect to reference parameters obtained from a manual selection of the peaks in ECG data. In most cases, the neural network based HRV estimation was closer to the reference HRV compared to baseline methods (lower μ and σ ) Also, σ is smaller for the best performing ANN approach across most HRV parameters. Inclusion of another PPG or acceleration channel did not affect HRV estimation. Although, the neural network learning approach outperforms conventional methods, the examined PPG-based HRV estimation has still accuracy limitations. Nonetheless, the proposed estimation approach opens up new directions for further improvement.
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Hedayatnia, Mostafa. "Smart Materials for Silent Alarms". Mechanical Engineering 120, n.º 02 (1 de fevereiro de 1998): 88–89. http://dx.doi.org/10.1115/1.1998-feb-7.
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This article discusses the recent demand for silent alarms. If the device is kept close to a user’s body, such as on her\his wrist or in a back pocket, vibration is well suited for this type of alarm. The technology has been used for sometime in pagers, for example, and the field could someday be expanded to watches and a variety of medical equipment. The most common form of vibrating alert involves a motor that rotates an eccentric weight in an electromagnetic field. However, the smallest of these motors is still too large to fit in watches and other consumer products. Piezoceramic materials can produce motion by receiving electric potential across their polarized surfaces. Using finite-element analysis and optimization, engineers at Philips Corp. in Sunnyvale, Calif., have developed a miniature vibrator/piezo-bender that consumes only 8 milliamperes. In addition to optimization and actual shake tests, ergonomic research has been done to find the best way of improving the vibration performance of these silent alarms. Although the piezo-vibrator device shook strongly enough to get the wearer's attention in most environmental circumstances, that awareness improved greatly when vibrations were pulsed or interrupted.
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Chang, Ray-I., Tzu-Chieh Lin e Jeng-Wei Lin. "A Vehicle Passive Entry Passive Start System with the Intelligent Internet of Things". Electronics 13, n.º 13 (26 de junho de 2024): 2506. http://dx.doi.org/10.3390/electronics13132506.
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With the development of sensor and communication technologies, the Internet of Things (IoT) subsystem is gradually becoming a crucial part in vehicles. It can effectively enhance functionalities of vehicles. However, new attack types are also emerging. For example, a driver with the smart key in their pocket can push the start button to start a car. At the same time, security issues in the push-to-start scenario are pervasive, such as smart key forgery. In this study, we propose a vehicle Passive Entry Passive Start (PEPS) system that adopts deep learning algorithms to recognize the driver using the electrocardiogram (ECG) signals measured on the driver’s smart watch. ECG signals are used for personal identification. Smart watches, serving as new smart keys of the PEPS system, can improve convenience and security. In the experiment, we consider commercial smart watches capable of sensing ECG signals. The sample rate and precision are typically lower than those of a 12-lead ECG used in hospitals. The experimental results show that Long Short-Term Memory (LSTM) models achieve the best accuracy score for identity recognition (91%) when a single ECG cycle is used. However, it takes at least 30 min for training. The training of a personalized Auto Encoder model takes only 5 min for each subject. When 15 continuous ECG cycles are sensed and used, this can achieve 100% identity accuracy. As the personalized Auto Encoder model is an unsupervised learning one-class recognizer, it can be trained using only the driver’s ECG signal. This will simplify the management of ECG recordings extremely, as well as the integration of the proposed technology into PEPS vehicles. A FIDO (Fast Identify Online)-like environment for the proposed PEPS system is discussed. Public key cryptography is adopted for communication between the smart watch and the PEPS car. The driver is first verified on the smart watch via local ECG biometric authentication, and then identified by the PEPS car. Phishing attacks, MITM (man in the middle) attacks, and replay attacks can be effectively prevented.
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Sharma, Richa, Shalli Rani e Deepali Gupta. "Stress Detection Using Machine Learning Classifiers in Internet of Things Environment". Journal of Computational and Theoretical Nanoscience 16, n.º 10 (1 de outubro de 2019): 4214–19. http://dx.doi.org/10.1166/jctn.2019.8502.
