Siga este enlace para ver otros tipos de publicaciones sobre el tema: Visualisation de la smartwatch.
Artículos de revistas sobre el tema "Visualisation de la smartwatch"
Crea una cita precisa en los estilos APA, MLA, Chicago, Harvard y otros
Consulte los 50 mejores artículos de revistas para su investigación sobre el tema "Visualisation de la smartwatch".
Junto a cada fuente en la lista de referencias hay un botón "Agregar a la bibliografía". Pulsa este botón, y generaremos automáticamente la referencia bibliográfica para la obra elegida en el estilo de cita que necesites: APA, MLA, Harvard, Vancouver, Chicago, etc.
También puede descargar el texto completo de la publicación académica en formato pdf y leer en línea su resumen siempre que esté disponible en los metadatos.
Explore artículos de revistas sobre una amplia variedad de disciplinas y organice su bibliografía correctamente.
1
Khurana, Rushil, Mayank Goel, and Kent Lyons. "Detachable Smartwatch." Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 3, no. 2 (2019): 1–14. http://dx.doi.org/10.1145/3328921.
Texto completo
2
Shin, Donghee, and Frank Biocca. "Impact of Social Influence and Users' Perception of Coolness on Smartwatch Behavior." Social Behavior and Personality: an international journal 46, no. 6 (2018): 881–90. http://dx.doi.org/10.2224/sbp.5134.
Texto completoResumen
We explored the motives behind switching one's smartwatch, focusing on 2 divergent motivations that relate to social influence and the mobile and visible nature of these watches. Our findings and a review of related work indicated that the social motive for switching a smartwatch was coolness, that is, distinguishing oneself from others, because owning the latest model of smartwatch is a way to demonstrate having ample financial resources. The perceived benefit of switching one's smartwatch was associated more with coolness and the identity formation process, and less with actual utility or social influence. Further, the effects of similarity avoidance based on smartwatch purchasing intention were mediated by the effect of identity formation. Our findings may shed light on the nature of smartwatch uses and the motives that drive smartwatch users' choice to upgrade their devices.
3
Yim, Yonghwan, Jaemoon Sim, and Kyungdoh Kim. "Design and Evaluation of Encoded Haptic Pulses for Smartwatches." Mobile Information Systems 2019 (December 28, 2019): 1–15. http://dx.doi.org/10.1155/2019/9628582.
Texto completoResumen
As smartwatches gain popularity in the marketplace, various smartwatch context studies have been conducted. The use of smartwatches can be divided into situations with and without constraint both physically and psychologically. Notably, in constrained situations, if the user wants to check the information received in the smartwatch visually, a high cognitive load is involved. To solve this problem, we propose a method to encode and transmit information from the smartwatch with haptic pulses. First, we determine the informational category of the smartwatch and generate various haptic pulses. Next, we propose and verify a haptic pulse set that can represent the informational category of the smartwatch. Using the proposed haptic pulse set, users can receive smartwatch information in constraint situations. The use of encoded haptic pulses needs to be considered to provide information effectively from the smartwatch to the wearer.
4
Swami, Divya Vishwanath, Sakshi Sachin Thamake, Nandini Sham Ubale, Pallavi Vijay Lokhande, and Dr Kazi Kutubuddin Sayyad Liyakat. "Sending Notification to Someone Missing you Through Smart Watch." International Journal of Information technology and Computer Engineering, no. 25 (September 29, 2022): 19–24. http://dx.doi.org/10.55529/ijitc.25.19.24.
Texto completoResumen
From our smartwatch we get the result will be the person can send the notification and the particular person will be receive the notification. With the information we obtain from our smartwatch, we can determine who will receive and who will send notifications. The major goal of adding that function or application to our smartwatch is to notify someone that we are missing them. We use the smartwatch since we can always take it with us. We have added a new function or application to our smartwatch so that if someone is missing, another person will be notified that they are missing them. In our project, a Bluetooth/Wi-Fi connection is made between a smartphone and a smartwatch. We can use Bluetooth or Wi-Fi to send someone a notification that we are looking for them by using their smartwatch's IP address. The smartwatch will first check our smartphone contact information before we can choose a contact's name from the contact list.
5
Jeon, Seong A., Hansol Chang, Sun Young Yoon, et al. "Effectiveness of Smartwatch Guidance for High-Quality Infant Cardiopulmonary Resuscitation: A Simulation Study." Medicina 57, no. 3 (2021): 193. http://dx.doi.org/10.3390/medicina57030193.
Texto completoResumen
Background and objectives: As in adults, the survival rates and neurological outcomes after infant Cardiopulmonary resuscitation (CPR) are closely related to the quality of resuscitation. This study aimed to demonstrate that using a smartwatch as a haptic feedback device increases the quality of infant CPR performed by medical professionals. Materials and methods: We designed a prospective, randomized, case-crossover simulation study. The participants (n = 36) were randomly allocated to two groups: control first group and smartwatch first group. Each CPR session consisted of 2 min of chest compressions (CCs) using the two-finger technique (TFT), 2 min of rest, and 2 min of CCs using the two-thumb encircling hands technique (TTHT). Results: The primary outcome was the variation in the “proportion of optimal chest compression duration” and “compression rate” between the smartwatch-assisted and non-smartwatch-assisted groups. The secondary outcome was the variation in the “compression depth” between two groups. The proportion of optimal CC duration was significantly higher in the smartwatch-assisted group than in the non-smartwatch-assisted group. The absolute difference from 220 was much smaller in the smartwatch-assisted group (218.02) than in the non-smartwatch-assisted group (226.59) (p-Value = 0.018). Conclusion: This study demonstrated the haptic feedback system using a smartwatch improves the quality of infant CPR by maintaining proper speed and depth regardless of the compression method used.
6
Said, Noor Azzah, Sharidatul Akma Abu Seman, Dilla Syadia Ab Latiff, Siti Noorsuriani Ma’o, and Noorizan Mohamad Mozie. "Consumers’ Behavioral Intention Towards Smartwatch Adoption in Malaysia: A Concept Paper." International Journal of Innovative Computing 11, no. 1 (2021): 13–19. http://dx.doi.org/10.11113/ijic.v11n1.281.
Texto completoResumen
The wide-ranging features of a smartwatch have driven the rapid growth of the smartwatch market as they pique the users’ interests by offering interactive technology that simultaneously promotes fitness and tracks health. Nevertheless, the factors influencing smartwatch adoption among individuals are yet to be comprehended despite the ever-growing popularity of smartwatch usage. Hence, to understand the possible factors in detail, a research model is proposed to indicate the influential underlying factors relative to smartwatch adoption in the Malaysian populace. This study will examine the four proposed dimensions of perceived benefits, healthology, IT innovation, and smartwatch as luxury products. Online questionnaires will be used to collect the research data, while SPSS will be used to run both preliminary research and descriptive analyses, PLS-SEM will be used to further analyze the model.
