Smartwatch Application Research Articles

A Smartwatch-Based Framework for Real-Time and Online Assessment and Mobility Monitoring

Pervasive computing is changing the monitoring landscape for patients to communicate their healthcare information in real-time to clinicians and researchers. We developed a framework based on a smartwatch application allowing researchers to execute a study that is customized to their needs. The application is configured to collect patient generated data in remote settings from both sensor-based (location and movement) and user-reported health factors through the visual display. For example, data are used to investigate concurrent symptoms and mobility patterns in free-living conditions. To support the collection and analysis of this data in a robust and scalable fashion, we have developed an event driven, serverless computing platform using Amazon cloud services. This system also allows multiple campaigns to run concurrently each under the auspices of a different researcher. The framework is ideal for harnessing and scaling the utilization of smart wearable devices in research and clinical settings.

Novel Low-Cost Integrated Multiband Antenna Design Customized for Smartwatch Applications with SAR Evaluation

This paper presents a novel low-cost integrated multiband antenna design customized for smartwatch applications and wearable devices. The design consists in using a broadband planar patch antenna with circular microstrip lines and a miniaturized feeding-point with a structure of 30 × 30 × 1.6 mm3, and it is easy to deploy inside the smartwatch and cost-effective for the wearable device industry. The parametric study and final dimensions of the design and the measured results of the reflection and radiation pattern are discussed. The antenna with maximum gain up to 6.6 dBi and S11 up to −22 dB exhibits excellent performance for all the frequencies required in wearable systems such as 1.9 GHz, 2.3 GHz, 2.4 GHz, 2.6 GHz, 5.2 GHz, and 5.8 GHz. We drew a comparison between similar research and this work in terms of antenna performance. Furthermore, we investigate the specific absorption rate (SAR) performance of the antenna for the smartwatch application, using both human hand wrist multilayer and SAM head mouth models. The SAR results in different positions for all the frequencies are compared to the Federal Communication Commission (FCC) standards.

1194 An Open-Label Study of Treating Traumatic Nightmares With an Investigational Smartwatch Based System

Abstract Introduction Nightmares are a common problem affecting 2-8% of the general population with the prevalence of comorbid nightmares in those with post-traumatic stress disorder (PTSD) being significantly higher at 72%. The negative sequelae of nightmares are myriad including impaired quality of life, sleep deprivation, insomnia, daytime sleepiness, fatigue, and suicidal ideation. This study investigated a novel approach for the treatment of nightmare disorder in military veterans. Methods All participants in this study were veterans receiving care at the Minneapolis Veterans Affairs Health Care System (VAHCS), diagnosed with PTSD, and had self-reported nightmares. At the baseline assessment, eligible participants were given a digital smartwatch preloaded with an application designed to arouse but not awaken the user out of the nightmare. Paired t-test analysis was used to compare the participants’ baseline and follow-up responses. Results A significant decrease in participant Pittsburgh Sleep Quality Index (PSQI) scores (13.6 to 7.4, p < 0.001), PTSD Checklist for DSM-5 (PCL-5) scores (45.8 to 25.1, p < 0.0025) and Patient Health Questionnaire (PHQ-9) scores (12.2 to 6.1, p < 0.002) was observed over the sixty day trial. All 9 participants experienced a decrease in the Epworth Sleepiness Scale (ESS) upon completion of the trial, with an average decrease of -2.5 ± 1.4. Conclusion These results demonstrate that a novel digital therapeutic smartwatch application is effective in improving sleep quality, reducing the burden associated with PTSD symptoms, and lowering depressive symptoms in those with nightmare disorder within an open label study. These results have initiated further investigation into efficacy of digital therapeutic smartwatch applications in nightmare intervention and improved sleep quality. Support NightWare, Inc.

A Scalable Smartwatch-Based Medication Intake Detection System Using Distributed Machine Learning.

