Auswahl der wissenschaftlichen Literatur zum Thema „Best Rugged Smart Watch“

Abstract Interventions utilizing consumer-grade wearable and mobile devices may support older adult health and wellness. However, rapid technology change and short industry product release cycles limit timely incorporation of these devices. We developed a novel, multi-stage process to rapidly move from within-team evaluations to lab- and field-based participants studies based on small-sample technology testing methods from Human-Computer Interaction. We present findings from a first-stage evaluation of the Amazfit Bips smart watch for potential use in studies with older adults as part of the methodology validation. A four-person research team conducted evaluations using: 1) a wearables framework for user experience and feature availability; and 2) the System Usability Scale (SUS). Evaluators wore the watch seven days straight from the box. User experience checklists indicated high usability. However, corresponding comments identified challenges with downloading the mobile app, pairing the watch and phone, navigating watch and mobile interfaces, and privacy controls. Average SUS score was 65.6 indicating marginal usability (C grade). While meeting study goals, divergence in usability perceptions suggest the process could be improved by completing each set of instruments separately for the watch and mobile app rather than all at once. Given failures in pairing, app navigation challenges, and small screen size, the Amazfit Bips may be best suited for studies among older adults with a high degree of technical proficiency. For those with little technical experience or high disease burden, training materials and dedicated training with support may be required. Future steps are lab- and field-based tests with older adult participants.

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.

The study purpose was to assess the reliability and validity of Polar V800 smart watch in measuring various cricket-specific movements. Materials and methods. Only one trained male volunteer was selected to perform all the cricket specific movements to minimize individual error and eliminate between-subject variability. Polar V800 obtained distances were compared with real field markings. Results. Split-half Reliability Statistical method has been used and 'r' score for the measurements taken has been found to be 0.93. 95% confidence intervals also express a good reliability score. The criterion validity method was used to determine the validity of the dataset. The Pearson correlations (r) have been found ranging from 0.86 to 0.99. Predicted best fit line of linear regression has been found as y = 0.9722 X + 0.0046 (where y = observed value, X = real field distance). One way ANOVA followed by Tukey’s post hoc test on observed 10m sprint, 20m sprint and run-a-three movements show maximum significant difference with other cricket-specific movements. The mean percentage of bias for all cricket-specific movements has been found to be -2.20 ± 13.17. Conclusions. The study reveals that Polar V800 smart watch has an acceptable accuracy, reliability, and validity for measuring various cricket specific movements with certain limitations.

Every day, millions of people logon to the Internet to view their favorite TV show on Netflix, or similar streaming services, or to watch the latest viral video on YouTube. Two things are paramount, 1) that they receive the best streaming quality available, and 2) the video starts to play as quickly as possible. There is nothing worse than a video that stops and starts, takes forever to view or constantly changes between viewable qualities (resolutions). Due to our limited download speeds (bandwidth), in most houses it is not uncommon to hear “Stop downloading, I’m trying to watch something on Netflix”. When we couple this rise in online streaming with the growing number of portable devices (smart phones, tablets, laptops) we see an ever-increasing demand for high-definition online videos while on the move. This demand for mobile streaming highlights the need for adaptive video streaming schemes that can adjust to ...

Pollution is a growing issue these days. It is necessary to analyze environment & keep it in check for a for best future as well as healthy living for all. Here we propose an Envi-ronment Monitoring System that permit us to watch & check live environment in espe-cially areas through Internet of Things (IOT). IoT supported a real time environmental monitoring system. It plays a crucial role in today’s world through a huge and pro-tract-ed system of sensor networks concerned to the environment & its parameters. This technique deals with monitoring important environmental conditions like temperature, humidity & CO level using the sensor & then this data is shipped to the web page. This information is often access from anyplace over the internet & then the sensor in-formation is presented as graphical statistics during mobile application. This paper explains & present the implementation & outcome of this environmental system uses the sensors for temperature, humidity, air quality & different environmental parameters of the surrounding space. This data is often used to take remote actions to regulate the conditions. Information is pushed to the distributed storage & android app get to the cloud & present the effect to the end users. The system employs a Node MCU, DHT-11 sensor, MQl35 sensor, which transmits data to WEBPAGE. An Android application is made which accesses the cloud data and displays results to the end users. The sensors interact with microcontroller which processes this information & transmit it over internet. This system is best method for any use in monitoring the environment and handling it because everything is controlled automatically through all the time of the process. The results say everything about the application of this system across different field where it was controlled precisely and effectively which further explains that this system easily makes our work easier because of this automatic monitoring system worries about other unexpected climate issues for world.