7
Visuri, Aku, Niels van Berkel, Jorge Goncalves, Reza Rawassizadeh, Denzil Ferreira, and Vassilis Kostakos. "Understanding usage style transformation during long-term smartwatch use." Personal and Ubiquitous Computing 25, no. 3 (2021): 535–49. http://dx.doi.org/10.1007/s00779-020-01511-2.
Texto completoResumen
AbstractDespite large investments in smartwatch development, the market growth remains smaller than forecasted. The purpose of smartwatch use remains unclear, indicated by the lack of large-scale adoption. Thus, we aim to better understand the early adoption and everyday smartwatch use. We investigate a diverse usage data of smartwatches logged over a period of up to 14 months from 79 individuals between December 2015 and March 2017, one of the largest wearable datasets collected. First, we identify both explorative and accepted behaviours that users exhibit and further investigate how the individual usage traits and features differ between the two categories. Our analysis offers an insightful perspective on how smartwatch use evolves organically. Our results improve our shared understanding of smartwatch use and users adapting their use of smartwatch over time to match the capabilities of the technology by validating numerous findings from previous literature.
8
Homayounfar, Morteza, Amirhossein Malekijoo, Aku Visuri, et al. "Understanding Smartwatch Battery Utilization in the Wild." Sensors 20, no. 13 (2020): 3784. http://dx.doi.org/10.3390/s20133784.
Texto completoResumen
Smartwatch battery limitations are one of the biggest hurdles to their acceptability in the consumer market. To our knowledge, despite promising studies analyzing smartwatch battery data, there has been little research that has analyzed the battery usage of a diverse set of smartwatches in a real-world setting. To address this challenge, this paper utilizes a smartwatch dataset collected from 832 real-world users, including different smartwatch brands and geographic locations. First, we employ clustering to identify common patterns of smartwatch battery utilization; second, we introduce a transparent low-parameter convolutional neural network model, which allows us to identify the latent patterns of smartwatch battery utilization. Our model converts the battery consumption rate into a binary classification problem; i.e., low and high consumption. Our model has 85.3% accuracy in predicting high battery discharge events, outperforming other machine learning algorithms that have been used in state-of-the-art research. Besides this, it can be used to extract information from filters of our deep learning model, based on learned filters of the feature extractor, which is impossible for other models. Third, we introduce an indexing method that includes a longitudinal study to quantify smartwatch battery quality changes over time. Our novel findings can assist device manufacturers, vendors and application developers, as well as end-users, to improve smartwatch battery utilization.
9
Sara, Meghshanth. "Stress Detection Smartwatch." International Journal for Research in Applied Science and Engineering Technology 10, no. 7 (2022): 3796–802. http://dx.doi.org/10.22214/ijraset.2022.45865.
Texto completoResumen
Abstract: When a person is unable to handle their circumstances, responsibilities, and workload, stress is a natural emotion that is produced. A person's physical and mental health may suffer when the body is triggered, which can be deadly. The physical impacts of stress on a person's body can include an increase in blood pressure, a rapid heartbeat, increased muscle tension, headaches, a decrease in bodily immunity functions, and a decrease in sleepiness, among other things. The latest technology, known as smartwatches, provides the user with easy access to mobile features. Users can employ the stress-detecting capabilities of high-end smartwatches. Although they can be used to understand things better, these stress applications for smartwatches are not precise in how they operate. Heart rate variability, or HRV, is used by smartwatches and involves the intervals between each heartbeat that the sensor records. A person who has a low HRV is likely under stress. Although stress applications may not be as precise as medical equipment, they are dependable when necessary because there is a good likelihood that the data is accurate. An Electro Dermal Activity (EDA) sensor, found in some smartwatches, monitors tension by electrically altering the amount of sweat on our skin. You must spend two minutes with your palm on the watch dial to achieve the same. As an increase in heart rate is a direct outcome of stress, stress is recognized in the project utilizing heart rate. Since it is also an immediate outcome of stress, heart rate is used in the implementation. In this sector, mobile applications give users a way to explore this data graphically or in greater detail. The user of mobile applications might utilize them for medical purposes and to understand the data
10
Prokudina, A. I., N. N. Kirillov, and A. S. Nikolaev. "Smartwatch Innovation Landscape." Economics. Law. Innovaion, no. 3 (September 2021): 45–52. http://dx.doi.org/10.17586/2713-1874-2021-3-45-52.
Texto completoResumen
The authors analyzed the smartwatch market and identified the prospects for its further innovative development. The purpose of the study was to build an innovative landscape of the smart watch market to determine the investment attractiveness of this industry.The relevance of the research topic is due to the growing number of buyers of devices that combine the functionality of a cell phone in a relatively small size and convenient form of operation. In this regard, the success of smart watches is justified by a combination of these factors. The process of improving the func-tionality launched by large manufacturing companies will continue in the current perspective, and the projected pace of technological updates will only grow.To understand the prospects for the development of the smartwatch market, this study used methods for analyzing patent and non-patent. Information and reviewed the latest patented technologies from two leading manufacturers: Samsung and Apple. The authors also analyzed user queries in the Google search engine using Google Trends Analytics. After analyzing patent and non-patent sources of information, conclusions were drawn, and recommendations were made for the development of the smartwatch market. The technological landscape was chosen as the final form of presentation of the research results.
11
Sikorski, Konrad. "SmartWatch - Inteligentny zegarek." ELEKTRONIKA - KONSTRUKCJE, TECHNOLOGIE, ZASTOSOWANIA 1, no. 3 (2017): 32–34. http://dx.doi.org/10.15199/13.2017.3.7.
Texto completo
12
Röhmig, Conny. "Smartwatch und Telemedizin." Onkologische Welt 14, no. 06 (2023): 272–74. http://dx.doi.org/10.1055/a-2092-6602.
Texto completoResumen
Sie kann Prozesse verändern, Diagnosen erleichtern, Medizin verbessern: Die Digitalisierung ist ein Game-Changer in der Gesundheitsversorgung. Doch neben ersten spannenden Ansätzen gibt es noch immer viele Hürden, wie eine Diskussionsrunde auf dem diesjährigen Kongress der Deutschen Gesellschaft für Pneumologie und Beatmungsmedizin (DGP) zeigte.
13
Li, Mengchi, Miranda McPhillips, Sarah Szanton, Jennifer Wenzel, and Junxin Li. "Smartwatch Technology for Physical Activity in Older Adults: A Qualitative Study." Innovation in Aging 5, Supplement_1 (2021): 430–31. http://dx.doi.org/10.1093/geroni/igab046.1673.
