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We present WearMoCap, an open-source library to track the human pose from smartwatch sensor data and leveraging pose predictions for ubiquitous robot control. WearMoCap operates in three modes: 1) a Watch Only mode, which uses a smartwatch only, 2) a novel Upper Arm mode, which utilizes the smartphone strapped onto the upper arm and 3) a Pocket mode, which determines body orientation from a smartphone in any pocket. We evaluate all modes on large-scale datasets consisting of recordings from up to 8 human subjects using a range of consumer-grade devices. Further, we discuss real-robot applications of underlying works and evaluate WearMoCap in handover and teleoperation tasks, resulting in performances that are within 2 cm of the accuracy of the gold-standard motion capture system. Our Upper Arm mode provides the most accurate wrist position estimates with a Root Mean Squared prediction error of 6.79 cm. To evaluate WearMoCap in more scenarios and investigate strategies to mitigate sensor drift, we publish the WearMoCap system with thorough documentation as open source. The system is designed to foster future research in smartwatch-based motion capture for robotics applications where ubiquity matters. www.github.com/wearable-motion-capture. [ABSTRACT FROM AUTHOR]

In the medical field, there are several very different movement disorders, such as tremors, Parkinson's disease, or Huntington's disease. A wide range of motor and non-motor symptoms characterizes them. It is evident that in the modern era, the use of smart wrist devices, such as smartwatches, wristbands, and smart bracelets is spreading among all categories of people. This diffusion is justified by the limited costs, ease of use, and less invasiveness (and consequently greater acceptability) than other types of sensors used for health status monitoring. This systematic review aims to synthesize research studies using smart wrist devices for a specific class of movement disorders. Following PRISMA-S guidelines, 130 studies were selected and analyzed. For each selected study, information is provided relating to the smartwatch/wristband/bracelet model used (whether it is commercial or not), the number of end-users involved in the experimentation stage, and finally the characteristics of the benchmark dataset possibly used for testing. Moreover, some articles also reported the type of raw data extracted from the smart wrist device, the implemented designed algorithmic pipeline, and the data classification methodology. It turned out that most of the studies have been published in the last ten years, showing a growing interest in the scientific community. The selected articles mainly investigate the relationship between smart wrist devices and Parkinson's disease. Epilepsy and seizure detection are also research topics of interest, while there are few papers analyzing gait disorders, Huntington's Disease, ataxia, or Tourette Syndrome. However, the results of this review highlight the difficulties still present in the use of the smartwatch/wristband/bracelet for the identified categories of movement disorders, despite the advantages these technologies could bring in the dissemination of low-cost solutions usable directly within living environments and without the need for caregivers or medical personnel. [ABSTRACT FROM AUTHOR]

This paper proposes a machine learning approach to detect threats using short-term PPG (photoplethysmogram) signals from a commercial smartwatch. In supervised learning, having accurately annotated training data is essential. However, a key challenge in the threat detection problem is the uncertainty regarding how accurately data labeled as 'threat' reflect actual threat responses since participants may react differently to the same experiments. In this paper, Gaussian Mixture Models are learned to remove ambiguously labeled training, and those models are also used to remove ambiguous test data. For the realistic test scenario, PPG measurements are collected from participants playing a horror VR (Virtual Reality) game, and the proposed method validates the superiority of our proposed approach in comparison with other methods. Also, the proposed filtering with GMM improves prediction accuracy by 23% compared to the method that does not incorporate the filtering. [ABSTRACT FROM AUTHOR]

Objectives: Atrial fibrillation (AF) is a common early arrhythmia after heart valve surgery that limits physical activity. We aimed to evaluate the criterion validity of the Apple Watch Series 5 single-lead electrocardiogram (ECG) for detecting AF in patients after heart valve surgery. Design: We enrolled 105 patients from the University Hospital of North Norway, of whom 93 completed the study. All patients underwent single-lead ECG using the smartwatch three times or more daily on the second to third or third to fourth postoperative day. These results were compared with continuous 2–4 days ECG telemetry monitoring and a 12-lead ECG on the third postoperative day. Results: On comparing the Apple Watch ECGs with the ECG monitoring, the sensitivity and specificity to detect AF were 91% (75, 100) and 96% (91, 99), respectively. The accuracy was 95% (91, 99). On comparing Apple Watch ECG with a 12-lead ECG, the sensitivity was 71% (62, 100) and the specificity was 92% (92, 100). Conclusion: The Apple smartwatch single-lead ECG has high sensitivity and specificity, and might be a useful tool for detecting AF in patients after heart valve surgery. [ABSTRACT FROM AUTHOR]

Background: Smartwatches can use sensors to collect and send data to medical teams and family members through the platform of the Internet of things (IoT). The data are first analysed on the platform and the final results are used by the medical team. Aims: This paper reviews and categorises studies conducted in the field of the Internet of things based on smartwatches. Methods: The covered papers have been published over 13 years from 2010 to 2022. The search yielded 227 papers out of which 43 papers were reviewed after screening. The search keywords were “wearables, internet of things, smartwatches, smart bracelet, healthcare, and disease”. The search covered databases including PubMed, ScienceDirect, and IEEE. Results: Smartwatches are used in three fields of healthcare, including palliative care, speech therapy, diagnosis, disease prevention, rehabilitation, and health improvement. Conclusion: Smartwatches are not free of drawbacks and have not received the attention they deserve in the healthcare field. Given the potential of smartwatches, they can be useful in the health sector. Keywords: Disease, IOT, Smartwatch, Wearable

The article offers a framework called WatchLogger for keystroke detection and recognition of typed words using a smartwatch's sensors, addressing potential privacy risks associated with wearable devices. Topics include the use of audio and accelerometer signals for detecting typed words, the development of an ensemble classification model to improve word recognition accuracy, and the creation of the WTW-100 dataset for evaluating performance.

This paper proposes an outlier-robust pedestrian walking direction estimation method. The outliers caused by the unexpected behavior of pedestrians, e.g., wiping sweat, are detected by exploiting the eigenvalue characteristics of the principal components obtained by principal component analysis (PCA) on the distribution of the acceleration measurements. Once the outliers are detected, we solve a robust PCA problem with a newly defined stable principal component pursuit (SPCP) in the inertial sensor measurement domain to recover a low-rank matrix from the acceleration measurement matrix. Eventually, from this outlier-removed low-rank matrix, we estimate the correct walking direction. The performance of the proposed method was evaluated through experiments on several behaviors defined as unexpected behavior of pedestrians. In the outlier-inducing scenario, the proposed method using robust PCA via SPCP outperformed the existing methods by about 70% with a mean error of 4.83°. Furthermore, in the extended scenario, the robust PCA via SPCP outperformed the existing methods by 70–78% with a mean error of 3.50°, improving the robustness of the PCA-based walking direction estimation method. [ABSTRACT FROM AUTHOR]