BACKGROUND: Manual interpretation of single-lead ECGs (SL-ECGs) is often required to confirm a diagnosis of atrial fibrillation. However accuracy in detecting atrial fibrillation via SL-ECGs may vary according to clinical expertise and choice of smart device. AIMS: To compare the accuracy of cardiologists, internal medicine residents and medical students in detecting atrial fibrillation via SL-ECGs from five different smart devices (Apple Watch, Fitbit Sense, KardiaMobile, Samsung Galaxy Watch, Withings ScanWatch). Participants were also asked to assess the quality and readability of SL-ECGs. METHODS: In this prospective study (BaselWearableStudy, NCT04809922), electronic invitations to participate in an online survey were sent to physicians at major Swiss hospitals and to medical students at Swiss universities. Participants were asked to classify up to 50 SL-ECGs (from ten patients and five devices) into three categories: sinus rhythm, atrial fibrillation or inconclusive. This classification was compared to the diagnosis via a near-simultaneous 12-lead ECG recording interpreted by two independent cardiologists. In addition, participants were asked their preference of each manufacturer’s SL-ECG. RESULTS: Overall, 450 participants interpreted 10,865 SL-ECGs. Sensitivity and specificity for the detection of atrial fibrillation via SL-ECG were 72% and 92% for cardiologists, 68% and 86% for internal medicine residents, 54% and 65% for medical students in year 4–6 and 44% and 58% for medical students in year 1–3; p <0.001. Participants who stated prior experience in interpreting SL-ECGs demonstrated a sensitivity and specificity of 63% and 81% compared to a sensitivity and specificity of 54% and 67% for participants with no prior experience in interpreting SL-ECGs (p <0.001). Of all participants, 107 interpreted all 50 SL-ECGs. Diagnostic accuracy for the first five interpreted SL-ECGs was 60% (IQR 40–80%) and diagnostic accuracy for the last five interpreted SL-ECGs was 80% (IQR 60–90%); p <0.001. No significant difference in the accuracy of atrial fibrillation detection was seen between the five smart devices; p = 0.33. SL-ECGs from the Apple Watch were considered as having the best quality and readability by 203 (45%) and 226 (50%) participants, respectively. CONCLUSION: SL-ECGs can be challenging to interpret. Accuracy in correctly identifying atrial fibrillation depends on clinical expertise, while the choice of smart device seems to have no impact.

Abstract: Industrial automation has historically been finished with wired structures. Wireless versions swiftly rising in commercial communique because of the extended ease of setting up with wireless structures. In addition to the capacity wired structures are bulky. Industrial automation structures are used for circumstance monitoring, control programs, and the mobile workforce. Common to all commercial automation programs are the necessities for international availability of components, coexistence among wired and wireless technologies, lifetime, security, and interoperability. The wireless generation is hastily rising to fulfill those needs. Industry 4.0 is revolutionizing the manner organizations manufacture, enhance and distribute their products. Manufacturers are integrating new technologies, together with the Internet of Things (IoT), cloud computing and analytics, and AI and machine learning into their manufacturing centres and in the course of their operations. This digital technology results in improved automation, predictive maintenance, self-optimization of techniques enhancements and, above all, a brand-new degree of efficiencies and responsiveness to clients now no longer formerly possible. Using high-tech IoT devices in smart factories ends in better productiveness and progressed best. Replacing manual inspection enterprise models with AIpowered visible insights reduces production mistakes and saves cash and time. With minimum investment, quality control personnel can set up a smart phone linked to the cloud to monitor production processes from simply anywhere. By making use of machine learning algorithms and equipping themselves with the right tools now, such as rugged IT equipment, enterprise resource planning and manufacturing execution systems, manufacturers can create an IT infrastructure that allows them to capitalize on these exciting new technologies at the earliest opportunity.

This paper describes how to use the Internet of Things to control and monitor induction motors. Because it can be utilized from anywhere via Wi-Fi, IoT is more convenient and efficient to control the system. The purpose of this monitoring system is to prevent induction motor failure by implementing preventive measures. Induction motors are used in a variety of applications such as in industry due to their many advantages such as self-starting, high power factor, and rugged design. As a result, using the best available smart protection techniques, it is imperative to detect flaws in the motor at an early stage to improve the efficiency of the motor and ensure safe and reliable operation. The voltage, current and temperature of the induction motor can be monitored remotely with the Blynk IoT application. The monitoring results will determine the error in the motor operating parameters that caused severe damage to the motor. Three PZEM-004T sensors to obtain parameters of each R, S, and T phase of the motor in real-time and relays to control the motor are used in this system to monitor and regulate using IoT. Information from the sensor is received by the microcontroller unit, which processes the sensing data. If an abnormal value is detected, the system automatically generates a control signal to start or stop the motor.