Texto completoResumen
Abstract Older adults’ experiences using smartwatch technology for physical activity (PA) have not been well studied. We studied older adults’ acceptance, capacity, and experience using smartwatches for self-monitoring and promoting PA. We conducted individual interviews using semi-structured interview guides with 15 older adults who participated in two studies. The two studies employed smartwatches in combination with personalized exercise training to promote PA and the interventions were 4 and 24 weeks in length. Interviews were transcribed verbatim. Two researchers conducted inductive content analysis using NVivo V.12 to identify and categorize codes into major themes. Participants reported high overall acceptance, ease of use (i.e., charging, checking steps, reading the screen), and attractive design of smartwatches. Participants’ positive descriptions of their smartwatch experience included: increased activity awareness (step counts and heart rate), improved exercise accountability, and enhanced motivation (response to prompts). Most participants expressed interest in long-term smartwatch use. While participants expressed enjoyment of smartwatch technology for self-monitoring and PA promotion, some reported decreased motivation over time. Participants’ concerns of smartwatch functionalities included short battery life, inaccurate step-recordings, and touchscreen insensitivity. Some also reported failure to troubleshoot smartwatch syncing/pairing problems with smartphones and daily smartwatch charging issues. Smartwatch Bluetooth connectivity and battery life can be improved to increase usability and acceptability among older adults. Future research should explore the role of smartwatches for older adults’ PA with emphasis on behavior change over time.
14
Putra, Roni Anggara, Abdul Fadlil, and Imam Riadi. "Forensik Mobile Pada Smartwatch Berbasis Android." Jurnal Rekayasa Teknologi Informasi (JURTI) 1, no. 1 (2017): 41. http://dx.doi.org/10.30872/jurti.v1i1.638.
Texto completoResumen
Perkembangan teknologi pada saat ini berkembang sangat pesat. Salah satu contoh berkembangnya alat telekomunikasi yang dipadukan dengan jam tangan yang dikenal sebagai smartwatch. Perkembangan smartwatch telah menyamai kemampuaan yang ada di smatphone, sehingga tidak menutup kemungkinan smartwatch dapat digunakan sebagai alat tindak kejahatan. Hal ini merupakan tantangan bagi IT forensik dan penegak hukum untuk melakukan penyelidikan terhadap smartwatch dari seseorang yang melakukan kejahatan dijadikan tersangka dari sebuah kasus. Caranya adalah dengan menerapkan pengembangan metode-metode forensic yang ada, sehingga dari hasil yang didapatkan dari metode forensic yang dilakukan diharapakan menjadi hasil yang berguna bagi IT forensic dan penegak hukum.
15
Oudman, Erik, Beth Lloyd, Sterre Smits, Mareike Altgassen, and Albert Postma. "85 Smartwatch reminders support prospective memory in Korsakoff’s syndrome." Journal of the International Neuropsychological Society 29, s1 (2023): 488. http://dx.doi.org/10.1017/s1355617723006331.
Texto completoResumen
Objective:Korsakoff’s syndrome (KS) is a neuropsychiatric disorder, caused by malnutrition. Central to KS are severe amnesia and executive dysfunctions. KS patients often fail to remember future intentions (prospective memory),and rely heavily on external support by caregivers. Recently, specialized smartwatches have been developed to support prospective memory verbally and by displaying pictures of future events. We investigated the benefit of a smartwatch and smartphone compared to no aid in supporting time accuracy and the ability to carry out future intentions in one case study. In three subsequent case studies, we investigated the possible benefits of a smartwatch aid for prospective memory (PM) compared to verbal in-person reminders.Participants and Methods:In the first case study, one high-functioning KS patient with a WAIS IQ of 127 points, performed a total of 36 novel prospective memory tasks in three conditions (smartwatch, smartphone and no-aid).In the second case series, three KS patients with average IQ performed 30 everyday PM tasks in two conditions (smartwatch, in-person).Two dependent variables were indexed in both studies: PM time accuracy (in minutes), this was calculated as minutes difference from the assigned time, and precision of the PM task (correct or incorrect).Results:In the first study, time accuracy was improved with a smartwatch compared to a smartphone and no-aid condition. Furthermore, the smartwatch and smartphone conditions were more effective than no aid in assisting memory for task content. In the second study, both the smartwatch and in-person instructions were equally effective in supporting prospective memory tasks.Conclusions:Since prospective memory is compromised in KS, patients require assistance throughout the day in performing everyday and non-everyday tasks. The results of our case studies suggest that a smartwatch that gives specific verbal and visual reminders can be particularly helpful in supporting prospective memory for KS patients. Giving in-person instructions was equally effective as the use of this smartwatch, highlighting the possibility to support KS patients with less intensive everyday coaching. Together, these results are promising in applying smartwatches clinically to support prospective memory.
16
Rey, Bradley, Bongshin Lee, Eun Kyoung Choe, and Pourang Irani. "Investigating In-Situ Personal Health Data Queries on Smartwatches." Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 6, no. 4 (2022): 1–19. http://dx.doi.org/10.1145/3569481.
Texto completoResumen
Smartwatches enable not only the continuous collection of but also ubiquitous access to personal health data. However, exploring this data in-situ on a smartwatch is often reserved for singular and generic metrics, without the capacity for further insight. To address our limited knowledge surrounding smartwatch data exploration needs, we collect and characterize desired personal health data queries from smartwatch users. We conducted a week-long study (N = 18), providing participants with an application for recording responses that contain their query and current activity related information, throughout their daily lives. From the responses, we curated a dataset of 205 natural language queries. Upon analysis, we highlight a new preemptive and proactive data insight category, an activity-based lens for data exploration, and see the desired use of a smartwatch for data exploration throughout daily life. To aid in future research and the development of smartwatch health applications, we contribute the dataset and discuss implications of our findings.
17
Behzadi, Amirali, Alireza Sepehri Shamloo, Konstantinos Mouratis, Gerhard Hindricks, Arash Arya, and Andreas Bollmann. "Feasibility and Reliability of SmartWatch to Obtain 3-Lead Electrocardiogram Recordings." Sensors 20, no. 18 (2020): 5074. http://dx.doi.org/10.3390/s20185074.
Texto completoResumen
Some of the recently released smartwatch products feature a single-lead electrocardiogram (ECG) recording capability. The reliability of obtaining 3-lead ECG with smartwatches is yet to be confirmed in a large study. This study aimed to assess the feasibility and reliability of smartwatch to obtain 3-lead ECG recordings, the classical Einthoven ECG leads I-III compared to standard ECG. To record lead I, the watch was worn on the left wrist and the right index finger was placed on the digital crown for 30 s. For lead II, the watch was placed on the lower abdomen and the right index finger was placed on the digital crown for 30 s. For lead III, the same process was repeated with the left index finger. Spearman correlation and Bland-Altman tests were used for data analysis. A total of 300 smartwatch ECG tracings were successfully obtained. ECG waves’ characteristics of all three leads obtained from the smartwatch had a similar duration, amplitude, and polarity compared to standard ECG. The results of this study suggested that the examined smartwatch (Apple Watch Series 4) could obtain 3-lead ECG tracings, including Einthoven leads I, II, and III by placing the smartwatch on the described positions.
18
Bajaj, Ruhi Kiran, Rebecca Mary Meiring, and Fernando Beltran. "Co-Design, Development, and Evaluation of a Health Monitoring Tool Using Smartwatch Data: A Proof-of-Concept Study." Future Internet 15, no. 3 (2023): 111. http://dx.doi.org/10.3390/fi15030111.