Poor Medication adherence causes significant economic impact resulting in hospital readmission, hospital visits and other healthcare costs. The authors developed a smartwatch application and a cloud based data pipeline for developing a user-friendly medication intake monitoring system that can contribute to improving medication adherence. The developed Android smartwatch application collects activity sensor data using accelerometer and gyroscope. The cloud-based data pipeline includes distributed data storage, distributed database management system and distributed computing frameworks in order to build a machine learning model which identifies activity types using sensor data. With the proposed sensor data extraction, preprocessing and machine learning algorithms, this study successfully achieved a high F1 score of 0.977 with 13.313 seconds of training time and 0.139 seconds for testing.

GOFlow: Smartwatch app to deliver laboratory results in emergency departments – A feasibility study

PurposeInformation Technology (IT) plays a critical role in supporting emergency physicians’ (EPs) routines. Pagers, personal computers, and smartphones offer fast access to patient data, such as laboratory results. However, due to the inherent features of specimen processing and laboratory instruments, the turnaround time from test ordering to availability of results can be long. Lack of follow-up of abnormal results can lead to missed information that could impact patient care and safety. Despite the increasing use of ubiquitous technologies, a third of physicians remains devoid of reliable methods for ensuring that results have been received. In this feasibility study, we report the potential of using a smartwatch to deliver laboratory results to EPs at the point-of-care and to support efficiency in emergency care. Unlike mobile devices that are increasingly used by EPs, smartwatches are always accessible, even during hands-on procedures. MethodTwo EPs and four experts in human-computer interaction designed the smartwatch application following the Design Science Research Methodology (DSRM). The application was then evaluated in a pediatric emergency department through semi-simulated scenarios by eleven EPs. The primary outcome was to measure both the app perceived usability and satisfaction scores by the aim of the System Usability Scale (SUS), and the perceived usefulness and intention of its use by the aim of the Unified Theory of Acceptance and Use of Technology (UTAUT) scale. Secondary outcomes were to assess the application’s efficiency by measuring the delay between the reception of the notification and 1) the access to its details and 2) the visit to the patient. Finally, open questions about the positive and negative aspects of the prototype as well as potential improvements were asked and evaluated qualitatively. ResultsThe prototype obtained a score of 81.4 out of 100 (good) on the SUS and a score of 5.96 out of 7 on the UTAUT scale. EPs using the smartwatch visited patients within 30 seconds receiving the laboratory results. ConclusionsThis study demonstrates the capacity of smartwatches to speed up the point-of-care delivery of laboratory results in the ED.

Large-Scale Assessment of a Smartwatch to Identify Atrial Fibrillation

BackgroundOptical sensors on wearable devices can detect irregular pulses. The ability of a smartwatch application (app) to identify atrial fibrillation during typical use is unknown.MethodsParticipants without atrial fibrillation (as reported by the participants themselves) used a smartphone (Apple iPhone) app to consent to monitoring. If a smartwatch-based irregular pulse notification algorithm identified possible atrial fibrillation, a telemedicine visit was initiated and an electrocardiography (ECG) patch was mailed to the participant, to be worn for up to 7 days. Surveys were administered 90 days after notification of the irregular pulse and at the end of the study. The main objectives were to estimate the proportion of notified participants with atrial fibrillation shown on an ECG patch and the positive predictive value of irregular pulse intervals with a targeted confidence interval width of 0.10.ResultsWe recruited 419,297 participants over 8 months. Over a median of 117 days of monitoring, 2161 participants (0.52%) received notifications of irregular pulse. Among the 450 participants who returned ECG patches containing data that could be analyzed — which had been applied, on average, 13 days after notification — atrial fibrillation was present in 34% (97.5% confidence interval [CI], 29 to 39) overall and in 35% (97.5% CI, 27 to 43) of participants 65 years of age or older. Among participants who were notified of an irregular pulse, the positive predictive value was 0.84 (95% CI, 0.76 to 0.92) for observing atrial fibrillation on the ECG simultaneously with a subsequent irregular pulse notification and 0.71 (97.5% CI, 0.69 to 0.74) for observing atrial fibrillation on the ECG simultaneously with a subsequent irregular tachogram. Of 1376 notified participants who returned a 90-day survey, 57% contacted health care providers outside the study. There were no reports of serious app-related adverse events.ConclusionsThe probability of receiving an irregular pulse notification was low. Among participants who received notification of an irregular pulse, 34% had atrial fibrillation on subsequent ECG patch readings and 84% of notifications were concordant with atrial fibrillation. This siteless (no on-site visits were required for the participants), pragmatic study design provides a foundation for large-scale pragmatic studies in which outcomes or adherence can be reliably assessed with user-owned devices. (Funded by Apple; Apple Heart Study ClinicalTrials.gov number, NCT03335800.)