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Over the years, Recommender systems have emerged as a means to provide relevant content to the users, be it in the field of entertainment, social-network, health, education, travel, food or tourism. Further,with the expeditious development of Big Data and Internet of Things (IoT), technology has successfully associated with our everyday life activities with smart healthcare being one. The global acceptance towards smart watches, wearable devices or wearable biosensors have paved the way for the evolution of novel applications for personalized eHealth and mHealth technologies. The data gathered by wearables can further be interpreted using Machine learning algorithms and shared with healthcare experts to provide suitable recommendations. In this work, we study the role of recommender systems in IoT and Cloud and vice-versa. Further, we have analyzed the performance of different machine learning techniques on SWELL dataset. Based on the results, it is observed that 2 Class Neural network performs the best with 98% accuracy.
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C, Vijesh Joe, e Jennifer S. Raj. "Location-based Orientation Context Dependent Recommender System for Users". March 2021 3, n.º 1 (10 de abril de 2021): 14–23. http://dx.doi.org/10.36548/jtcsst.2021.1.002.
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As the technology revolving around IoT sensors develops in a rapid manner, the subsequent social networks that are essential for the growth of the system will be utilized as a means to filter the objects that are preferred by the consumers. The ultimate purpose of the system is to give the customers personalized recommendations based on their preference. Similarly, the location and orientation will also play a crucial role in identifying the preference of the customer is a more efficient manner. Almost all social networks make use of location information to provide better services to the users based on the research performed. Hence there is a need for developing a recommender system that is dependent on location. In this paper, we have incorporated a recommender system that makes use of recommender algorithm that is personalized to take into consideration the context of the user. The preference of the user is analysed with the help of IoT smart devices like the smart watches, Google home, smart phones, ipads etc. The user preferences are obtained from these devices and will enable the recommender system to gauge the best resources. The results based on evaluation are compared with that of the content-based recommender algorithm and collaborative filtering to enable the recommendation engine’s power.
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Sriram, Arram, G. Sekhar Reddy, G. L. Anand Babu, Prashant Bachanna, Singh Chhabra Gurpreet, Vishal Moyal, D. C. Shubhangi et al. "A Smart Solution for Cancer Patient Monitoring Based on Internet of Medical Things Using Machine Learning Approach". Evidence-Based Complementary and Alternative Medicine 2022 (24 de junho de 2022): 1–6. http://dx.doi.org/10.1155/2022/2056807.
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The Internet of Medical Things (IoMT) is a huge, exciting new phenomenon that is changing the world of technology and innovating various industries, including healthcare. It has specific applications and changes in the medical world based on what can be done for clinical workflow models. The first and most fundamental thing that IoMT does in healthcare is to bring a flood of new data into medical processes. In this study, an efficient Internet of Medical Things based cancer detection model was proposed. In fact, for many, new fitness monitors and watches are one of the best examples on the Internet; these mobile, portable, wearable devices can record real-time heart rate, blood pressure, and eye movement of cancer patients. These details are sent to doctors or anywhere else. The proposed method leads to a kind of big data renaissance in the health service. The proposed model gets more accuracy while comparing with the existing models. This will help the doctors to analyze the patients’ health report and provides better treatment.
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Shukri, Sarah E., Rizik Al-Sayyed, Hamed Al-Bdour, Esraa Alhenawi, Tamara Almarabeh e Hiba Mohammad. "Internet of Things: Underwater routing based on user’s health status for smart diving". International Journal of Data and Network Science 7, n.º 4 (2023): 1715–28. http://dx.doi.org/10.5267/j.ijdns.2023.7.019.