Texto completoResumen
Computational analysis and integration of smartwatch data with Electronic Medical Records (EMR) present potential uses in preventing, diagnosing, and managing chronic diseases. One of the key requirements for the successful clinical application of smartwatch data is understanding healthcare professional (HCP) perspectives on whether these devices can play a role in preventive care. Gaining insights from the vast amount of smartwatch data is a challenge for HCPs, thus tools are needed to support HCPs when integrating personalized health monitoring devices with EMR. This study aimed to develop and evaluate an application prototype, co-designed with HCPs and employing design science research methodology and diffusion of innovation frameworks to identify the potential for clinical integration. A machine learning algorithm was developed to detect possible health anomalies in smartwatch data, and these were presented visually to HCPs in a web-based platform. HCPs completed a usability questionnaire to evaluate the prototype, and over 60% of HCPs scored positively on usability. This preliminary study tested the proposed research to solve the practical challenges of HCP in interpreting smartwatch data before fully integrating smartwatches into the EMR. The findings provide design directions for future applications that use smartwatch data to improve clinical decision-making and reduce HCP workloads.
19
Umami, Hanifatul, Gita Faroka, Fadhila Isnahardiyanti, Nahrodi Muhlis, and Rayfaldi Ananda An-Naafi. "GLUSENT (Glucose Assistant): Sugar Monitoring System Smart Watch Based on Bioelectronics and Internet of Things to Prevent Diabetes Melitus in The 5.0 Society Era." Asian Journal of Natural Sciences 2, no. 1 (2023): 25–34. http://dx.doi.org/10.55927/ajns.v2i1.3266.
Texto completoResumen
Diabetics in Indonesia are ranked 5th in the world. According to the International Diabetes Federation (IDF), there were 10.7 million people with diabetes in Indonesia in 2019 and a rapid increase to 19.5 million in 2021. Most cases of diabetes are not diagnosed early, this is the basis for the importance of monitoring blood glucose periodically. Based on our study of people aged 20-40 years, most of them have never done regular glucose monitoring. Lack of public awareness of the dangers of diabetes and fear of needles are the reasons for not monitoring blood glucose. Therefore, an effective non-invasive self-monitoring of glucose levels is needed. The Glusen Smartwatch is a smartwatch that integrates bioelectronics and the Internet of Things (IoT) for monitoring glucose levels through sweat taken from the wrist. If the glucose level exceeds the normal limit, this smartwatch will notify the user through sounds, vibrations and lights on the smartwatch. This smartwatch also provides a solution if the user has glucose above the normal limit, such as telling users if they need to exercise regularly, reduce stress, and lead a healthy diet. The basic components of the Glusen Smartwatch include electrochemical sensors, flexible photovoltaic cells, flexible Zn-MnO2 batteries, printed circuit board (PCB) and electronic ink (E-ink). The Glusen Smartwatch is expected to be a solution for continuous early glucose monitoring so as to reduce the number of diabetics in Indonesia. The advantages of this tool can be used independently and non-invasively.
20
Giang, Wayne C. W., Huei-Yen Winnie Chen, and Birsen Donmez. "Smartwatches vs. Smartphones." International Journal of Mobile Human Computer Interaction 9, no. 2 (2017): 39–57. http://dx.doi.org/10.4018/ijmhci.2017040103.
Texto completoResumen
This work seeks to understand whether the unique features of a smartwatch, compared to a smartphone, mitigate or exacerbate driver distraction due to notifications, and to provide insights about drivers' perceptions of the risks associated with using smartwatches while driving. As smartwatches are gaining popularity among consumers, there is a need to understand how smartwatch use may influence driving performance. Previous driving research has examined voice calling on smartwatches, but not interactions with notifications, a key marketed feature. Engaging with notifications (e.g., reading and texting) on a handheld device is a known distraction associated with increased crash risks. Two driving simulator studies compared smartwatch to smartphone notifications. Experiment I asked participants to read aloud brief text notifications and Experiment II had participants manually select a response to arithmetic questions presented as notifications. Both experiments investigated the resulting glances to and physical interactions with the devices, as well as self-reported risk perception. Experiment II also investigated driving performance and self-reported knowledge/expectation about legislation surrounding the use of smart devices while driving. Experiment I found that participants were faster to visually engage with the notification on the smartwatch than the smartphone, took longer to finish reading aloud the notifications, and exhibited more glances longer than 1.6 s. Experiment II found that participants took longer to reply to notifications and had longer overall glance durations on the smartwatch than the smartphone, along with longer brake reaction times to lead vehicle braking events. Compared to the no device baseline, both devices increased lane position variability and resulted in higher self-reported perceived risk. Experiment II participants also considered that smartwatch use while driving deserves penalties equal to or less than smartphone use. The findings suggest that smartwatches may have road safety consequences. Given the common view among participants to associate smartwatch use with equal or less traffic penalties than smartphone use, there may be a disconnect between drivers' actual performance and their perceptions about smartwatch use while driving.
21
Hamidou, Saoud, Mohamed Abdallah, Rawad Abdulghafor, and Sharyar Wani. "Emergency Smartwatch for Fisherman." International Journal on Perceptive and Cognitive Computing 6, no. 2 (2020): 77–80. http://dx.doi.org/10.31436/ijpcc.v6i2.162.
Texto completoResumen
This 
 work
 is about designing a smartwatch that is used mostly by fishermen. The watch provides the location of the user using the global positioning system (GPS) and a panic button that he can use in case of emergency. It can record the heart rate by using a heart rate sensor. The 
 paper 
 works with a software that is used by the appropriate authorities; they record the information provided by the smartwatch. The third part of the 
 paper
 is a mobile application used by the rescue team; the application shows the location of the fisherman. This 
 paper
 aims to help fishermen in case of emergency cases, they can be saved by the rescue team. Based on the heart rate sensor and the panic button provided by the smartwatch, the appropriate authorities can know when a fisherman needs help and send the rescue team.
22
Jeong, Hayeon, Heepyung Kim, Rihun Kim, Uichin Lee, and Yong Jeong. "Smartwatch Wearing Behavior Analysis." Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 1, no. 3 (2017): 1–31. http://dx.doi.org/10.1145/3131892.
Texto completo
23
Bremmers, Suzanne. "Smartwatch helpt COPD-patiënt." Zorgvisie ICT 18, no. 4 (2017): 10–11. http://dx.doi.org/10.1007/s41186-017-0045-4.
Texto completo
24
gvg. "Smartwatch forscht nach Vorhofflimmern." CardioVasc 18, no. 2 (2018): 14. http://dx.doi.org/10.1007/s15027-018-1329-8.
Texto completo
25
Lux, Sebastian. "Ausschlag nach Smartwatch-Upgrade." HNO Nachrichten 54, no. 2 (2024): 10. http://dx.doi.org/10.1007/s00060-024-9119-7.
Texto completo
26
Han, Sumin, and Rob Paul. "Smartwatch gait coordination index: New measure for human gait utilizing smartwatch sensor." Medicine 102, no. 12 (2023): e33267. http://dx.doi.org/10.1097/md.0000000000033267.