Development of an Automated 30 Second Chair Stand Test Using Smartwatch Application.

This paper presents development of the smartwatch application for automation of the standard 30 Second Chair Stand Test (30SCST). 30SCST is primarily used to test leg strength and endurance, but also speed and mobility and assess risk of falls. We use inertial signals on smartwatch to detect and count stands during the test. The application notifies the user to start and stop the test using vibration on the smartwatch. Synchronization of notifications and signal acquisition allows assessment of user's response time during the test. Our application monitors baseline heart rate before the test, heart rate increase during the test, and heart rate recovery after the test that might allow assessment of cardiovascular fitness of the user. The application is developed using Wear OS and tested on two smartwatch platforms: Fossil G4 and Polar M600. Pilot test included 12 subjects, six male and six female (mean age 39.1, S.D. 19 years). Overall accuracy of detection of the number of standups is 98.8%. Smartwatch application can be used for automated testing in clinical setups as well as for self-monitoring at home.

A Clinical Applicable Smartwatch Application for Measuring Hyperkinetic Movement Disorder Severity.

Measuring the severity of hyperkinetic movement disorders like tremor and myoclonus is challenging. Although many accelerometers are available to quantify movements, the vast majority lacks real-time analysis and an interface that makes it possible to real-time adjust therapy like deep brain stimulation (DBS). Here, we developed a smartwatch/smartphone application that is capable of real-time analysing movement disorder severity. Movement analysis was realised by integrating acceleration values, to velocity and subsequently to distance. Measured distances were compared with a validated accelerometer already applied for quantifying movement disorders. Further validation was done by quantitative assessment of simulated movement disorders in 10 healthy volunteers. Finally, the approach was tested in two patients treated with DBS to quantify the effect of different DBS settings on myoclonus and tremor severity, respectively. The distance measured with the application had a 96% accuracy. This was non-inferior (p = 0.76) compared to accelerometers already clinically applied. Furthermore, (simulated) movement disorder severity could be classified correctly in 93% of the cases. Finally, the method was capable of distinguishing effective from non-effective DBS parameters in two patients. In summary, with our approach we realised an instantaneous and reliable estimation of the severity of movement disorders which can assist in real time titrating therapy like DBS.

Scaling Crowdsourcing with Mobile Workforce

Traditional urban-scale crowdsourcing approaches suffer from three caveats - lack of complete spatiotemporal coverage, lack of accurate information and lack of sustained engagement of crowd workers. In this paper, we argue that these caveats can be addressed by embedding crowdsourcing tasks into the daily routine of mobile workforces that roam around an urban area. As a use case, we take the bpost who deliver the letters and parcels to the citizens across entire Belgium. We present a study that explores the behavioural attributes of these mobile postal workers both quantitatively (6.3K) and qualitatively (6) to assess the opportunity of leveraging them for crowdsourcing tasks. We report their mobility pattern, workflow, and behavioural traits which collectively inform the design of a purpose-built crowdsourcing solution. In particular, our solution operates on two key techniques - route augmentation, and on-wearable interruptibility management. Together, these mechanisms enhance the spatial coverage, response accuracy and increase workers' engagement with crowdsourcing tasks. We describe these principal components in a wearable smartwatch application supported by a data management infrastructure. Finally, we report a first-of-its-kind real-world trial with ten postal workers for two weeks to assess the quality of road signs at the city centre of Antwerp. Our findings suggest that our solution was effective in achieving 89% spatial coverage and increasing response rate (83.6%) and accuracy (100%) of the crowdsourcing tasks. Although limited in scale, these and the rest of our findings highlight the way of building an efficient and purposeful crowdsourcing solution of the future.