Background An estimated 60%-90% of patients with cancer experience moderate to severe pain. Poorly managed cancer pain negatively affects the quality of life for both patients and their family caregivers and can be a particularly challenging symptom to manage at home. Mobile and wireless technology (“Smart Health”) has significant potential to support patients with cancer and their family caregivers and empower them to safely and effectively manage cancer pain. Objective This study will deploy a package of sensing technologies, known as Behavioral and Environmental Sensing and Intervention for Cancer (BESI-C), and evaluate its feasibility and acceptability among patients with cancer-family caregiver dyads. Our primary aims are to explore the ability of BESI-C to reliably measure and describe variables relevant to cancer pain in the home setting and to better understand the dyadic effect of pain between patients and family caregivers. A secondary objective is to explore how to best share collected data among key stakeholders (patients, caregivers, and health care providers). Methods This descriptive two-year pilot study will include dyads of patients with advanced cancer and their primary family caregivers recruited from an academic medical center outpatient palliative care clinic. Physiological (eg, heart rate, activity) and room-level environmental variables (ambient temperature, humidity, barometric pressure, light, and noise) will be continuously monitored and collected. Behavioral and experiential variables will be actively collected when the caregiver or patient interacts with the custom BESI-C app on their respective smart watch to mark and describe pain events and answer brief, daily ecological momentary assessment surveys. Preliminary analysis will explore the ability of the sensing modalities to infer and detect pain events. Feasibility will be assessed by logistic barriers related to in-home deployment, technical failures related to data capture and fidelity, smart watch wearability issues, and patient recruitment and attrition rates. Acceptability will be measured by dyad perceptions and receptivity to BESI-C through a brief, structured interview and surveys conducted at deployment completion. We will also review summaries of dyad data with participants and health care providers to seek their input regarding data display and content. Results Recruitment began in July 2019 and is in progress. We anticipate the preliminary results to be available by summer 2021. Conclusions BESI-C has significant potential to monitor and predict pain while concurrently enhancing communication, self-efficacy, safety, and quality of life for patients and family caregivers coping with serious illness such as cancer. This exploratory research offers a novel approach to deliver personalized symptom management strategies, improve patient and caregiver outcomes, and reduce disparities in access to pain management and palliative care services. International Registered Report Identifier (IRRID) DERR1-10.2196/16178

OBJECTIVES/GOALS: Research objectives include prototyping a mobile phone application that allows physicians to monitor CD19-directed CAR T-cell therapy patients remotely after discharge. This app will also enable standardized data collection across different centers that provide CAR-T cell therapy and allow for the harmonization of follow-up protocols. METHODS/STUDY POPULATION: Literature review and semi-structured interviews with patients, clinical coordinators, and other experts in the field will be used to determine what parameters must be included in the mobile application prototype to effectively monitor the side effects of CD19-directed CAR T-cell therapy. The mobile phone application will be designed using process mapping to integrate data from self-reporting and wearable technologies, including the Garmin smart watch. Figma will then be used to develop new screens based on an existing patient monitoring app for Allogeneic Stem Cell Transplant follow-up. Finally, a preliminary feasibility study will be conducted to collect feedback on the app prototype from CAR T-cell therapy patients, providers, and stakeholders. RESULTS/ANTICIPATED RESULTS: The anticipated results of this study include an app prototype that will include the functionalities required to monitor patients for adverse effects of CD19-directed CAR T-cell therapy. This will include the parameters that will be recorded or measured using a combination of self-reporting, a reliable body temperature sensor, and the Garmin watch which monitors basic vitals, activity, and sleep. Additional parameters may be added during the stakeholder co-design process. The app prototype will include a physician interface where doctors can monitor their patients and will be alerted if they require further physician assessment. It is expected that the app will provide standardized monitoring of patients when they are discharged from the hospital after receiving CAR T-cell therapy. DISCUSSION/SIGNIFICANCE: This app will allow physicians to monitor patients for general follow-up and adverse effects, including cytokine release syndrome and neurotoxicity. Future studies may utilize this app to develop best practices for harmonizing CAR-T follow-up protocols across Canada.