Texto completo
27
Chotiyaputta, Veerisa, and Donghee Shin. "Explicating Consumer Adoption of Wearable Technologies." International Journal of Technology and Human Interaction 18, no. 1 (2022): 1–21. http://dx.doi.org/10.4018/ijthi.293195.
Texto completoResumen
This research aims to determine the key antecedent factors in consumers’ adoption of and their intention to recommend smartwatch wearable technology. The proposed research model combines the current technology acceptance and innovation diffusion theories with perceived aesthetic and perceived privacy risk to explain individuals’ smartwatch adoption and subsequent recommendation to other people. Based on a sample of 299 completed individual online surveys, the research employed partial least squares (a variance-based analysis method) for the model and hypotheses testing. The results showed some similarities as well as differences from the previous literature. The study found that performance expectancy, habit, and perceived aesthetic were the main predictors of smartwatch adoption. Compatibility was the antecedent factor of performance expectancy, and innovativeness directly influenced user adoption and effort expectancy. Consequently, user smartwatch adoption usually led to recommendation.
28
Brew, Bruce, Steven G. Faux, and Elizabeth Blanchard. "Effectiveness of a Smartwatch App in Detecting Induced Falls: Observational Study." JMIR Formative Research 6, no. 3 (2022): e30121. http://dx.doi.org/10.2196/30121.
Texto completoResumen
Background Older adults are at an increased risk of falls with the consequent impacts on the health of the individual and health expenditure for the population. Smartwatch apps have been developed to detect a fall, but their sensitivity and specificity have not been subjected to blinded assessment nor have the factors that influence the effectiveness of fall detection been fully identified. Objective This study aims to assess accuracy metrics for a novel fall detection smartwatch algorithm. Methods We performed a cross-sectional study of 22 healthy adults comparing the detection of induced forward, side (left and right), and backward falls and near falls provided by a smartwatch threshold-based algorithm, with a video record of induced falls serving as the gold standard; a blinded assessor compared the two. Three different smartwatches with two different operating systems were used. There were 226 falls: 64 were backward, 51 forward, 55 left sided, and 56 right sided. Results The overall smartwatch app sensitivity for falls was 77%, the specificity was 99%, the false-positive rate was 1.7%, and the false-negative rate was 16.4%. The positive and negative predictive values were 98% and 84%, respectively, while the accuracy was 89%. There were 249 near falls: the sensitivity was 89%, the specificity was 100%, there were no false positives, 11% were false negatives, the positive predictive value was 100%, the false-negative predictive value was 83%, and the accuracy was 93%. Conclusions Falls were more likely to be detected if the fall was on the same side as the wrist with the smartwatch. There was a trend toward some smartwatches and operating systems having superior sensitivity, but these did not reach statistical significance. The effectiveness data and modifying factors pertaining to this smartwatch app can serve as a reference point for other similar smartwatch apps.
29
Rafl, Jakub, Thomas E. Bachman, Veronika Rafl-Huttova, Simon Walzel, and Martin Rozanek. "Commercial smartwatch with pulse oximeter detects short-time hypoxemia as well as standard medical-grade device: Validation study." DIGITAL HEALTH 8 (January 2022): 205520762211321. http://dx.doi.org/10.1177/20552076221132127.
Texto completoResumen
Objective We investigated how a commercially available smartwatch that measures peripheral blood oxygen saturation (SpO2) can detect hypoxemia compared to a medical-grade pulse oximeter. Methods We recruited 24 healthy participants. Each participant wore a smartwatch (Apple Watch Series 6) on the left wrist and a pulse oximeter sensor (Masimo Radical-7) on the left middle finger. The participants breathed via a breathing circuit with a three-way non-rebreathing valve in three phases. First, in the 2-minute initial stabilization phase, the participants inhaled the ambient air. Then in the 5-minute desaturation phase, the participants breathed the oxygen-reduced gas mixture (12% O2), which temporarily reduced their blood oxygen saturation. In the final stabilization phase, the participants inhaled the ambient air again until SpO2 returned to normal values. Measurements of SpO2 were taken from the smartwatch and the pulse oximeter simultaneously in 30-s intervals. Results There were 642 individual pairs of SpO2 measurements. The bias in SpO2 between the smartwatch and the oximeter was 0.0% for all the data points. The bias for SpO2 less than 90% was 1.2%. The differences in individual measurements between the smartwatch and oximeter within 6% SpO2 can be expected for SpO2 readings 90%–100% and up to 8% for SpO2 readings less than 90%. Conclusions Apple Watch Series 6 can reliably detect states of reduced blood oxygen saturation with SpO2 below 90% when compared to a medical-grade pulse oximeter. The technology used in this smartwatch is sufficiently advanced for the indicative measurement of SpO2 outside the clinic. Trial Registration ClinicalTrials.gov NCT04780724
30
Volsa, Selina, Bernad Batinic, and Stefan Stieger. "Self-Reports in the Field Using Smartwatches: An Open-Source Firmware Solution." Sensors 22, no. 5 (2022): 1980. http://dx.doi.org/10.3390/s22051980.
Texto completoResumen
In situ self-reports are a useful tool in the social sciences to supplement laboratory experiments. Smartwatches are a promising form factor to realize these methods. However, to date, no user-friendly, general-purpose solution has been available. This article therefore presents a newly developed, free and open-source firmware that facilitates the Experience Sampling Method and other self-report methods on a commercially-available, programmable smartwatch based on the ESP32 microcontroller. In a small-scale pilot study comparing this smartwatch and firmware to an equivalent design on smartphones, participants using the smartwatch showed increased compliance. The presented project demonstrates a useful tool for complementary tools like smartphones for self-reports.
31
Baudier, Patricia, Chantal Ammi, and Samuel Fosso Wamba. "Differing Perceptions of the Smartwatch by Users Within Developed Countries." Journal of Global Information Management 28, no. 4 (2020): 1–20. http://dx.doi.org/10.4018/jgim.2020100101.
Texto completoResumen
This article aims to identify differences in the impact of Self-connectivity and the variables of the technological acceptance model (TAM) on smartwatch adoption in developed countries. The countries involved in the data collection were the United States of America, the United Kingdom, Germany, and France. A sample of 1,197 respondents was used. The study identifies distinct adoption behaviours of smartwatch users in these countries and the moderating impacts of age and gender. The study's results confirm that perceived ease-of-use has no impact on attitude-toward-using the smartwatch and its findings emphasize the key role of perceived-connectivity and the moderating effect of culture on the adoption of innovative products.
32
Buelga Suárez, Mauro, Marina Pascual Izco, Jesús García Montalvo, and Gonzalo Luis Alonso Salinas. "Accuracy of Smartwatch Electrocardiographic Recording in the Acute Coronary Syndrome Setting: Rationale and Design of the ACS WATCH II Study." Journal of Clinical Medicine 13, no. 2 (2024): 389. http://dx.doi.org/10.3390/jcm13020389.