A smartwatch-based framework for real-time and online assessment and mobility monitoring.

Smartphone and smartwatch technology is changing the transmission and monitoring landscape for patients and research participants to communicate their healthcare information in real time. Flexible, bidirectional and real-time control of communication allows development of a rich set of healthcare applications that can provide interactivity with the participant and adapt dynamically to their changing environment. Additionally, smartwatches have a variety of sensors suitable for collecting physical activity and location data. The combination of all these features makes it possible to transmit the collected data to a remote server, and thus, to monitor physical activity and potentially social activity in real time. As smartwatches exhibit high user acceptability and increasing popularity, they are ideal devices for monitoring activities for extended periods of time to investigate the physical activity patterns in free-living condition and their relationship with the seemingly random occurring illnesses, which have remained a challenge in the current literature. Therefore, the purpose of this study was to develop a smartwatch-based framework for real-time and online assessment and mobility monitoring (ROAMM). The proposed ROAMM framework will include a smartwatch application and server. The smartwatch application will be used to collect and preprocess data. The server will be used to store and retrieve data, remote monitor, and for other administrative purposes. With the integration of sensor-based and user-reported data collection, the ROAMM framework allows for data visualization and summary statistics in real-time.

Development of monitoring and hospital patient alert systems using smartwatch application

Health is particularly important family. When a person is sick and hospitalized, the number of family will be limited to just one or two from the patient’s family. The family often responds slowly to the critical situation due to the distance traveled from home to hospital far enough. It would be easier if they able to monitor the condition by using a smart watch. it sends the current condition through the API displayed in the mobile application. The Android mobile app will process any data retrieved from the watch then decide the real condition of the patient. This research could support the Internet of Things in the field of health, may improve the chances of life of patients by providing a quicker and accurate information.

An End To End Ioe-Based Technology Solution for Diabetic Healthcare

In the context of keeping the elderly healthy and safe and with the development of the modern small energy efficient technology, with the help of a health monitoring device (HM) as a main activity. The Cloud-based IoT architecture contains a cloud computing and the Internet of Things In order to focus on the advantages that was derived from the complementarities. The sensing services is focused on the development of the sensors data usage and accessibility. beginning from the general scenarios of this service, this paper discusses a Human Machine-oriented Sensing Service scenario with the goal to make the quality of the service and its implementation, taking In consideration the demanding interests of this service which doesn't come only from big medical facilities but also from general practitioners and small clinics. A mathematical model for simulating induced hyperglycemia and hypoglycemia and a smartwatch application is implemented for the patient as well as the health monitoring company (HMC) and the cloud infrastructure for the data to be stored was designed as well as the infrastructure necessary for the patient to run the solution in his/her home.

BreathEZ: Using smartwatches to improve choking first aid

Choking first aid is an important tool for all individuals as choking is the cause of nearly 5000 deaths per year in the US. Normally for incidents like this, the local emergency medical services (EMS) could be relied up on to respond and provide necessary lifesaving aid. The average EMS response time is almost eight minutes and someone choking only has four to six minutes before permanent brain damage occurs. Cardiopulmonary resuscitation (CPR) trained individuals are often also trained in choking first aid for this very reason, though relying on bystanders presents its own problem. Many individuals assume others will be more qualified to help and therefore will not involve themselves, a psychological phenomenon known as the bystander effect. In order to combat this problem, we present BreathEZ, a smartwatch application that provides both choking first aid instruction and real-time tactile and visual feedback on the quality of the abdominal thrust compressions. To the best of our knowledge, prior to this there has been no feedback device developed that targets choking first aid. BreathEZ uses the onboard accelerometer on an Android smartwatch to identify an abdominal thrust and measure the peak acceleration. It then analyzes the data collected and provides feedback on the quality of the thrust to the user.

