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IntroductionThe increasing prevalence of arrhythmias, particularly atrial fibrillation (AF), presents a significant public health challenge. Early detection of arrhythmias is essential to mitigate the risks of stroke and other cardiovascular complications. Wearable devices, especially smartwatches, have emerged as effective tools for continuous monitoring of arrhythmias, providing a valuable solution for detecting infrequent events. These devices facilitate early detection by reversing the traditional healthcare model, where medical review precedes diagnostic monitoring. This shift helps bridge the gap between clinical visits and enables real-time data sharing between patients and healthcare providers.MethodsThis review examines the current landscape of smartwatches for arrhythmia detection. It focuses on the technologies utilized in these devices, such as photoplethysmography (PPG) and electrocardiography (ECG), their applications in arrhythmia detection, costs, licences, and clinical effectiveness, as evidenced by various studies and trials. Smartwatches from companies like Apple, Garmin, Samsung, and Huawei, incorporating PPG and ECG technologies, are assessed for their performance in detecting arrhythmias, particularly AF.ResultsSmartwatches equipped with PPG and ECG sensors have shown substantial accuracy in detecting AF, with sensitivity and specificity values exceeding 90%. For example, the Apple heart study showed Apple Watch’s positive predictive value (PPV) of 84% in detecting AF. Other smartwatches, such as the Garmin Venu series has PPV of 90%, Galaxy Watch by Samsung proved PPV of 95%, and Huawei Watch showed PPV of 91.6% in mAFA-II trial. Despite their effectiveness, these devices can lead to false positives and overdiagnosis, which may result in unnecessary anxiety and healthcare visits. While their performance in detecting AF is well-established, smartwatches have yet to be extensively validated for detecting other arrhythmias, limiting their broader clinical application.ConclusionsSmartwatches represent a transformative shift in arrhythmia detection and management, and their diagnostic accuracy for AF is promising. However, challenges such as the risk of overdiagnosis, limited validation for a broader spectrum of arrhythmias, and difficulties with integration into established healthcare systems remain. To fully realize their potential, future advancements should focus on enhancing detection algorithms, expanding the range of detectable arrhythmias, and driving cost reductions. Additionally, the inclusion of measurements for blood pressure, cardiac output, or suitable surrogates would significantly improve the evaluation of arrhythmias and provide critical insights for diagnosing syncope.Abstract 4-001 Table 1 Manufac-turer MODEL* FDA CE Cost** AF Detection Automatic-ally*** Core Study PPG ECG Apple Inc • Series 4,5,6,7,8,9,10• ULTRA 1 & 2 £300 -£800 Apple Heart Study Samsung electronics • Galaxy watch Active 2.• Galaxy Watch 3,4,5,6,7 £250 -£400 - Huawei Technologies Co • GT 3 Pro• Gt 5 pro• 4 pro space Edition £300 - £500 mAFA-II Trial Garmin Ltd • Venu 2 plus• Venu 3 Series• Epix Pro• Fenix 7 Pro £350 - £700 - Abstract 4-001 Figure 1[Image Omitted. See PDF.]

Smart wearable is the one of the fastest growing industries all over the world as per IDC Report 2022. From the same, Smartwatch along with fit bands are some of the fastest accepted commercial wearables. The aim of the study is to observe the model fit of the hypothesized research model. Another aim of the study is to identify the influence of perceived usefulness, perceived ease of use and perceived risk on attitude influencing purchase intention for smartwatch. The study was conducted in Surat, Navsari and Bardoli in South Gujarat region of India. The hypothesized research model was developed based on the model of Technology Acceptance (TAM). Total respondents considered for the study was 300. The data was collected using on probability convenience sampling from the specific cities or town. AMOS was used to analyse data. Structural equation modelling was applied in the study. It was identified that perceived usefulness and perceived risk were having influence on attitude for purchasing the smartwatch while perceived ease of use was not having significant influence on purchase intention for smart watch.