Texto completoResumen
Background: Acute Coronary Syndrome (ACS), with or without ST-segment elevation, is a major contributor to global mortality and morbidity. Swift diagnosis and treatment are vital for mitigating cardiac damage and improving long-term outcomes. The 12-lead electrocardiogram (ECG) currently serves as the gold standard for diagnosis in ACS with ST-segment elevation and may support the diagnosis in ACS without ST-segment elevation. However, the growing prevalence of smartwatches enables the acquisition of electrocardiographic data without traditional ECG equipment. While smaller studies support smartwatch ECG use, larger-scale validation within ACS remains lacking. The ACS WATCH II study aims to validate smartwatch ECG recordings for ACS. Methods: The primary objective is to validate smartwatch-obtained electrocardiographic data in patients presenting with ACS. Two cohorts of 120 patients each, presenting ACS with and without ST-segment elevation, will be assessed. Smartwatches will capture recordings of leads I, III, and V2 alongside standard ECGs. These leads, chosen due to a 97% ACS diagnosis sensitivity in previous studies, will undergo blind evaluation by two experienced external assessors against conventional ECG. Additionally, a control sample of 60 healthy individuals will be included. Conclusions: ACS WATCH II pioneers large-scale prospective validation of smartwatch ECG recordings in ACS patients. Additionally, it indirectly validates a swift diagnostic approach using three leads (I, III, and V2). This could expedite time-critical ACS diagnoses and simplify access through smartwatch-based diagnosis.
33
Vuković, Marin, Željka Car, Jasmina Ivšac Pavlisa, and Lidija Mandić. "Smartwatch as an Assistive Technology." International Journal of E-Health and Medical Communications 9, no. 1 (2018): 23–34. http://dx.doi.org/10.4018/ijehmc.2018010102.
Texto completoResumen
Wearables may have notable potential as an assistive technology for persons with various difficulties. Although quite popular, smartwatches' niches are still revealing. One of them is definitely in the domain of assistive technology due to their communication and location features. Positioning features enable parents or caregivers to know the whereabouts of child or persons with disabilities, thus increasing their safety. The paper presents smartwatch tracking system for people with complex communication needs with emphasis on detection of smartwatch wearer's common movement routes. The application is a result of multidisciplinary research performed in the area of information and communication technology, as an assistive technology aiming to explore the technological possibilities of connecting new generations of mobile devices and their technological supplements, or wearables, in order to establish a different communication and location aids.
34
Dewan, Ankita, Venkata M. V. Gunturi, and Vinayak Naik. "NEAT activity detection using smartwatch." International Journal of Ad Hoc and Ubiquitous Computing 45, no. 1 (2024): 36–51. http://dx.doi.org/10.1504/ijahuc.2024.136141.
Texto completo
35
Kranthi, A. K., and K. A. Asraar Ahmed. "Determinants of smartwatch adoption among IT professionals - an extended UTAUT2 model for smartwatch enterprise." International Journal of Enterprise Network Management 9, no. 3/4 (2018): 294. http://dx.doi.org/10.1504/ijenm.2018.094669.
Texto completo
36
Kranthi, A. K., and K. A. Asraar Ahmed. "Determinants of smartwatch adoption among IT professionals - an extended UTAUT2 model for smartwatch enterprise." International Journal of Enterprise Network Management 9, no. 3/4 (2018): 294. http://dx.doi.org/10.1504/ijenm.2018.10015843.
Texto completo
37
Cinar Bilge, Piri, Erboy Keskintıg Fatma, Soylemez Cansu, et al. "Scanning of obstructive sleep apnea syndrome using smartwatch: A comparison of smartwatch and polysomnography." Journal of Clinical Neuroscience 119 (January 2024): 212–19. http://dx.doi.org/10.1016/j.jocn.2023.12.009.
Texto completo
38
Moreno, Daniel, Evan Glasheen, Antoinette Domingo, et al. "Validity of Caloric Expenditure Measured from a Wheelchair User Smartwatch." International Journal of Sports Medicine 41, no. 08 (2020): 505–11. http://dx.doi.org/10.1055/a-1088-5629.
Texto completoResumen
AbstractThe objective of this study was to investigate the validity of measured caloric expenditure from a fitness smartwatch designed to measured values in wheelchair users against criterion values from a portable metabolic system. 15 wheelchair users and 15 able-bodied participants completed multiple tasks; wheelchair treadmill routine at 30, 45, and 60 strokes per minute, arm cycle ergometry at 45, 60, and 80 revolutions per minute, and arm cycle ergometry VO2Peak test. There were no interactions for device or task and group (wheelchair users vs. able bodied, p=0.375-0.944) therefore results were pooled across groups for all measures. The smartwatch exhibited poor to moderate caloric expenditure association during wheelchair treadmill routine (ICC<0.39) and arm cycle ergometry (ICC<0.541). Smartwatch underestimated caloric expenditure during the wheelchair treadmill task (Mean differences (Limits of Agreement)) (−2.11 (−8.19–3.96), −3.68 (−12.64–5.28), and −4.51 (−15.05–6.02)) and overestimated during the arm cycle ergometry task (0.89 (−3.10–4.88), 3.40 (−0.31–7.12), and 2.81 (−1.71–7.32)). The smartwatch is currently not well suited to calculate caloric expenditure when performing exercise tasks on a wheelchair treadmill and arm cycle ergometry.
39
Mardini, Mamoun, Todd Manini, and Jennifer Schrack. "Smart Wearables in the Lens of Aging: Results From the ROAMM Study." Innovation in Aging 4, Supplement_1 (2020): 798. http://dx.doi.org/10.1093/geroni/igaa057.2894.
Texto completoResumen
Abstract Continuous, long-term monitoring with remote capabilities using wearable technology is ideal for capturing information about patient/participant symptoms synced to sensor-based information. The Real-time Online Assessment and Mobility Monitor (ROAMM) is a smartwatch framework configured to collect data in free-living settings from both sensor-based (location and movement) and responses to symptom notifications through a visual display. The symposium presents the overall framework and preliminary findings from a demonstration study in older adults with knee osteoarthritis. Karnati will present the general framework of ROAMM explaining the data flow from the smartwatch to end users (clinicians and research). He will highlight components in the design that makes the framework unique and highly flexible to serve different studies with different research questions. Rouzaud evaluated satisfaction, usability and compliance wearing a smartwatch and using the ROAMM app. Participants were compliant to ecological prompts about pain, fatigue and mood three times a day (82.5% compliance rate). Additionally, &gt; 70% reported being satisfied with the function/usability and comfort with using ROAMM and wearing the smartwatch. Mardini examined the temporal relationship between ecological pain and derived life-space mobility features from Global Positioning System coordinates. Results suggested that higher level of knee pain in older adults was associated with lower life-space mobility. Manini examined physician perception towards an electronic health record (EHR) graphical interface of top ranked patient attributes of pain, falls, hydration and mobility patterns. Results indicated a relatively high level of usability of the EHR interface depicting smartwatch data.
