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Автор: Grafiati
Опубліковано: 26 липня 2024
Оновлено: 28 липня 2024

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Статті в журналах з теми "Best Smart Watches"

1

Hedayatnia, Mostafa. "Smart Materials for Silent Alarms." Mechanical Engineering 120, no. 02 (February 1, 1998): 88–89. http://dx.doi.org/10.1115/1.1998-feb-7.

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Анотація:
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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2

Chang, Ray-I., Tzu-Chieh Lin, and Jeng-Wei Lin. "A Vehicle Passive Entry Passive Start System with the Intelligent Internet of Things." Electronics 13, no. 13 (June 26, 2024): 2506. http://dx.doi.org/10.3390/electronics13132506.

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Анотація:
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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3

Sharma, Richa, Shalli Rani, and Deepali Gupta. "Stress Detection Using Machine Learning Classifiers in Internet of Things Environment." Journal of Computational and Theoretical Nanoscience 16, no. 10 (October 1, 2019): 4214–19. http://dx.doi.org/10.1166/jctn.2019.8502.

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Анотація:
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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4

C, Vijesh Joe, and Jennifer S. Raj. "Location-based Orientation Context Dependent Recommender System for Users." March 2021 3, no. 1 (April 10, 2021): 14–23. http://dx.doi.org/10.36548/jtcsst.2021.1.002.

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Анотація:
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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5

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 (June 24, 2022): 1–6. http://dx.doi.org/10.1155/2022/2056807.

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Анотація:
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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6

Shukri, Sarah E., Rizik Al-Sayyed, Hamed Al-Bdour, Esraa Alhenawi, Tamara Almarabeh, and Hiba Mohammad. "Internet of Things: Underwater routing based on user’s health status for smart diving." International Journal of Data and Network Science 7, no. 4 (2023): 1715–28. http://dx.doi.org/10.5267/j.ijdns.2023.7.019.

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Анотація:
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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7

Khwaounjoo, Prashanna, Gurleen Singh, Sophie Grenfell, Burak Özsoy, Michael R. MacAskill, Tim J. Anderson, and Yusuf O. Çakmak. "Non-Contact Hand Movement Analysis for Optimal Configuration of Smart Sensors to Capture Parkinson’s Disease Hand Tremor." Sensors 22, no. 12 (June 18, 2022): 4613. http://dx.doi.org/10.3390/s22124613.

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Анотація:
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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8

Alekhya, B. "Design and Development of Wearable Antenna: A Literature review." International Journal for Research in Applied Science and Engineering Technology 10, no. 6 (June 30, 2022): 2124–27. http://dx.doi.org/10.22214/ijraset.2022.44247.

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Анотація:
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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9

Shim, Dongsub, Zheda Mai, Jihwan Jeong, Scott Sanner, Hyunwoo Kim, and Jongseong Jang. "Online Class-Incremental Continual Learning with Adversarial Shapley Value." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 11 (May 18, 2021): 9630–38. http://dx.doi.org/10.1609/aaai.v35i11.17159.

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Анотація:
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.
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10

Johnson, Elizabeth A., and Jane M. Carrington. "Cyber-Situational Awarenss in Clinical Trials Using Wearable Device Technology." Proceedings of the International Symposium on Human Factors and Ergonomics in Health Care 10, no. 1 (June 2021): 33–36. http://dx.doi.org/10.1177/2327857921101011.

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Анотація:
It is estimated 1 in 3 clinical trials utilize a wearable device to gather real-time participant data, including sleep habits, telemetry, and physical activity. While wearable technologies (including smart watches, USBs, and implantable devices) have been revolutionary in their ability to provide a higher precision and accuracy to data acquisition external to the research milieu, there is hesitancy among providers and participants alike given security concerns, perception of cyber-related threats, and meaning attributed to privacy issues. The purpose of this research is to define cyber-situational awareness (CSA) as it pertains to clinical trials, evaluate its current measurement, and describe best practices for research investigators and trial participants to enhance protections in the digital age. This paper reviews integrated elements of CSA within the process of informed consent when wearable devices are implemented for trial procedures. Evaluation of CSA as part of informed consent allows the research site to support the participant in knowledge gaps surrounding the technology while also providing feedback to the trial sponsor as to technology improvements to enhance usability and wearability of the device.
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Книги з теми "Best Smart Watches"

1

Best garmin smart watches. Barcodeliveorg, 2023.