Wearable technology has advanced significantly and is now used in various entertainment and business contexts. Authentication methods could be trustworthy, transparent, and non-intrusive to guarantee that users can engage in online communications without consequences. An authentication system on a security framework starts with a process for identifying the user to ensure that the user is permitted. Establishing and verifying an individual’s appearance usually requires a lot of effort. Recent years have seen an increase in the usage of activity-based user identification systems to identify individuals. Despite this, there has not been much research into how complex hand movements can be used to determine the identity of an individual. This research used a one-dimensional residual network with squeeze-and-excitation (SE) configurations called the 1D-ResNet-SE model to investigate hand movements and user identification. According to the findings, the SE modules have enhanced the one-dimensional residual network’s identification ability. As a deep learning model, the proposed methodology is capable of effectively identifying features from the input smartwatch sensor and could be utilized as an end-to-end model to clarify the modeling process. The 1D-ResNet-SE identification model is superior to the other models. Hand movement assessment based on deep learning is an effective technique to identify smartwatch users.

In the modern world, stress has become a pervasive concern that affects individuals’ physical and mental well-being. To address this issue, many wearable devices have emerged as potential tools for stress detection and management by measuring heart rate, heart rate variability (HRV), and various metrics related to it. This literature review aims to provide a comprehensive analysis of existing research on HRV tracking and biofeedback using smartwatches pairing with reliable 3rd party mobile apps like Elite HRV, Welltory, and HRV4Training specifically designed for stress detection and management. We apply various algorithms and methodologies employed for HRV analysis and stress detection including time-domain, frequency-domain, and non-linear analysis techniques. Prominent smartwatches, such as Apple Watch, Garmin, Fitbit, Polar, and Samsung Galaxy Watch, are evaluated based on their HRV measurement accuracy, data quality, sensor technology, and integration with stress management features. We describe the efficacy of smartwatches in providing real-time stress feedback, personalized stress management interventions, and promoting overall well-being. To assist researchers, doctors, and developers with using smartwatch technology to address stress and promote holistic well-being, we discuss the data’s advantages and limitations, future developments, and the significance of user-centered design and personalized interventions.

Introduction Sleep apnea, a hyper common disorder is estimated to affect almost 1 billion people with the prevalence of more than 50% in some countries. It is almost impossible to do gold standard polysomnography for all the population for screening. Multiple studies have reported the use of cost effective wearable devices using photoplethysmography (PPG) for the screening. To our knowledge, we presented the first meta-analysis of all studies looking at PPG and screening of sleep apnea. Methods We conducted a systematic review and meta-analysis under the PRISMA guidelines. Study Eligibility criteria included patients aged > 18 without a prior diagnosis of sleep disorder. The intervention included screening using a wearable device using PPG with comparison using standard of care sleep study (e.g polysomnography). The databases used were Cochrane, PubMed, Embase and Google scholar. Search strategy with Boolean logic included Sleep apnea, wearable device, smart, photoplethysmography, PPG, smart and smartwatch. Outcomes were pooled diagnostic odds ratio, meta-analyzed area under the curve (AUC),, and forest plots of sensitivity & specificity. Statistical analysis was done using the programming language R with R package mada. Fixed and random effects models were used to derive diagnostic odds ratio (DOR) and paired forest plots for sensitivity & specificity. Summary Receiver operation curve was made using proportional hazard model and Reitsma et al model with a bivariate approach. Results The search strategy revealed following number of studies: PubMed (270), Cochrane (0), Embase (41), and Google Scholar(728). Total of 12 studies were included. We found high DOR with the fixed effect model which was 28.979. Mean DOR with the random effects model was 27.6. Bivariate diagnostic random-effects meta-analyzed AUC was 0.902. Studies used various electronic devices some of which are commercially available such as E4 Wristband, Galaxy watch 4, Smartwatch GT2, Belun ring etc. 4 studies used PPG solely while others used multimodal data. 7 studies used conventional machine learning algorithm for analysis. Conclusion Screening of sleep apnea could be done in a cost-effective manner from commercially available devices using photoplethysmography owing to possible high sensitivity, AUC and DOR. More studies for data replicability and further standardization is needed. Support (if any)