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Technological advancements affect everyday life; they benefited our daily routines, habits, and activities. Underwater diving is one of the most interesting and attractive activities for tourists worldwide but could be risky and challenging. When paths are not clear, diving might take additional time and effort leading to some health problems. Thus, providing divers with proper direction information to surf underwater can be useful and helpful. Also, monitoring diverse health statuses and alerting them in case of any undesirable condition can increase their safety. Smart devices such as mobiles, watches, sensor devices, cellular networks along with the Internet of Things (IoT) can all provide location-based services. Such services can help in providing the best path for the divers and monitor their health status during diving. This paper proposes a new underwater routing approach, called Underwater Routing for Smart Diving “URSD”, which provides divers with routing information to visit underwater cultural or natural resources and monitors their health status during the diving period. The URSD approach was simulated and compared with the shortest path. Results showed that the URSD helped divers to route within paths that have a larger number of nodes, furthermore, it could enhance and improve divers experience and help them mitigate underwater risks.
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Khwaounjoo, Prashanna, Gurleen Singh, Sophie Grenfell, Burak Özsoy, Michael R. MacAskill, Tim J. Anderson e Yusuf O. Çakmak. "Non-Contact Hand Movement Analysis for Optimal Configuration of Smart Sensors to Capture Parkinson’s Disease Hand Tremor". Sensors 22, n.º 12 (18 de junho de 2022): 4613. http://dx.doi.org/10.3390/s22124613.
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Parkinson’s disease affects millions worldwide with a large rise in expected burden over the coming decades. More easily accessible tools and techniques to diagnose and monitor Parkinson’s disease can improve the quality of life of patients. With the advent of new wearable technologies such as smart rings and watches, this is within reach. However, it is unclear what method for these new technologies may provide the best opportunity to capture the patient-specific severity. This study investigates which locations on the hand can be used to capture and monitor maximal movement/tremor severity. Using a Leap Motion device and custom-made software the volume, velocity, acceleration, and frequency of Parkinson’s (n = 55, all right-handed, majority right-sided onset) patients’ hand locations (25 joints inclusive of all fingers/thumb and the wrist) were captured simultaneously. Distal locations of the right hand, i.e., the ends of fingers and the wrist showed significant trends (p < 0.05) towards having the largest movement velocities and accelerations. The right hand, compared with the left hand, showed significantly greater volumes, velocities, and accelerations (p < 0.01). Supplementary analysis showed that the volumes, acceleration, and velocities had significant correlations (p < 0.001) with clinical MDS-UPDRS scores, indicating the potential suitability of using these metrics for monitoring disease progression. Maximal movements at the distal hand and wrist area indicate that these locations are best suited to capture hand tremor movements and monitor Parkinson’s disease.
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Alekhya, B. "Design and Development of Wearable Antenna: A Literature review". International Journal for Research in Applied Science and Engineering Technology 10, n.º 6 (30 de junho de 2022): 2124–27. http://dx.doi.org/10.22214/ijraset.2022.44247.
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Abstract: Wearable electronics and its related technologie’s demand is growing tremendously in recent years .The major developments that accelerated this growth are decrement in the size of wireless devices, advancement of high-speed wireless networks, accessibility of ultra-compact, evolving battery technologies. Most of the Wearable electronic devices for example smart watches, wristbands, rings etc; we use various types of antennas to sense, fetch, and exchange data wirelessly. As one of the best antennas in many terms like compact size, flexibility and easy design is the micrpstrip patch antenna, therefore designing of the same is done. We are designing a microstrip patch antenna as a wearable antenna for its main usage in WBAN (Wideband Body Area Network). The software of CST studio suite which is a 3D electromagnetic analyzer is udes to design the required antenna, where the methodology of cascading of two antennas is taken into account for the design with required gain.
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Shim, Dongsub, Zheda Mai, Jihwan Jeong, Scott Sanner, Hyunwoo Kim e Jongseong Jang. "Online Class-Incremental Continual Learning with Adversarial Shapley Value". Proceedings of the AAAI Conference on Artificial Intelligence 35, n.º 11 (18 de maio de 2021): 9630–38. http://dx.doi.org/10.1609/aaai.v35i11.17159.