Background: Obesity in childhood and adolescence represents a major public health problem, mostly attributed to dietary and physical activity factors. We aimed to determine the dietary and physical activity habits of participants before and after the implementation of a personalized, multidisciplinary, lifestyle intervention program for the management of obesity in the context of the Horizon Research Project 'BigO: Big Data against Childhood Obesity'. Methods: Three hundred and eighty-six (n = 386) children and adolescents (mean age ± SD: 12.495 ± 1.988 years, 199 males and 187 females) participated in the study prospectively. Based on body mass index (BMI), subjects were classified as having obesity (n = 293, 75.9%) and overweight (n = 93, 24.1%) according to the International Obesity Task Force (IOTF) cut-off points. We implemented a personalized, multidisciplinary, lifestyle intervention program providing guidance on diet, sleep, and exercise, and utilized the BigO technology platform to objectively record data collected via a Smartphone and Smartwatch for each patient. Results: Following the intervention, a statistically significant decrease was noted in the consumption of cheese, cereal with added sugar, savory snacks, pasta, and fried potatoes across both BMI categories. Also, there was an increase in daily water intake between meals among all participants (p = 0.001) and a reduction in the consumption of evening snack or dinner while watching television (p < 0.05). Boys showed a decrease in the consumption of savory snacks, fried potato products, and pasta (p < 0.05), an increase in the consumption of sugar-free breakfast cereal (p < 0.05), and drank more water between meals daily (p < 0.001). Conclusions: Our findings suggest that a personalized, multidisciplinary, lifestyle intervention improves the dietary habits of children and adolescents. [ABSTRACT FROM AUTHOR]

This work examines swipe-based interactions on smart devices, like smartphones and smartwatches, that detect vibration signals through defined swipe surfaces. We investigate how these devices, held in users' hands or worn on their wrists, process vibration signals from swipe interactions and ambient noise using a support vector machine (SVM). The work details the signal processing workflow involving filters, sliding windows, feature vectors, SVM kernels, and ambient noise management. It includes how we separate the vibration signal from a potential swipe surface and ambient noise. We explore both software and human factors influencing the signals: the former includes the computational techniques mentioned, while the latter encompasses swipe orientation, contact, and movement. Our findings show that the SVM classifies swipe surface signals with an accuracy of 69.61% when both devices are used, 97.59% with only the smartphone, and 99.79% with only the smartwatch. However, the classification accuracy drops to about 50% in field user studies simulating real-world conditions such as phone calls, typing, walking, and other undirected movements throughout the day. The decline in performance under these conditions suggests challenges in ambient noise discrimination, which this work discusses, along with potential strategies for improvement in future research. [ABSTRACT FROM AUTHOR]

ObjectiveThis study examined the accuracy of smartwatches in predicting running performance.MethodsA total of 154 amateur runners (123 males and 31 females) were recruited. After wearing the HUAWEI WATCH GT Runner for a minimum of six weeks, the runners' actual completion times for 5 km, 10 km, and half marathon distances were measured, resulting in 288 test instances. The predicted completion times for the same distances displayed on the watch on the test day were recorded simultaneously.ResultsThe actual and predicted performances for the 5, 10, and 21.1 km distances were highly correlated, with r ≥ 0.95 (p

We present WearMoCap, an open-source library to track the human pose from smartwatch sensor data and leveraging pose predictions for ubiquitous robot control. WearMoCap operates in three modes: 1) a Watch Only mode, which uses a smartwatch only, 2) a novel Upper Arm mode, which utilizes the smartphone strapped onto the upper arm and 3) a Pocket mode, which determines body orientation from a smartphone in any pocket. We evaluate all modes on large-scale datasets consisting of recordings from up to 8 human subjects using a range of consumer-grade devices. Further, we discuss real-robot applications of underlying works and evaluate WearMoCap in handover and teleoperation tasks, resulting in performances that are within 2 cm of the accuracy of the gold-standard motion capture system. Our Upper Arm mode provides the most accurate wrist position estimates with a Root Mean Squared prediction error of 6.79 cm. To evaluate WearMoCap in more scenarios and investigate strategies to mitigate sensor drift, we publish the WearMoCap system with thorough documentation as open source. The system is designed to foster future research in smartwatch-based motion capture for robotics applications where ubiquity matters. www.github.com/wearable-motion-capture.

Advances in wearable technologies now allow modern smartwatches to collect body composition estimates through bioelectrical impedance techniques embedded within their design. However, this technique is susceptible to increased measurement error when postural changes alter body fluid distribution. The purpose of this study was to evaluate the effects of postural orientation on body composition and total body water (TBW) estimates produced by smartwatch bioelectrical impedance analysis (SWBIA) and determine its agreement with criterion measures. For this cross-sectional evaluation, 117 (age: 21.4±3.0 y; BMI: 25.3±5.7 kg/m2) participants (F:69, M:48) completed SWBIA measurements while in the seated, standing, and supine positions, then underwent criterion dual-energy X-ray absorptiometry (DXA) and bioelectrical impedance spectroscopy (BIS) assessments. In the combined sample and females, body fat percent, fat mass, and fat-free mass using SWBIA were significantly different between the supine and standing positions (all p

Self-direction skill (SDS) is an essential 21st-century skill that can help learners be independent and organized in their quest for knowledge acquisition. While some studies considered learners from higher education levels as the target audience, providing opportunities to start the SDS practice by K12 learners is still rare. Further, practicing such skills requires a concrete context and scaffolding during the skill acquisition. This article introduces the Goal Oriented Active Learner (GOAL) system that facilitates SDS acquisition in learners utilizing daily activities as context. The GOAL architecture integrates learning logs from online environments and physical activity logs from wearable trackers to provide a data-rich environment for the learners to acquire and practice their SDS. The GOAL users follow DAPER, a five-phase process model, to utilize the affordances in the system while practicing SDS. We implemented the GOAL system at a K12 public institution in Japan in 2019. Learners used the online environments for extensive reading and smartwatches for tracking walking and sleeping activities. This study analyzes detailed interaction patterns in GOAL while learners planned and monitored their self-directed actions. The results illustrate the strategies for DAPER behaviors that emerge in different activity contexts. We discuss the potentials and challenges of this technology ecosystem that connects learners’ learning logs and physical activity logs, specifically in the K12 context in Japan and, more generally, from the learning analytics research perspective to provide a context to practice SDS.