Ubiquitous computing has been a green research area that has managed to attract and sustain the attention of researchers for some time now. As ubiquitous computing applications, human activity recognition and localization have also been popularly worked on. These applications are used in healthcare monitoring, behavior analysis, personal safety, and entertainment. A robust model has been proposed in this article that works over IoT data extracted from smartphone and smartwatch sensors to recognize the activities performed by the user and, in the meantime, classify the location at which the human performed that particular activity. The system starts by denoising the input signal using a second-order Butterworth filter and then uses a hamming window to divide the signal into small data chunks. Multiple stacked windows are generated using three windows per stack, which, in turn, prove helpful in producing more reliable features. The stacked data are then transferred to two parallel feature extraction blocks, i.e., human activity recognition and human localization. The respective features are extracted for both modules that reinforce the system’s accuracy. A recursive feature elimination is applied to the features of both categories independently to select the most informative ones among them. After the feature selection, a genetic algorithm is used to generate ten different generations of each feature vector for data augmentation purposes, which directly impacts the system’s performance. Finally, a deep neural decision forest is trained for classifying the activity and the subject’s location while working on both of these attributes in parallel. For the evaluation and testing of the proposed system, two openly accessible benchmark datasets, the ExtraSensory dataset and the Sussex-Huawei Locomotion dataset, were used. The system outperformed the available state-of-the-art systems by recognizing human activities with an accuracy of 88.25% and classifying the location with an accuracy of 90.63% over the ExtraSensory dataset, while, for the Sussex-Huawei Locomotion dataset, the respective results were 96.00% and 90.50% accurate.

Mobile and wearable devices have enabled numerous applications, including activity tracking, wellness monitoring, and human–computer interaction, that measure and improve our daily lives. Many of these applications are made possible by leveraging the rich collection of low-power sensors found in many mobile and wearable devices to perform human activity recognition (HAR). Recently, deep learning has greatly pushed the boundaries of HAR on mobile and wearable devices. This paper systematically categorizes and summarizes existing work that introduces deep learning methods for wearables-based HAR and provides a comprehensive analysis of the current advancements, developing trends, and major challenges. We also present cutting-edge frontiers and future directions for deep learning-based HAR.

Automatic and continuous blood pressure monitoring is important for preventing cardiovascular diseases such as hypertension. The evaluation of medication effects and the diagnosis of clinical hypertension can both benefit from continuous monitoring. The current generation of wearable blood pressure monitors frequently encounters limitations with inadequate portability, electrical safety, limited accuracy, and precise position alignment. Here, we present an optical fiber sensor-assisted smartwatch for precise continuous blood pressure monitoring. A fiber adapter and a liquid capsule were used in the building of the blood pressure smartwatch based on an optical fiber sensor. The fiber adapter was used to detect the pulse wave signals, and the liquid capsule was used to expand the sensing area as well as the conformability to the body. The sensor holds a sensitivity of -213µw/kPa, a response time of 5 ms, and high reproducibility with 70,000 cycles. With the assistance of pulse wave signal feature extraction and a machine learning algorithm, the smartwatch can continuously and precisely monitor blood pressure. A wearable smartwatch featuring a signal processing chip, a Bluetooth transmission module, and a specially designed cellphone APP was also created for active health management. The performance in comparison with commercial sphygmomanometer reference measurements shows that the systolic pressure and diastolic pressure errors are -0.35 ± 4.68 mmHg and -2.54 ± 4.07 mmHg, respectively. These values are within the acceptable ranges for Grade A according to the British Hypertension Society (BHS) and the Association for the Advancement of Medical Instrumentation (AAMI). The smartwatch assisted with an optical fiber is expected to offer a practical paradigm in digital health.