Medhere: A Smartwatch-based Medication Adherence Monitoring System using Machine Learning and Distributed Computing.

Poor medication adherence threatens an individual's health and is responsible for substantial medical costs in the United States annually. In order to improve medication adherence rates and provide timely reminders, we developed a smartwatch application that collects data from embedded inertial sensors, which include an accelerometer and gyroscope, to monitor a series of actions happening during an individual's medication intake. After the collected data was delivered to a server, Apache Spark was used to distribute the data and apply machine learning algorithms in order to predict several discrete actions including medication intake. By utilizing these tools, we were able to preprocess high frequency sensor data and apply a random forest algorithm, yielding high frequency and recall of the aforementioned actions.

Consumer Smartwatches for Collecting Self-Report and Sensor Data: App Design and Engagement.

Longitudinal data from patients' natural environments would benefit chronic disease care, yet most devices cannot collect sensor data alongside patient-reported outcomes. Here we describe Koalap, a consumer cellular smartwatch application that collects patient-reported outcomes alongside physical activity data from various sensors. Additionally, we show preliminary results indicating high engagement of our 26 participants with knee osteoarthritis. Our future work will show whether data collection with consumer smartwatches is feasible in terms of user engagement, acceptability, data quality and consistency.

The Study and Design of Collaboration Tools for Flight Attendants

Collaboration is a core component of work activities amongst flight attendants. This is as they work to promote onboard safety and deliver a high level of customer service. Yet we know little of how flight attendants collaborate and how we can best design technology to support this collaboration. Through an interview study with flight attendants, the authors explored their collaborative practices and processes and how technology aided such practices. While technologies like interphones and flight attendant call buttons act as collaboration tools, they identified instances where the usability and functionality of these devices were barriers for maintaining efficient communication, situation awareness, and information exchange. The authors used these results to identify design suggestions for technology that can enhance communication and collaboration in aircraft settings amongst flight attendants. To illustrate these design suggestions, they designed and developed Smart Crew, a smartwatch application that allows flight attendants to maintain an awareness of each other and communicate through messaging with haptic feedback. Smart Crew is designed with an emphasis on real time information access, location updates and direct communication between flight attendants regardless of their location on the airplane.

Smartwatch feedback device for high-quality chest compressions by a single rescuer during infant cardiac arrest: a randomized, controlled simulation study.

ObjectiveAccording to the guidelines, rescuers should provide chest compressions (CC) ~1.5 inches (40 mm) for infants. Feedback devices could help rescuers perform CC with adequate rates (CCR) and depths (CCD). However, there is no CC feedback device for infant cardiopulmonary resuscitation (CPR). We suggest a smartwatch-based CC feedback application for infant CPR.Participants and methodsWe created a smartwatch-based CC feedback application. This application provides feedback on CCD and CCR by colour and text for infant CPR. To evaluate the application, 30 participants were divided randomly into two groups on the basis of whether CC was performed with or without the assistance of the smartwatch application. Both groups performed continuous CC-only CPR for 2 min on an infant mannequin placed on a firm table. We collected CC parameters from the mannequin, including the proportion of correct depth, CCR, CCD and the proportion of correct decompression depth.ResultsDemographics between the two groups were not significantly different. The median (interquartile range) proportion of correct depth was 99 (97–100) with feedback compared with 83 (58–97) without feedback (P = 0.002). The CCR and proportion of correct decompression depth were not significantly different between the two groups (P = 0.482 and 0.089). The CCD of the feedback group was significantly deeper than that of the control group [feedback vs. control: 41.2 (39.8–41.7) mm vs. 38.6 (36.1–39.6) mm; P=0.004].ConclusionRescuers who receive feedback of CC parameters from a smartwatch could perform adequate CC during infant CPR.