This study introduces a fall detection system utilizing an affordable consumer smartwatch and smartphone with edge computing capabilities for implementing AI algorithms. Due to the widespread use of these devices, the system as a whole is extremely accepted, easy to use, requires no tuning of any kind, and guarantees extended functioning for a long period. From a technical standpoint, falls are identified using AI techniques to analyze 3D raw data acquired by the smartwatch's built-in accelerometer. However, existing AI models for fall detection are often trained on simulated falls involving young people, which may not accurately represent the falls of elderly in unhealthy conditions, such as arthritis or Parkinson's disease, leading to limitations in detecting falls in this population. Additionally, variations in hardware features among different smartwatches can result in inconsistencies in accelerometer data measurements across X, Y, and Z orientations, further complicating accurate fall detection. To address the challenge of limited and device-specific datasets and to enhance model generalization across various devices, a Deep Transfer Learning approach is proposed. This method proves effective when data are poor. Specifically, the Continuous Wavelet Transform (CWT) is applied to raw accelerometer signals to convert them into 2D images, enabling the use of deep architectures for Transfer Learning. By employing CWT on 5 s time windowed raw accelerometer signals, heat maps (scalograms) are generated. Real-time accelerations sampled at 50 Hz are collected using a smartwatch application, transmitted via Bluetooth to a smartphone app, and converted into scalograms. These serve as input for pre-trained Deep Learning models to estimate fall probabilities. Preliminary tests on the Wrist Early Daily Activity and Fall Dataset (WEDA-FALL) show promising results with an accuracy of approximately 98%, underscoring the efficacy of utilizing wrist-worn wearable devices for processing raw accelerometer data. [ABSTRACT FROM AUTHOR]

Wearable devices are playing an important role in our daily lives. Nowadays, wearable devices have transformative implications for health, technology, connectivity, humancomputer interaction, and data analytics. Their importance lies in their ability to enhance various aspects of life and contribute to the ongoing evolution of digital landscapes. At the heart of smartwatch design, the processor takes center stage, driving the majority of advancements in smartwatch technology. This paper presents an experimental comparative study of ARM 64-bit processors, analyzing their performance and impact on power consumption, CPU usage, and battery temperature. We evaluate the characteristics of four smartwatch processors: Snapdragon W5+, Snapdragon Wear4100, Exynos W920, and Exynos W930. All of those smartwatches are equipped with ARM 64-bit processors. Our results indicate that none of the four selected smartwatches excelled in all aspects; each exhibits superiority over the others in specific features while being surpassed by others in different attributes. [ABSTRACT FROM AUTHOR]

Self-direction skill (SDS) is an essential 21st-century skill that can help learners be independent and organized in their quest for knowledge acquisition. While some studies considered learners from higher education levels as the target audience, providing opportunities to start the SDS practice by K12 learners is still rare. Further, practicing such skills requires a concrete context and scaffolding during the skill acquisition. This article introduces the Goal Oriented Active Learner (GOAL) system that facilitates SDS acquisition in learners utilizing daily activities as context. The GOAL architecture integrates learning logs from online environments and physical activity logs from wearable trackers to provide a data-rich environment for the learners to acquire and practice their SDS. The GOAL users follow DAPER, a five-phase process model, to utilize the affordances in the system while practicing SDS. We implemented the GOAL system at a K12 public institution in Japan in 2019. Learners used the online environments for extensive reading and smartwatches for tracking walking and sleeping activities. This study analyzes detailed interaction patterns in GOAL while learners planned and monitored their self-directed actions. The results illustrate the strategies for DAPER behaviors that emerge in different activity contexts. We discuss the potentials and challenges of this technology ecosystem that connects learners' learning logs and physical activity logs, specifically in the K12 context in Japan and, more generally, from the learning analytics research perspective to provide a context to practice SDS. [ABSTRACT FROM AUTHOR]

Monitoring activities of daily living (ADLs) plays an important role in measuring and responding to a person's ability to manage their basic physical needs. Effective recognition systems for monitoring ADLs must successfully recognize naturalistic activities that also realistically occur at infrequent intervals. However, existing systems primarily focus on either recognizing more separable, controlled activity types or are trained on balanced datasets where activities occur more frequently. In our work, we investigate the challenges associated with applying machine learning to an imbalanced dataset collected from a fully in-the-wild environment. This analysis shows that the combination of preprocessing techniques to increase recall and postprocessing techniques to increase precision can result in more desirable models for tasks such as ADL monitoring. In a user-independent evaluation using in-the-wild data, these techniques resulted in a model that achieved an event-based F1-score of over 0.9 for brushing teeth, combing hair, walking, and washing hands. This work tackles fundamental challenges in machine learning that will need to be addressed in order for these systems to be deployed and reliably work in the real world. [ABSTRACT FROM AUTHOR]

In recent years, there has been a growing interest in developing portable and personal devices for measuring air quality and surrounding pollutants, partly due to the need for ventilation in the aftermath of COVID-19 situation. Moreover, the monitoring of hazardous chemical agents is a focus for ensuring compliance with safety standards and is an indispensable component in safeguarding human welfare. Air quality measurement is conducted by public institutions with high precision but costly equipment, which requires constant calibration and maintenance by highly qualified personnel for its proper operation. Such devices, used as reference stations, have a low spatial resolution since, due to their high cost, they are usually located in a few fixed places in the city or region to be studied. However, they also have a low temporal resolution, providing few samples per hour. To overcome these drawbacks and to provide people with personalized and up-to-date air quality information, a personal device (smartwatch) based on MEMS gas sensors has been developed. The methodology followed to validate the performance of the prototype was as follows: firstly, the detection capability was tested by measuring carbon dioxide and methane at different concentrations, resulting in low detection limits; secondly, several experiments were performed to test the discrimination capability against gases such as toluene, xylene, and ethylbenzene. principal component analysis of the data showed good separation and discrimination between the gases measured. [ABSTRACT FROM AUTHOR]

Background: Elucidating the association between heart rate variability (HRV) metrics obtained through non-invasive methods and mental health symptoms could provide an accessible approach to mental health monitoring. This study explores the correlation between HRV, estimated using photoplethysmography (PPG) signals, and self-reported symptoms of depression and anxiety. Methods: A 4-week longitudinal study was conducted among 47 participants. Time–domain and frequency–domain HRV metrics were derived from PPG signals collected via smartwatches. Mental health symptoms were evaluated using the Patient Health Questionnaire-9 (PHQ-9) and Generalized Anxiety Disorder-7 (GAD-7) at baseline, week 2, and week 4. Results: Among the investigated HRV metrics, RMSSD, SDNN, SDSD, LF, and the LF/HF ratio were significantly associated with the PHQ-9 score, although the number of significant correlations was relatively small. Furthermore, only SDNN, SDSD and LF showed significant correlations with the GAD-7 score. All HRV metrics showed negative correlations with self-reported clinical symptoms. Conclusions: Our findings indicate the potential of PPG-derived HRV metrics in monitoring mental health, thereby providing a foundation for further research. Notably, parasympathetically biased HRV metrics showed weaker correlations with depression and anxiety scores. Future studies should validate these findings in clinically diagnosed patients. [ABSTRACT FROM AUTHOR]