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2

Best Smart Watches with Advanced Features. Barcodeliveorg, 2023.

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3

Best Golf Smart Watches for Value. Barcodeliveorg, 2023.

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4

Best Smart Watches for Extreme Environments. Barcodeliveorg, 2023.

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5

Best Rugged Smart Watch for Outdoor Enthusiasts. Barcodeliveorg, 2023.

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6

Best Smart Watch for Running. Barcodeliveorg, 2023.

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7

Best Smart Watch for Seniors with Health Tracking. Barcodeliveorg, 2023.

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8

Smith, Bryan. Weight Watchers Freestyle Cookbook 2019 : 1000 Day Delicious Meal Plan Using WW Smart Points: The Best Weight Watchers Cookbook for Fast and Healthy Weight Loss. Independently Published, 2019.

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9

Lane, Jennifer. Instant Pot Cookbook: The Complete Weight Watchers Smart Points Guide and over 100 Top Best Pressure Cooker Recipes for Rapid Weight Loss. Independently Published, 2018.

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10

Constable, Simon, and Robert E. Wright. WSJ Guide to the 50 Economic Indicators That Really Matter: From Big Macs to Zombie Banks, the Indicators Smart Investors Watch to Beat the Market. HarperCollins Publishers, 2011.

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Частини книг з теми "Best Smart Watches"

1

Ahmed, Mohamed Ismail, and Mai Fouad Ahmed. "A Dual-Band Flexible Wearable Antenna Integrated on a Smart Watch for 5G Applications." In Advances in Systems Analysis, Software Engineering, and High Performance Computing, 77–101. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-1152-7.ch004.

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Анотація:
This chapter focuses on the design of dual band flexible wearable antennas for modern 5G applications to integrate on a smartwatch. The first is a rectangular antenna which the patch and the ground etched on new flexible material is called ULTRALAM® 3850HT. This antenna is designed to operate at 38 GHz and 60 GHz. The second is a planar inverted-2F wearable antenna pasted on a jeans textile material. Two methods for measuring the dielectric properties of the jeans will be presented. This antenna is designed to operate at 28 GHz and 38 GHz. The SAR (specific absorption ratio) is also introduced and SAR results will be shown. Moreover, the proposed smartwatch under the bent condition will be also studied. These antennas are simulated using HFSS and CST 2018.
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Тези доповідей конференцій з теми "Best Smart Watches"

1

Shejwal, Amogh, Ashutosh Srivastava, Sandeep Medikonda, and 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.
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2

Ma, William, and Yu Sun. "A Wearable and Internet-Of-Things (IoT) Application for Sleep Detection and Lighting Control using AI and Machine Learning Techniques." In 10th International Conference on Foundations of Computer Science & Technology (FCST 2022). Academy and Industry Research Collaboration Center (AIRCC), 2022. http://dx.doi.org/10.5121/csit.2022.120808.

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There are many apps that let you control hardwares with the application of the internet-of-thing today, however, I have seen none that lets you customly control the hardwares, most likely you can only use the few controls the developer of the hardware gives you, and there are very few auto control options. This paper designs an application to auto control the hardwares into desired state based on personal status detected [1]. We use a smart watch to detect the heart beat of the user and determine if they are asleep, and once they are asleep, we turn off a light switch in the user's room to create a total darkness environment. Much research done on sleeping quality shows that sleeping in total darkness gives much better sleeping quality, while many, out of fear or sleeping disorders, still leave little lights on when sleeping [3]. This software helps to give these people better sleeping quality with ease [2].
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