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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.

Wearable devices and fitness trackers have gained popularity in healthcare and telemedicine as tools to reduce hospitalization costs, improve personalized health management, and monitor patients in remote areas. Smartwatches, particularly, offer continuous monitoring capabilities through step counting, heart rate tracking, and activity monitoring. However, despite being recognized as an emerging technology, the adoption of smartwatches in patient monitoring systems is still at an early stage, with limited studies delving beyond their feasibility. Developing healthcare applications for smartwatches faces challenges such as short battery life, wearable comfort, patient compliance, termination of non-native applications, user interaction difficulties, small touch screens, personalized sensor configuration, and connectivity with other devices. This paper presents a case study on designing an Android smartwatch application for remote monitoring of geriatric patients. It highlights obstacles encountered during app development and offers insights into design decisions and implementation details. The aim is to assist programmers in developing more efficient healthcare applications for wearable systems.

The negative effects of mental stress on human health has been known for decades. High-level stress must be detected at early stages to prevent these negative effects. After the emergence of wearable devices that could be part of our lives, researchers have started detecting extreme stress of individuals with them during daily routines. Initial experiments were performed in laboratory environments and recently a number of works took a step outside the laboratory environment to the real-life. We developed an automatic stress detection system using physiological signals obtained from unobtrusive smart wearable devices which can be carried during the daily life routines of individuals. This system has modality-specific artifact removal and feature extraction methods for real-life conditions. We further tested our system in a real-life setting with collected physiological data from 21 participants of an algorithmic programming contest for nine days. This event had lectures, contests as well as free time. By using heart activity, skin conductance and accelerometer signals, we successfully discriminated contest stress, relatively higher cognitive load (lecture) and relaxed time activities by using different machine learning methods.

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.]

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.

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.

This paper presents SmartFall, an Android app that uses accelerometer data collected from a commodity-based smartwatch Internet of Things (IoT) device to detect falls. The smartwatch is paired with a smartphone that runs the SmartFall application, which performs the computation necessary for the prediction of falls in real time without incurring latency in communicating with a cloud server, while also preserving data privacy. We experimented with both traditional (Support Vector Machine and Naive Bayes) and non-traditional (Deep Learning) machine learning algorithms for the creation of fall detection models using three different fall datasets (Smartwatch, Notch, Farseeing). Our results show that a Deep Learning model for fall detection generally outperforms more traditional models across the three datasets. This is attributed to the Deep Learning model’s ability to automatically learn subtle features from the raw accelerometer data that are not available to Naive Bayes and Support Vector Machine, which are restricted to learning from a small set of extracted features manually specified. Furthermore, the Deep Learning model exhibits a better ability to generalize to new users when predicting falls, an important quality of any model that is to be successful in the real world. We also present a three-layer open IoT system architecture used in SmartFall, which can be easily adapted for the collection and analysis of other sensor data modalities (e.g., heart rate, skin temperature, walking patterns) that enables remote monitoring of a subject’s wellbeing.

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.

Smartwatches are widely adopted globally for tracking health metrics, offering potential for enhancing individual health care and public health efforts. Continuous use of the devices and users' willingness to share the data collected are critical to realizing their full benefits. This study aimed to identify key factors that determine continuous smartwatch use and users' comfort levels in sharing health data with health care providers and public health authorities. A cross-sectional online survey of current and past smartwatch users (aged >18 years) was conducted to assess determinants of continuous use based on the Expectation-Confirmation Model (ECM) and user comfort levels with different data-sharing methods. Structural equation modeling was used to evaluate relationships between habit formation, satisfaction, perceived enjoyment, and perceived usefulness with continuous use. Wilcoxon signed-rank tests were used to analyze user comfort in sharing data, comparing noninternet- versus internet-based sharing methods and fully versus partially anonymized data. A total of 273 responses were analyzed, with participants aged 18-65 (mean 35.6, SD 11.7) years. The results indicate that continuous use of smartwatches is explained by habit (β=.35; P<.001) and satisfaction (β=.38; P<.001), which is in turn explained by perceived usefulness (β=.38; P<.001), perceived enjoyment (β=.32; P<.001), confirmation (β=.24; P<.001), and perceived usability (β=.10; P=.03). Smartwatch users preferred noninternet-based sharing options (z=-5.793; P<.001) when sharing data with their physician. Similarly, users were more comfortable sharing fully anonymized data with public health authorities than partially anonymized data (z=-3.592; P<.001). Habit formation and satisfaction emerged as pivotal drivers of continuous intention to use smartwatches, emphasizing the need for features that foster integration into daily routine and a rewarding user experience. Preferences for noninternet-based data sharing with physicians highlight privacy concerns that must be addressed to build users' trust. By aligning device features and data-sharing protocols with user preferences, manufacturers, health care providers, and policy makers can enhance user engagement and maximize the potential of smartwatches to support individual health management and public health initiatives.

Wearable devices are starting to gain popularity, which means that a large portion of the population is starting to acquire these products. This kind of technology comes with a lot of advantages, as it simplifies different tasks people do daily. However, as they recollect sensitive data, they are starting to be targets for cybercriminals. The number of attacks on wearable devices forces manufacturers to improve the security of these devices to protect them. Many vulnerabilities have appeared in communication protocols, specifically Bluetooth. We focus on understanding the Bluetooth protocol and what countermeasures have been applied during their updated versions to solve the most common security problems. We have performed a passive attack on six different smartwatches to discover their vulnerabilities during the pairing process. Furthermore, we have developed a proposal of requirements needed for maximum security of wearable devices, as well as the minimum requirements needed to have a secure pairing process between two devices via Bluetooth.