40
Samol, Alexander, Kristina Bischof, Blerim Luani, Dan Pascut, Marcus Wiemer, and Sven Kaese. "Single-Lead ECG Recordings Including Einthoven and Wilson Leads by a Smartwatch: A New Era of Patient Directed Early ECG Differential Diagnosis of Cardiac Diseases?" Sensors 19, no. 20 (2019): 4377. http://dx.doi.org/10.3390/s19204377.
Texto completoResumen
Background: Smartwatches that are able to record a bipolar ECG and Einthoven leads were recently described. Nevertheless, for detection of ischemia or other cardiac diseases more leads are required, especially Wilson’s chest leads. Objectives: Feasibility study of six single-lead smartwatch (Apple Watch Series 4) ECG recordings including Einthoven (I, II, III) and Wilson-like pseudo-unipolar chest leads (Wr, Wm, Wl). Methods: In 50 healthy subjects (16 males; age: 36 ± 11 years, mean ± SD) without known cardiac disorders, a standard 12-lead ECG and a six single-lead ECG using an Apple Watch Series 4 were performed under resting conditions. Recording of Einthoven I was performed with the watch on the left wrist and the right index finger on the crown, Einthoven II was recorded with the watch on the left lower abdomen and the right index finger on the crown, Einthoven III was recorded with the watch on the left lower abdomen and the left index finger on the crown. Wilson-like chest leads were recorded corresponding to the locations of V1 (Wr), V4 (Wm) and V6 (Wl) in the standard 12-lead ECG. Wr was recorded in the fourth intercostal space right parasternal, Wm was recorded in the fifth intercostal space on the midclavicular line, and Wl was recorded in the fifth intercostal space in left midaxillary line. For all Wilson-like chest lead recordings, the smartwatch was placed on the described three locations on the chest, the right index finger was placed on the crown and the left hand encompassed the right wrist. Both hands and forearms also had contact to the chest. Three experienced cardiologists were independently asked to allocate three bipolar limb smartwatch ECGs to Einthoven I–III leads, and three smartwatch Wilson-like chest ECGs (Wr, Wm, Wl) to V1, V4 and V6 in the standard 12-lead ECG for each subject. Results: All 300 smartwatch ECGs showed a signal quality useable for diagnostics with 281 ECGs of good signal quality (143 limb lead ECGs (95%), 138 chest lead ECGs (92%). Nineteen ECGs had a moderate signal quality (7 limb lead ECGs (5%), 12 chest lead ECGs (8%)). One-hundred percent of all Einthoven and 92% of all Wilson-like smartwatch ECGs were allocated correctly to corresponding leads from 12-lead ECG. Forty-six subjects (92%) were assigned correctly by all cardiologists. Allocation errors were due to similar morphologies and amplitudes in at least two of the three recorded Wilson-like leads. Despite recording with a bipolar smartwatch device, morphology of all six leads was identical to standard 12-lead ECG. In two patients with acute anterior myocardial infarction, all three cardiologists recognized the ST-elevations in Wilson-like leads and assumed an occluded left anterior descending coronary artery correctly. Conclusion: Consecutive recording of six single-lead ECGs including Einthoven and Wilson-like leads by a smartwatch is feasible with good ECG signal quality. Thus, this simulated six-lead smartwatch ECG may be useable for the detection of cardiac diseases necessitating more than one ECG lead like myocardial ischemia or more complex cardia arrhythmias.
41
Kobayashi, Satoshi, and Tatsuhito Hasegawa. "Detection of Motion on a Trampoline with a Smartwatch." Sensors 21, no. 24 (2021): 8413. http://dx.doi.org/10.3390/s21248413.
Texto completoResumen
In this study, we develop a method for detecting the motions performed on a trampoline using an accelerometer mounted on a smartwatch. This method will lead to a system that can be used to promote trampoline exercise using a home trampoline by detecting motions on the trampoline using a smartwatch. We proposed a method based on the convolutional neural network to detect the motions on a trampoline. As a result of the performance evaluation by leave-one-subject-out cross-validation on eight subjects, our method achieves 78.8% estimation accuracy, which is the best estimation accuracy compared to the baseline methods. We also evaluate the inference time and the battery consumption when the model is actually running on a smartwatch. Our method is effective for on-device prediction.
42
Yao, Lin, Xi Pan, Yiqun Yang, et al. "Accuracy and reliability of wristband smartwatch-based estimation of circadian rhythm of heart rate in stroke patients." Neurology Asia 29, no. 1 (2024): 61–69. http://dx.doi.org/10.54029/2024mui.
Texto completoResumen
Background & Objective: Stroke has a high incidence, disability, and mortality rate, which has a serious impact on the patients’ quality of life. Heart rate (HR) is valuable in the prognostic assessment of stroke patients. Continuous HR tracking helps to detect abnormal indicators and provide interventions timely to avoid disease progression. However, the cuff-based or electrode-based HR monitoring tools commonly used in clinic are not suitable for continuous monitoring in daily life. The aim of this study was to verify the accuracy and reliability of a wristband smartwatch in monitoring HR over 24-hour in stroke patients, including in terms of the circadian rhythm characteristics of HR. Methods: 25 stroke patients underwent 24-hour HR monitoring by wearing a smartwatch and a clinical monitor. We use ambulatory blood pressure monitors and bedside monitors as the gold standard for clinical HR monitoring. The degree of association between the two measurements was compared using Pearson’s correlation coefficient. Bland-Altman analysis showed the accuracy of the smartwatch in HR monitoring. Intraclass correlation coefficients (ICC) and coefficients of variation (CV) were used to verify the reliability of the wristband smartwatch for repeated measurements. Results: A total of 625 heart rate pairs were acquired. The smartwatches had an average error of 0.816 bpm in 24-hour, with no difference from standard monitors (P>0.05), indicating good accuracy. The intraday repeated measures ICC was 0.969 and the interday repeated measures ICC was 0.788, indicating good reliability. Conclusions: The smartwatch is acceptable in detecting 24-hour ambulatory HR in stroke patients, and in the future, long-term HR monitoring with diurnal variations can be performed in daily life.
43
Ponnada, Aditya, Jixin Li, Shirlene Wang та ін. "Contextual Biases in Microinteraction Ecological Momentary Assessment (μEMA) Non-response". Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 6, № 1 (2022): 1–24. http://dx.doi.org/10.1145/3517259.
Texto completoResumen
Ecological momentary assessment (EMA) is used to gather in-situ self-report on behaviors using mobile devices. Microinteraction EMA (μEMA), is a type of EMA where each survey is only one single question that can be answered with a glanceable microinteraction on a smartwatch. Prior work shows that even when μEMA interrupts far more frequently than smartphone-EMA, μEMA yields higher response rates with lower burden. We examined the contextual biases associated with non-response of μEMA prompts on a smartwatch. Based on prior work on EMA non-response and smartwatch use, we identified 10 potential contextual biases from three categories: temporal (time of the day, parts of waking day, day of the week, and days in study), device use (screen state, charging status, battery mode, and phone usage), and activity (wrist motion and location). We used data from a longitudinal study where 131 participants (Mean age 22.9 years, SD = 3.0) responded to μEMA surveys on a smartwatch for at least six months. Using mixed-effects logistic regression, we found that all temporal, activity/mobility, and device use variables had a statistically significant (p<0.001) association with momentary μEMA non-response. We discuss the implication of these results for future use of context-aware μEMA methodology.