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As image-based deep learning becomes pervasive on every device, from cell phones to smart watches, there is a growing need to develop methods that continually learn from data while minimizing memory footprint and power consumption. While memory replay techniques have shown exceptional promise for this task of continual learning, the best method for selecting which buffered images to replay is still an open question. In this paper, we specifically focus on the online class-incremental setting where a model needs to learn new classes continually from an online data stream. To this end, we contribute a novel Adversarial Shapley value scoring method that scores memory data samples according to their ability to preserve latent decision boundaries for previously observed classes (to maintain learning stability and avoid forgetting) while interfering with latent decision boundaries of current classes being learned (to encourage plasticity and optimal learning of new class boundaries). Overall, we observe that our proposed ASER method provides competitive or improved performance compared to state-of-the-art replay-based continual learning methods on a variety of datasets.
Livros sobre o assunto "Best Golf Smart Watches"
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Bill, Fields, ed. Mind over golf: Play your best by thinking smart. New York: Macmillan Pub. Co., 1993.
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Arnold, J. Douglas, e Zach Meston. Awesome Super Nintendo Secrets 2. Lahaina, USA: Sandwich Islands Publishing, 1993.
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Best garmin smart watches. Barcodeliveorg, 2023.
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Super NES Games Secrets, Greatest Tips. Rocklin, CA: Prima Publishing, 1993.
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Awesome Super Nintendo Secrets II. Bournermouth, U.K.: Paragon Publishing, Limited, 1993.
Encontre o texto completo da fonteTrabalhos de conferências sobre o assunto "Best Golf Smart Watches"
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Shejwal, Amogh, Ashutosh Srivastava, Sandeep Medikonda e S. Babu Aminjikarai. "Reliability Analysis of Flexible PCBs". In ASME 2021 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/imece2021-71540.
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Abstract Flexible Circuit Boards (FCB) are ubiquitous in most electronic devices used today. These are utilized in mobile phones, display cables in laptops, cameras, smart watches, robotic arms and more. They are mainly used in applications where space, flexibility and construction constraints limit the usage of conventional Printed Circuit Board (PCB). While FCBs offer numerous advantages over traditional PCBs, like enhanced reliability, capabilities, reduced weight, and lesser space utilization, on the other hand, they present different set of challenges like assembly, installation, and difficulty in repairing and reworking after installation. The flexes are generally bent at several points before conforming to the installed state which induces stresses before the actual operation or the working phase. These stresses are further magnified during the cyclic loading which can lead to breakage of these flexes. Due to intricacies involved in FCBs, numerical modeling of these components is challenging. In this work, a methodology is developed in Ansys Mechanical™ to model the installation and operating phase of the FCB. Stresses generated in both the phases are calculated and fatigue life is computed after the operational phase. Two different models are analyzed. The first model is a Rigid Flex PCB, where a FCB connects with the rigid PCBs. The second model is a standalone FCB cable. For both the models, shell elements are used to mesh the FCBs, which are typically thin structures and experience a large amount of rotation and bending loads. Trace mapping feature is used to accurately model the large number of intricate features such as copper traces, vias and other Electronic-CAD data. The trace mapping feature simplifies the model by modeling the geometry as dielectric layers and includes the effect of traces by mapping the metal fraction onto the dielectric layers. The loop forming capability of both the models is analyzed where they are subjected to a 180° bend. The fatigue induced due to this bending load is calculated for both the models. For the FCB cable case, the work is extended to study the stresses developed in the installation phase as it impacts the overall fatigue life of the FCB. Here the rigid surface bodies are used to push/deform the FCB cable to its final installation stage. Lastly, a detailed High-Performance Computing (HPC) scalability study is performed in-order to find the best balance between the number of cores and solution time.