Purpose: Patients undergoing radiation therapy (RT) often experience psychological anxiety that manifests as muscle contraction. Our study explored psychological anxiety in these patients by using biological signals recorded using a smartwatch. Materials and Methods: Informed consent was obtained from participating patients prior to the initiation of RT. The patients wore a smartwatch from the waiting room until the conclusion of the treatment. The smartwatch acquired data related to heart rate features (average, minimum, and maximum) and stress score features (average, minimum, and maximum). On the first day of treatment, we analyzed the participants' heart rates and stress scores before and during the treatment. The acquired data were categorized according to sex and age. For patients with more than three days of data, we observed trends in heart rate during treatment relative to heart rate before treatment (HRtb) over the course of treatment. Statistical analyses were performed using the Wilcoxon signedrank test and paired t-test. Results: Twenty-nine individuals participated in the study, of which 17 had more than 3 days of data. During treatment, all patients exhibited elevated heart rates and stress scores, particularly those in the younger groups. The HRtb levels decreased as treatment progresses. Conclusion: Patients undergoing RT experience notable psychological anxiety, which tends to diminish as the treatment progresses. Early stage interventions are crucial to alleviate patient anxiety during RT. [ABSTRACT FROM AUTHOR]

Introduction & Purpose Lactate threshold (LT) is a critical performance measure traditionally obtained using costly laboratory-based tests. Wearables offer a practical and noninvasive alternative for LT assessment in recreational and professional athletes. However, the comparability of these estimates with the gold standards requires further evaluation. This study therefore aimed to compare pace and heart rate (HR) at the LT between the Garmin Fenix 7® threshold running test and a standardized blood lactate field test. Methods In our sample of 26 participants (nf = 7 and nm = 19; 25.97 (± 6.26) years, BMI: 24.58 (± 2.8) kg/m2) we determined running pace and HR at LT with two subsequent tests. First, all participants were equipped with a Fenix 7® smartwatch for a calibration phase of 5 weeks. Subsequently, all performed the Fenix 7® threshold running test, which guides the athlete through incrementing HR zones. Based on that, the watch estimated pace and HR at LT. After a break of at least 48 h, they were tested in a standardized, graded blood lactate field test analyzed by the modified D-Max method (Cheng et al, 1992). Results Pace at LT calculated by Fenix 7® (M =11.87 km/h ± 1.26 km/h) was 11,8% lower compared to the field test (M =13.28 km/h ± 1.72 km/h), which was significant (p < .001, d = -1.19). HR estimated by the watch at LT was 1,72% lower (p > .05). LT data obtained in the field test showed greater overall variance. Conclusion Our results suggest sufficient accuracy of Fenix 7® LT estimates for recreational athletes. It can be assumed that for professional athletes, it would fail to provide the nuanced data needed for high-quality training management. References Cheng, B., Kuipers, H., Snyder, A., Keizer, H., Jeukendrup, A., & Hesselink, M. (1992). A new approach for the determination of ventilatory and lactate thresholds. International Journal of Sports Medicine, 13(7), 518–522. https://doi.org/10.1055/s-2007-1021309

AimsThe integration of smartwatches into postoperative cardiac care transforms patient monitoring, systematically tracking vital signs and delivering real-time data to a centralized platform. This study focuses on developing a platform for seamless integration, assessing reliability, and evaluating the impact on post-cardiac surgery. The goal is to establish a robust foundation for understanding the efficacy and dependability of smartwatch-based telemonitoring, enhancing care for this population.Methods and resultsA total of 108 cardiac surgery patients were divided into telemonitoring (TLM) and control (CTL) groups. The TLM group utilized smartwatches for continuous monitoring of vital parameters (SpO2, HR, BP, ECG) over 30 ± 3 days. Statistical analyses (Pearson, Intraclass Correlation, Bland-Altman, Tost Test) were employed to compare smartwatch measurements with traditional methods. Significant correlations and concordance were observed, particularly in HR and BP measurements. Challenges were noted in SpO2 measurement. The ECG algorithm exhibited substantial agreement with cardiologists (Kappa: 0.794; p > 0.001), highlighting its reliability. The telemonitoring platform played a crucial role in early detection of clinical changes, including prompt Emergency Department (ED) visits, contributing significantly to preventing outcomes that could lead to mortality, such as asymptomatic Atrioventricular block. Positive patient responses affirmed technological efficacy, especially in identifying cardiac arrhythmias like atrial fibrillation.ConclusionThe integration of smartwatches into remote telemonitoring for postoperative cardiac care demonstrates substantial potential, improving monitoring and early complication detection, thereby enhancing patient outcomes. The FAPO-X Study (Assisted Digital Telemonitoring with Wearables in Patients After Cardiovascular Surgery; NCT05966857) underscores the promising role of telemonitoring in postoperative cardiac care.

INTRODUCTION: In-person dermatology clinical research studies often face recruitment and participation challenges due to travel-, time-, and cost-associated barriers. Studies incorporating virtual/asynchronous formats can potentially enhance research subject participation and satisfaction, but few mobile health tools are available to enable remote study conduct. We developed SkinTracker, a patient-facing mobile app and researcher-facing web platform, that enables longitudinal collection of skin photos, patient reported outcomes, and biometric health and environmental data. METHODS: Eight design thinking sessions including dermatologists, clinical research staff, software engineers, and graphic designers were held to create the components of SkinTracker. Following iterative prototyping, SkinTracker was piloted across six adult and four pediatric subjects with atopic dermatitis (AD) of varying severity levels to test and provide feedback on SkinTracker for six months. RESULTS: The SkinTracker app enables collection of informed consent for study participation, baseline medical history, standardized skin photographs, patient-reported outcomes (e.g., Patient Oriented Eczema Measure (POEM), Pruritus Numerical Rating Scale (NRS), Dermatology Life Quality Index (DLQI)), medication use, adverse events, voice diary to document qualitative experiences, chat function for communication with research team, environmental and biometric data such as exercise and sleep metrics through integration with an Apple Watch. The researcher web portal allows for management and visualization of subject enrollment, skin photographs for examination and severity scoring, survey completion, and other patient modules. The pilot study requested that subjects complete surveys and photographs on a weekly to monthly basis via the SkinTracker app. Afterwards, participants rated their experience in a 7-item user experience survey covering app function, design, and desire for participation in future studies using SkinTracker. Almost all subjects agreed or strongly agreed that SkinTracker enabled more convenient participation in skin research studies compared to an in-person format. DISCUSSION: To our knowledge, SkinTracker is one of the first integrated app- and web-based platforms allowing collection and management of data commonly obtained in clinical research studies. SkinTracker enables detailed, frequent capture of data that may better reflect the fluctuating course of conditions such as AD, and can be modularly customized for different skin conditions to improve dermatologic research participation and patient access.