Application of Wearable Devices for Supporting Operators in Human-Robot Cooperative Assembly Tasks

This paper presents a tool for supporting operators in shared industrial workplaces where humans and robots coexist. The tool has been developed in the form of software application for wearable devices, such as smartwatches, and provides functionalities for direct interaction with the robot. Interfaces to audio commands, manual guidance applications and AR visualization systems have been implemented. The paper focuses on the development of the smartwatch application and its integration with the network of resources under the ROS framework. The tool has been tested on two pilot cases, one in the automotive and one in the white goods production sectors. The results indicate that the approach can significantly enhance the operators’ integration in the hybrid assembly system.

Toward Using a Smartwatch to Monitor Frailty in a Hospital Setting: Using a Single Wrist-Wearable Sensor to Assess Frailty in Bedbound Inpatients

Background: While various objective tools have been validated for assessing physical frailty in the geriatric population, these are often unsuitable for busy clinics and mobility-impaired patients. Recently, we have developed a frailty meter (FM) using two wearable sensors, which allows capturing key frailty phenotypes (weakness, slowness, and exhaustion), by testing 20-s rapid elbow flexion-extension test. Objective: In this study, we proposed an enhanced automated algorithm to identify frailty using a single wrist-worn sensor. Methods: The data collected from 100 geriatric inpatients (age: 78.9 ± 9.1 years, 49% frail) were reanalyzed to validate the new algorithm. The frailty status of the participants was determined using a validated modified frailty index. Different FM phenotypes (31 features) including velocity of elbow rotation, decline in velocity of elbow rotation over 20 s, range of motion, etc. were extracted. A regression model, bootstrap with 2,000 iterations, and recursive feature elimination technique were used for optimizing the FM parameters and identifying frailty using a single wrist-worn sensor. Results: A strong agreement was observed between two-sensor and wrist-worn sensor configuration (r = 0.87, p < 0.001). Results suggest that the wrist-worn FM with no demographic information still yields a high accuracy of 80.0% (95% CI: 79.7-80.3%) and an area under the curve of 87.7% (95% CI: 87.4-87.9%) to identify frailty status. Results are comparable with two-sensor configuration, where the observed accuracy and area under the curve were 80.6% (95% CI: 80.4-80.9%) and 87.4% (95% CI: 87.1-87.6%), respectively. Conclusion: The simplicity of FM may open new avenues to integrate wearable technology and mobile health to capture frailty status in a busy hospital setting. Furthermore, the reduction of needed sensors to a single wrist-worn sensor allows deployment of the proposed algorithm in the form of a smartwatch application. From the application standpoint, the proposed FM is superior to traditional physical frailty-screening tools in which the walking test is a key frailty phenotype, and thus they cannot be used for bedbound patients or in busy clinics where administration of gait test as a part of routine assessment is impractical.

StretchArms: Promoting Stretching Exercise with a Smartwatch

ABSTRACTA smartwatch is a wearable computer equipped with sensors. It can be used to provide fitness- or healthcare-related functionalities. This article presents three versions of a smartwatch application for prompting a user to stretch during the user’s daily life. The first version just reminds periodically the user that it is time to stretch and then guides the stretching motion. In contrast, the second version evaluates how correctly the stretching motion is made and provides success/fail feedback for the user. The third version adds gamification elements to the second version. With these three versions, an elaborate user study was performed with 42 participants. The results show that the reminders were effective in relaxing the mental/muscular tension of the users and the motion evaluation feedback encouraged the users to stretch more. In contrast, the effectiveness of gamification was not proven. This is the first study to prompt stretching exercise and evaluate it with a smartwatch. It can help develop effective healthcare applications on wearable devices.