Introduction: Healthcare professionals frequently experience work-related fatigue, which may jeopardize their health and put patient safety at risk. In this study, we applied a machine learning (ML) approach based on data collected from a smartwatch to construct prediction models of work-related fatigue for emergency clinicians.Methods: We conducted this prospective study at the emergency department (ED) of a tertiary teaching hospital from March 10–June 20, 2021, where we recruited physicians, nurses, and nurse practitioners. All participants wore a commercially available smartwatch capable of measuring various physiological data during the experiment. Participants completed the Multidimensional Fatigue Inventory (MFI) web form before and after each of their work shifts. Wecalculated and labeled the before-and-after-shift score differences between each pair of scores. Using several tree-based algorithms, we constructed the prediction models based on features collected from the smartwatch. Records were split into training/validation and testing sets at a 70∶30 ratio, and we evaluated the performances using the area under the curve (AUC) measure of receiver operating characteristic on the test set.Results: In total, 110 participants were included in this study, contributing to a set of 1,542 effective records. Of these records, 85 (5.5%) were labeled as having work-related fatigue when setting the MFI difference between two standard deviations as the threshold. The mean age of the participants was 29.6. Most of the records were collected from nurses (87.7%) and females (77.5%). We selected a union of 31 features to construct the models. For total participants, CatBoost classifier achieved the best performances of AUC (0.838, 95% confidence interval [CI] 0.742–0.918) to identify work-related fatigue. By focusing on a subgroup of nurses

Marie Heiber,1,&ast; Andrea Schittenhelm,1,&ast; Jennifer Schlie,2,&ast; Marcus Beckert,2 Pascal Graf,2 Annette Schmidt1– 3 1dtec.bw, NextGenerationEU Project Smart Health Lab, University of the Bundeswehr, Chair of Sport Biology Munich, Munich, Germany; 2University of the Bundeswehr, Institute of Sport Sciences, Chair of Sport Biology, Munich, Germany; 3Research Center Smart Digital Health, University of the Bundeswehr Munich, Munich, Germany&ast;These authors contributed equally to this workCorrespondence: Annette Schmidt, Chair of Sport Biology, University of the Bundeswehr Munich, Werner-Heisenberg-Weg 39, Neubiberg, Munich, Bavaria, 85577, Germany, Tel +49 89 6004 4412, Email annette.schmidt@unibw.dePurpose: Lactate threshold (LT) is a critical performance measure traditionally obtained using costly laboratory-based tests. Wearables offer a practical and noninvasive alternative for LT assessment in recreational and professional athletes. However, the comparability of these estimates with the regular field tests requires further evaluation.Patients and Methods: In our sample of 26 participants (nf=7 and nm=19), we compared the estimated running pace and heart rate (HR) at LT with two subsequent tests. First, participants performed the Fenix 7® threshold running test after a calibration phase. Subsequently, they were tested in a standardized, graded blood lactate field test. Age was 25.97 (± 6.26) years, and body mass index (BMI) was 24.58 (± 2.8) kg/m2.Results: Pace at LT calculated by Fenix 7® (M=11.87 km/h ± 1.26 km/h) was 11.96% lower compared to the field test (M=13.28 km/h ± 1.72 km/h), which was significant (p < 0.001, d=− 1.19). HR estimated by the Fenix 7® at LT was 1.71% lower (p > 0.05). LT data obtained in the field test showed greater overall variance.Conclusion: Our results suggest sufficient accuracy of Fenix 7® LT estimates for recreational athletes. It can be assumed that for professional athletes, it would fail to provide the nuanced data needed for high-quality training management.Keywords: smartwatch, physical performance, physiology, heart rate, pace

Abstract To develop and validate a machine learning based algorithm to estimate physical activity (PA) intensity using the smartwatch with the capacity to record PA and determine outdoor state. Two groups of participants, including 24 adults (13 males) and 18 children (9 boys), completed a sequential activity trial. During each trial, participants wore a smartwatch, and energy expenditure was measured using indirect calorimetry as gold standard. The support vector machine algorithm and the least squares regression model were applied for the metabolic equivalent (MET) estimation using raw data derived from the smartwatch. Exercise intensity was categorized based on MET values into sedentary activity (SED), light activity (LPA), moderate activity (MPA), and vigorous activity (VPA). The classification accuracy was evaluated using area under the ROC curve (AUC). The METs estimation accuracy were assessed via the mean absolute error (MAE), the correlation coefficient, Bland–Altman plots, and intraclass correlation (ICC). A total of 24 adults aged 21–34 years and 18 children aged 9–13 years participated in the study, yielding 1790 and 1246 data points for adults and children respectively for model building and validation. For adults, the AUC for classifying SED, MVPA, and VPA were 0.96, 0.88, and 0.86, respectively. The MAE between true METs and estimated METs was 0.75 METs. The correlation coefficient and ICC were 0.87 (p

Abstract Background Among people with COPD, smartphone and wearable technology may provide an effective method to improve care at home by supporting, encouraging, and sustaining self-management. The current study was conducted to determine if patients with COPD will use a dedicated smartphone and smartwatch app to help manage their COPD and to determine the effects on their self-management. Methods We developed a COPD self-management application for smartphones and smartwatches. Participants were provided with the app on a smartphone and a smartwatch, as well as a cellular data plan and followed for 6 months. We measured usage of the different smartphone app functions. For the primary outcome, we examined the change in self-management from baseline to the end of follow up. Secondary outcomes include changes in self-efficacy, quality of life, and COPD disease control. Results Thirty-four patients were enrolled and followed. Mean age was 69.8 years, and half of the participants were women. The most used functions were recording steps through the smartwatch, entering a daily symptom questionnaire, checking oxygen saturation, and performing breathing exercises. There was no significant difference in the primary outcome of change in self-management after use of the app or in overall total scores of health-related quality of life, disease control or self-efficacy. Conclusion We found older patients with COPD would engage with a COPD smartphone and smartwatch application, but this did not result in improved self-management. More research is needed to determine if a smartphone and smartwatch application can improve self-management in people with COPD. Trial registration ClinicalTrials.Gov NCT03857061, First Posted February 27, 2019.

Assessing mobility in daily life can provide significant insights into several clinical conditions, such as Chronic Obstructive Pulmonary Disease (COPD). In this paper, we present a comprehensive analysis of wearable devices' performance in gait speed estimation and explore optimal device combinations for everyday use. Using data collected from smartphones, smartwatches, and smart shoes, we evaluated the individual capabilities of each device and explored their synergistic effects when combined, thereby accommodating the preferences and possibilities of individuals for wearing different types of devices. Our study involved 20 healthy subjects performing a modified Six-Minute Walking Test (6MWT) under various conditions. The results revealed only little performance differences among devices, with the combination of smartwatches and smart shoes exhibiting superior estimation accuracy. Particularly, smartwatches captured additional health-related information and demonstrated enhanced accuracy when paired with other devices. Surprisingly, wearing all devices concurrently did not yield optimal results, suggesting a potential redundancy in feature extraction. Feature importance analysis highlighted key variables contributing to gait speed estimation, providing valuable insights for model refinement. [ABSTRACT FROM AUTHOR]

Smartwatch health sensor data are increasingly utilized in smart health applications and patient monitoring, including stress detection. However, such medical data often comprise sensitive personal information and are resource-intensive to acquire for research purposes. In response to this challenge, we introduce the privacy-aware synthetization of multi-sensor smartwatch health readings related to moments of stress, employing Generative Adversarial Networks (GANs) and Differential Privacy (DP) safeguards. Our method not only protects patient information but also enhances data availability for research. To ensure its usefulness, we test synthetic data from multiple GANs and employ different data enhancement strategies on an actual stress detection task. Our GAN-based augmentation methods demonstrate significant improvements in model performance, with private DP training scenarios observing an 11.90–15.48% increase in F1-score, while non-private training scenarios still see a 0.45% boost. These results underline the potential of differentially private synthetic data in optimizing utility–privacy trade-offs, especially with the limited availability of real training samples. Through rigorous quality assessments, we confirm the integrity and plausibility of our synthetic data, which, however, are significantly impacted when increasing privacy requirements. [ABSTRACT FROM AUTHOR]

Smartwatches represent one of the most widely adopted technological innovations among wearable devices. Their evolution has equipped them with an increasing array of features, including the capability to record an electrocardiogram. This functionality allows users to detect potential arrhythmias, enabling prompt intervention or monitoring of existing arrhythmias, such as atrial fibrillation. In our research, we aimed to compile case reports, case series, and cohort studies from the Web of Science, PubMed, Scopus, and Embase databases published until 1 August 2023. The search employed keywords such as "Smart Watch", "Apple Watch", "Samsung Gear", "Samsung Galaxy Watch", "Google Pixel Watch", "Fitbit", "Huawei Watch", "Withings", "Garmin", "Atrial Fibrillation", "Supraventricular Tachycardia", "Cardiac Arrhythmia", "Ventricular Tachycardia", "Atrioventricular Nodal Reentrant Tachycardia", "Atrioventricular Reentrant Tachycardia", "Heart Block", "Atrial Flutter", "Ectopic Atrial Tachycardia", and "Bradyarrhythmia." We obtained a total of 758 results, from which we selected 57 articles, including 33 case reports and case series, as well as 24 cohort studies. Most of the scientific works focused on atrial fibrillation, which is often detected using Apple Watches. Nevertheless, we also included articles investigating arrhythmias with the potential for circulatory collapse without immediate intervention. This systematic literature review provides a comprehensive overview of the current state of research on arrhythmia detection using smartwatches. Through further research, it may be possible to develop a care protocol that integrates arrhythmias recorded by smartwatches, allowing for timely access to appropriate medical care for patients. Additionally, continuous monitoring of existing arrhythmias using smartwatches could facilitate the assessment of the effectiveness of prescribed therapies. [ABSTRACT FROM AUTHOR]

Purpose: Lactate threshold (LT) is a critical performance measure traditionally obtained using costly laboratory-based tests. Wearables offer a practical and noninvasive alternative for LT assessment in recreational and professional athletes. However, the comparability of these estimates with the regular field tests requires further evaluation. Patients and Methods: In our sample of 26 participants (nf=7 and nm=19), we compared the estimated running pace and heart rate (HR) at LT with two subsequent tests. First, participants performed the Fenix 7® threshold running test after a calibration phase. Subsequently, they were tested in a standardized, graded blood lactate field test. Age was 25.97 (± 6.26) years, and body mass index (BMI) was 24.58 (± 2.8) kg/m2. Results: Pace at LT calculated by Fenix 7® (M=11.87 km/h ±  1.26 km/h) was 11.96% lower compared to the field test (M=13.28 km/h ±  1.72 km/h), which was significant (p < 0.001, d=− 1.19). HR estimated by the Fenix 7® at LT was 1.71% lower (p > 0.05). LT data obtained in the field test showed greater overall variance. Conclusion: Our results suggest sufficient accuracy of Fenix 7® LT estimates for recreational athletes. It can be assumed that for professional athletes, it would fail to provide the nuanced data needed for high-quality training management. [ABSTRACT FROM AUTHOR]

This article examines technical use of Fitbit during an intervention for pulmonary hypertension (PAH)‐patients. Technical issues with the device led to data being unavailable(37.5%). During intervention objective daily physical activity (DPA) decreased and subjective DPA increased. This emphasizes that an assessment of DPA in PAH requires incorporating both objective and subjective measurements. [ABSTRACT FROM AUTHOR]

Variations in Global Positioning Systems (GPSs) have been used for tracking users' locations. However, when location tracking is needed for an indoor space, such as a house or building, then an alternative means of precise position tracking may be required because GPS signals can be severely attenuated or completely blocked. In our approach to indoor positioning, we developed an indoor localization system that minimizes the amount of effort and cost needed by the end user to put the system to use. This indoor localization system detects the user's room-level location within a house or indoor space in which the system has been installed. We combine the use of Bluetooth Low Energy beacons and a smartwatch Bluetooth scanner to determine which room the user is located in. Our system has been developed specifically to create a low-complexity localization system using the Nearest Neighbor algorithm and a moving average filter to improve results. We evaluated our system across a household under two different operating conditions: first, using three rooms in the house, and then using five rooms. The system was able to achieve an overall accuracy of 85.9% when testing in three rooms and 92.106% across five rooms. Accuracy also varied by region, with most of the regions performing above 96% accuracy, and most false-positive incidents occurring within transitory areas between regions. By reducing the amount of processing used by our approach, the end-user is able to use other applications and services on the smartwatch concurrently. [ABSTRACT FROM AUTHOR]

Background Among people with COPD, smartphone and wearable technology may provide an efective method to improve care at home by supporting, encouraging, and sustaining self-management. The current study was conducted to determine if patients with COPD will use a dedicated smartphone and smartwatch app to help manage their COPD and to determine the efects on their self-management. Methods We developed a COPD self-management application for smartphones and smartwatches. Participants were provided with the app on a smartphone and a smartwatch, as well as a cellular data plan and followed for 6 months. We measured usage of the diferent smartphone app functions. For the primary outcome, we examined the change in self-management from baseline to the end of follow up. Secondary outcomes include changes in self-efcacy, quality of life, and COPD disease control. Results Thirty-four patients were enrolled and followed. Mean age was 69.8 years, and half of the participants were women. The most used functions were recording steps through the smartwatch, entering a daily symptom questionnaire, checking oxygen saturation, and performing breathing exercises. There was no signifcant diference in the primary outcome of change in self-management after use of the app or in overall total scores of health-related quality of life, disease control or self-efcacy. Conclusion We found older patients with COPD would engage with a COPD smartphone and smartwatch application, but this did not result in improved self-management. More research is needed to determine if a smartphone and smartwatch application can improve self-management in people with COPD. [ABSTRACT FROM AUTHOR]

A primary goal of neuroscience is to understand the relationship between the brain and behavior. While magnetic resonance imaging (MRI) examines brain structure and function under controlled conditions, digital phenotyping via portable automatic devices (PAD) quantifies behavior in real‐world settings. Combining these two technologies may bridge the gap between brain imaging, physiology, and real‐time behavior, enhancing the generalizability of laboratory and clinical findings. However, the use of MRI and data from PADs outside the MRI scanner remains underexplored. Herein, we present a Preferred Reporting Items for Systematic Reviews and Meta‐Analysis systematic literature review that identifies and analyzes the current state of research on the integration of brain MRI and PADs. PubMed and Scopus were automatically searched using keywords covering various MRI techniques and PADs. Abstracts were screened to only include articles that collected MRI brain data and PAD data outside the laboratory environment. Full‐text screening was then conducted to ensure included articles combined quantitative data from MRI with data from PADs, yielding 94 selected papers for a total of N = 14,778 subjects. Results were reported as cross‐frequency tables between brain imaging and behavior sampling methods and patterns were identified through network analysis. Furthermore, brain maps reported in the studies were synthesized according to the measurement modalities that were used. Results demonstrate the feasibility of integrating MRI and PADs across various study designs, patient and control populations, and age groups. The majority of published literature combines functional, T1‐weighted, and diffusion weighted MRI with physical activity sensors, ecological momentary assessment via PADs, and sleep. The literature further highlights specific brain regions frequently correlated with distinct MRI‐PAD combinations. These combinations enable in‐depth studies on how physiology, brain function and behavior influence each other. Our review highlights the potential for constructing brain–behavior models that extend beyond the scanner and into real‐world contexts. [ABSTRACT FROM AUTHOR]

Occupant feedback enables building managers to improve occupants' health, comfort, and satisfaction. However, acquiring continuous occupant feedback and integrating this feedback with other building information is challenging. This paper presents a scalable method to acquire continuous occupant feedback and directly integrate this with other building information. Semantic web technologies were applied to solve data interoperability issues. The Occupant Feedback Ontology was developed to describe feedback semantically. Next to this, a smartwatch app – Mintal – was developed to acquire continuous feedback on indoor environmental quality. The app gathers location, medical information, and answers on short micro surveys. Mintal applied the Occupant Feedback Ontology to directly integrate the feedback with linked building data. A case study was performed to evaluate this method. A semantic digital twin was created by integrating linked building data, sensor data, and occupant feedback. Results from SPARQL queries gave more insight into an occupant's perceived comfort levels in the Open Flat. The case study shows how integrating feedback with building information allows for more occupant-centric decision support tools. The approach presented in this paper can be used in a wide range of use cases, both within and without the architecture, building, and construction domain. [ABSTRACT FROM AUTHOR]

IntroductionThe effect of displayed step count in smartwatches on the accuracy of daily step-count estimation and the potential underlying psychological factors have not been revealed. The study aimed for the following: (i) To investigate whether the counting and reporting of daily steps by a smartwatch increases the daily step-count estimation accuracy and (ii) to elucidating underlying psychological factors.MethodsA total of 34 healthy men and women participants wore smartwatches for 4 weeks. In week 1 (baseline), 3 (follow-up 1), and 8 (follow-up 2), the number of smartwatch displayed steps was blinded for each participant. In week 2 (Intervention), the number of steps was not blinded. During baseline and follow-ups 1 and 2, the participants were instructed to estimate their number of steps four times per day. During the 4-week wash-out period between follow-ups 1 and 2, no feedback was provided. The Body Awareness Questionnaire and the Body Responsiveness Questionnaire (BRQ) were used to elucidate the psychological facets of the assumed estimation accuracy.ResultsThe mean absolute percentage error between the participants’ steps count estimations and measured steps counts were 29.49% (at baseline), 0.54% (intervention), 11.89% (follow-up 1), and 15.14% (follow-up 2), respectively. There was a significant effect between baseline and follow-up 1 [t (61.7) = 3.433, p

Guangxin Zhou,1,2 Wei Zhao,2 Yi Zhang,2 Wenli Zhou,2 Haizhou Yan,2 Yongli Wei,1 Yuming Tang,1 Zijing Zeng,2 Hanrong Cheng1 1Department of Sleep Medicine, Institute of Respiratory Diseases, Shenzhen People’s Hospital, The Second Clinical Medical College of Jinan University, The First Affiliated Hospital of Southern University of Science and Technology, Shenzhen, Guangdong, People’s Republic of China; 2OPPO Health, Guangdong OPPO Mobile Telecommunications Co. Ltd., Shenzhen, Guangdong, People’s Republic of ChinaCorrespondence: Hanrong Cheng, Department of Sleep Medicine, Shenzhen People’s Hospital, The Second Clinical Medical College of Jinan University, The First Affiliated Hospital of Southern University of Science and Technology, Shenzhen, Guangdong, People’s Republic of China, Email cheng.hanrong@szhospital.comObjective: To evaluate the clinical performance of the OPPO Watch (OW) Sleep Analyzer (OWSA) on OSA screening with polysomnography reference.Methods: We recruited 350 participants using OWSA and PSG simultaneously in a sleep laboratory. The respiratory event index (REI) derived from OWSA and the apnea-hypopnea index (AHI) provided by PSG were compared. SHapley Additive exPlanation (SHAP) values were calculated to explain the model of OWSA.Results: The OWSA-REI (26.5± 18.5 events/h) correlated well with PSG-AHI (33.2± 25.7 events/h; r = 0.91, p < 0.001), with an intraclass correlation coefficient (ICC) of 0.83. Using a threshold of AHI ≥ 15 events/h, the sensitivity, specificity, accuracy, and area under the curve (AUC) were 86.1%, 86.7%, 86.3%, and 0.94, respectively. Bland-Altman analysis showed that OWSA-REI and PSG-AHI were in good agreement (Mean Difference: − 6.7, 95% CI:16.0 to − 29.3 events/h). In addition, the effectiveness of the models in OWSA were also explained by visualizing SHAP values.Conclusion: The OWSA demonstrated a reasonable performance for OSA screening in the clinical setting. In light of this, it is possible for smartwatches to become a complementary tool to PSG, which is particularly useful for larger-scale preliminary screenings.Plain Language Summary: OPPO Watch Sleep Analyzer (OWSA), an emerging sleep-tracking method based on a wearable device, uses a machine learning model to analyze physiological signals including snoring recordings, and other basic anthropometric information to estimate REI. The study evaluated the OSA screening performance of OWSA with PSG-AHI and interpreted the results of the pre-trained machine learning model.OWSA demonstrated consistent clinical diagnostic performance for OSA. The interpretive machine learning models used in OWSA highlighted the impact of multi-modal data on estimation results. Therefore, these types of models are likely to be more widely accepted and promoted in clinical practice.Keywords: obstructive sleep apnea, photoplethysmography, polysomnography, smartwatch

The dynamic behavior of pedestrians causes a misalignment problem between the sensor orientation and the walking direction, which hinders the performance of pedestrian dead reckoning (PDR) systems. Pedestrians wearing smartwatches are constantly faced with this problem when running. In this paper, we propose a novel kinematic modeling of arm swing that segments arm swing motion from the movement of the center of mass of the body. The proposed decomposition method allows for effective negation of the sensor outputs due to the redundant motion that obstructs the estimation of the true walking direction. The correct direction vector is computed by deducting the direction vector of the arm swing from that of the entire motion, which are both derived from performing two separate principal component analyses (PCA). The performance of the proposed method was evaluated through several experiments. In the running track experiment, the proposed method demonstrates the best performance, with 57% – 70% performance improvement compared to the existing methods. In the general scenario involving both walking and running, the proposed method outperforms the baseline method by 56%, improving the generality of the PCA-based methods.

The process of visual analytics is composed of the visual data exploration tasks supporting analytical reasoning. When performing analytical tasks with the interactive visual interfaces displayed by the large screen, physical discomforts such as gorilla-arm effect can be easily caused. To enrich the input space for analysts, there has been some researches concerning the cross-device analysis combining mobile devices with the large display. Although the effectiveness of expert-level designs has been demonstrated, little is known of the ordinary users’ preferences for using a mobile device to issue commands, especially the small one like smartwatch. We implement a three-stage study to investigate and validate these preferences. A total of 181 distinctive gestural inputs and 52 interface designs for 21 tasks were collected from analysts. Expert designers selected the best practices from these user-defined interactions. A performance test was subsequently developed to assess the selected interactions in terms of quantitative statistics and subjective ratings. Our work provides empirical support and proposes a set of design guidelines for optimizing watch-based interactions aimed at remote control of visual data on the large display. Through this research, we hope to advance the development of smartwatches as visual analytics tools and provide visual analysts with a better usage experience.

AbstractBackground Early detection of AF is critical for stroke prevention. Several commercially available smartwatches are FDA cleared for AF detection. However, little is known about how patient-physician relationships affect patients’ anxiety, activation, and health-related quality of life when prescribed smartwatch for AF detection.Methods Data were used from the Pulsewatch study (NCT03761394), which randomized adults (>50 years) with no contraindication to anticoagulation and a CHA2DS2-VASc risk score ≥2 to receive a smartwatch-smartphone app dyad for AF monitoring vs. conventional monitoring with an ECG patch (Cardea SoloTM) and monitored participants for up to 45 days. The Perceived Efficacy in Patient-Physician Interactions survey was used to assess patient confidence in physician interaction at baseline with scores ≥45 indicating high perceived efficacy in patient-provider interactions. Generalized Anxiety Disorder-7 Scale, Consumer Health Activation Index, and Short-Form Health Survey were utilized to examine anxiety, patient activation, and physical and mental health status, at baseline, 14, and 44 days, respectively. We used mixed-effects repeated measures linear regression models to assess changes in psychosocial outcomes among smartwatch users in relation to self-reported efficacy in physician interaction over the study period.Results A total of 93 participants (average age 64.1 ± 8.9 years; 43.0% female; 88.2% non-Hispanic white) were included in this analysis. At baseline, fifty-six (60%) participants reported high perceived efficacy in patient-physician interaction. In the fully adjusted models, high perceived efficacy (vs. low) at baseline was associated with greater patient activation and perceived mental health (β 12.0, p-value

Pap smear is a digital image generated from the recording of cervical cancer cell preparation. Images generated are susceptible to errors due to relatively small cell sizes and overlapping cell nuclei. Therefore, an accurate analysis of the Pap smear image is essential to obtain the right information. This research compares nucleus segmentation and detection using gray-level cooccurrence matrix (GLCM) features in two methods: Otsu and polynomial. The data tested consisted of 400 images sourced from RepoMedUNM, a publicly accessible repository containing 2,346 images. Both methods were compared and evaluated to obtain the most accurate characteristics. The research results showed that the average distance of the Otsu method was 6.6457, which was superior to the polynomial method with a value of 6.6215. Distance refers to the distance between the nucleus detected by the Otsu and the Polynomial method. Distance is an important measure to assess how closely the detection results align with the actual nucleus positions. It indicates that the polynomial method produces nucleus detections that are on average closer to the actual nucleus positions compared to the Otsu method. Consequently, this research can serve as a reference for future studies in developing new methods to enhance identification accuracy.

This work examines swipe-based interactions on smart devices, like smartphones and smartwatches, that detect vibration signals through defined swipe surfaces. We investigate how these devices, held in users’ hands or worn on their wrists, process vibration signals from swipe interactions and ambient noise using a support vector machine (SVM). The work details the signal processing workflow involving filters, sliding windows, feature vectors, SVM kernels, and ambient noise management. It includes how we separate the vibration signal from a potential swipe surface and ambient noise. We explore both software and human factors influencing the signals: the former includes the computational techniques mentioned, while the latter encompasses swipe orientation, contact, and movement. Our findings show that the SVM classifies swipe surface signals with an accuracy of 69.61% when both devices are used, 97.59% with only the smartphone, and 99.79% with only the smartwatch. However, the classification accuracy drops to about 50% in field user studies simulating real-world conditions such as phone calls, typing, walking, and other undirected movements throughout the day. The decline in performance under these conditions suggests challenges in ambient noise discrimination, which this work discusses, along with potential strategies for improvement in future research.

Heart related disease is one of deadly non communicable diseases. In order to detect heart related problems, heart rates must be monitored. Monitoring heart rate can be done with several methods, from stethoscope, to sensors such as PPG and ECG sensors. However, those monitoring can be impractical as it requires doctor visitations. In order to simplify the process, smartwatch is used. Those devices are usually equipped with heart rate monitoring sensors, and many others. However, some previously researched smartwatch-based systems used in monitoring health are cost prohibitive as some requires their own server, high price of smartwatches used, and cumbersome to use. The goal of this research is to build cost effective heart rate monitoring using cheap smartwatches. A proposed system using smartwatch and Android device is used alongside with Whatsapp messaging, with ringtone that plays when emergency situation happened. The message contains GPS location and emergency condition. The resulting system does not require custom made server and nets an acceptable GPS coordinate accuracy outdoors, but not so accurate indoors. It also does not require expensive smartwatches. However, the system requires the device to not be locked due to limitations on Android devices.

Hypertension has been a silent disease that has affected a large part of the world population; in 2022, 5.5 million cases were registered in Peru. Current treatments show an inadequate control of this disease. Therefore, a framework is proposed to build an application for remote monitoring of hypertensive patients using technologies such as smartwatches and artificial intelligence of GPT, considering the diagnostic methodologies of hypertension used in the world, physiological variables and the implementation of GPT-4 as an assistant for the correct treatment of hypertension. The following methodology was followed: selection of measurement techniques, selection of physiological variables, selection of the smartwatch model, implementation of GPT-4 and construction of a mobile application. The experimentation had two scenarios: (a) use of the traditional model and (b) using the proposed method. The results of the experimentation showed that the time to measure and record blood pressure and heart rate (TMR) was 44.44% faster with the app. The medical diagnosis time (MDT) was 80% more efficient than the traditional method. In addition, in the expert judgment evaluation, patients and cardiologists rated the solution with 4.2 and 4.1 points respectively, valuing it as "agree" in use of the proposed solution.