44
Hong, Jun-Hyuk. "A Study on the Understanding of Smart Watch on Intention of Continuous Use in Health Care." Global Convergence Research Academy 1, no. 2 (2022): 93–102. http://dx.doi.org/10.57199/jgcr.2022.1.2.93.
Texto completoResumen
The healthcare industry is changing dramatically due to the need for real-time monitoring. With point-of-care, diagnostics and long-term health conditions, most smartwatch manufacturers are shifting markedly with a focus on healthcare applications. The purpose of this study is to analyze whether there is a mediating effect between the understanding of the smartwatch system and the continuous intention to use the system. For this study, the degree of understanding of the smart watch system was classified into safety, convenience, and technology readiness. Smart watch trust was selected as an individual dependent variable. For this study, 250 samples were collected through an online survey, and 220 of the respondents gave valid responses. For statistical analysis, structural equation modeling technique was used. The study results are summarized as follows. First, safety, convenience, and technical readiness have a positive effect on motivation to use. Second, trust in smartwatches has a positive effect on motivation to use them. Third, the motive for use has a positive effect on the intention of continuous use. Through this empirical study, it became the basis for continuous use of smartwatch applications by users. It will serve as an opportunity to provide smartwatch manufacturers with a mechanism for the user's decision-making process.
45
Herman, Ivan, Maylis Delest, and Guy Melancon. "Tree Visualisation and Navigation Clues for Information Visualisation." Computer Graphics Forum 17, no. 2 (1998): 153–65. http://dx.doi.org/10.1111/1467-8659.00235.
Texto completo
46
Tchuente, Franck, Natalie Baddour, and Edward D. Lemaire. "Classification of Aggressive Movements Using Smartwatches." Sensors 20, no. 21 (2020): 6377. http://dx.doi.org/10.3390/s20216377.
Texto completoResumen
Recognizing aggressive movements is a challenging task in human activity recognition. Wearable smartwatch technology with machine learning may be a viable approach for human aggressive behavior classification. This research identified a viable classification model and feature selector (CM-FS) combination for separating aggressive from non-aggressive movements using smartwatch data and determined if only one smartwatch is sufficient for this task. A ranking method was used to select relevant CM-FS models across accuracy, sensitivity, specificity, precision, F-score, and Matthews correlation coefficient (MCC). The Waikato environment for knowledge analysis (WEKA) was used to run 6 machine learning classifiers (random forest, k-nearest neighbors (kNN), multilayer perceptron neural network (MP), support vector machine, naïve Bayes, decision tree) coupled with three feature selectors (ReliefF, InfoGain, Correlation). Microsoft Band 2 accelerometer and gyroscope data were collected during an activity circuit that included aggressive (punching, shoving, slapping, shaking) and non-aggressive (clapping hands, waving, handshaking, opening/closing a door, typing on a keyboard) tasks. A combination of kNN and ReliefF was the best CM-FS model for separating aggressive actions from non-aggressive actions, with 99.6% accuracy, 98.4% sensitivity, 99.8% specificity, 98.9% precision, 0.987 F-score, and 0.984 MCC. kNN and random forest classifiers, combined with any of the feature selectors, generated the top models. Models with naïve Bayes or support vector machines had poor performance for sensitivity, F-score, and MCC. Wearing the smartwatch on the dominant wrist produced the best single-watch results. The kNN and ReliefF combination demonstrated that this smartwatch-based approach is a viable solution for identifying aggressive behavior. This wrist-based wearable sensor approach could be used by care providers in settings where people suffer from dementia or mental health disorders, where random aggressive behaviors often occur.
47
Sardana, Mayank, Honghuang Lin, Yuankai Zhang, et al. "Association of Habitual Physical Activity With Home Blood Pressure in the Electronic Framingham Heart Study (eFHS): Cross-sectional Study." Journal of Medical Internet Research 23, no. 6 (2021): e25591. http://dx.doi.org/10.2196/25591.
Texto completoResumen
Background When studied in community-based samples, the association of physical activity with blood pressure (BP) remains controversial and is perhaps dependent on the intensity of physical activity. Prior studies have not explored the association of smartwatch-measured physical activity with home BP. Objective We aimed to study the association of habitual physical activity with home BP. Methods Consenting electronic Framingham Heart Study (eFHS) participants were provided with a study smartwatch (Apple Watch Series 0) and Bluetooth-enabled home BP cuff. Participants were instructed to wear the watch daily and transmit BP values weekly. We measured habitual physical activity as the average daily step count determined by the smartwatch. We estimated the cross-sectional association between physical activity and average home BP using linear mixed effects models adjusting for age, sex, wear time, antihypertensive drug use, and familial structure. Results We studied 660 eFHS participants (mean age 53 years, SD 9 years; 387 [58.6%] women; 602 [91.2%] White) who wore the smartwatch 5 or more hours per day for 30 or more days and transmitted three or more BP readings. The mean daily step count was 7595 (SD 2718). The mean home systolic and diastolic BP (mmHg) were 122 (SD 12) and 76 (SD 8). Every 1000 increase in the step count was associated with a 0.49 mmHg lower home systolic BP (P=.004) and 0.36 mmHg lower home diastolic BP (P=.003). The association, however, was attenuated and became statistically nonsignificant with further adjustment for BMI. Conclusions In this community-based sample of adults, higher daily habitual physical activity measured by a smartwatch was associated with a moderate, but statistically significant, reduction in home BP. Differences in BMI among study participants accounted for the majority of the observed association.
48
Kwon, Joon-myoung, Yong-Yeon Jo, Soo Youn Lee, et al. "Artificial Intelligence-Enhanced Smartwatch ECG for Heart Failure-Reduced Ejection Fraction Detection by Generating 12-Lead ECG." Diagnostics 12, no. 3 (2022): 654. http://dx.doi.org/10.3390/diagnostics12030654.
Texto completoResumen
Background: We developed and validated an artificial intelligence (AI)-enabled smartwatch ECG to detect heart failure-reduced ejection fraction (HFrEF). Methods: This was a cohort study involving two hospitals (A and B). We developed the AI in two steps. First, we developed an AI model (ECGT2T) to synthesize ten-lead ECG from the asynchronized 2-lead ECG (Lead I and II). ECGT2T is a deep learning model based on a generative adversarial network, which translates source ECGs to reference ECGs by learning styles of the reference ECGs. For this, we included adult patients aged ≥18 years from hospital A with at least one digitally stored 12-lead ECG. Second, we developed an AI model to detect HFrEF using a 10 s 12-lead ECG. The AI model was based on convolutional neural network. For this, we included adult patients who underwent ECG and echocardiography within 14 days. To validate the AI, we included adult patients from hospital B who underwent two-lead smartwatch ECG and echocardiography on the same day. The AI model generates a 10 s 12-lead ECG from a two-lead smartwatch ECG using ECGT2T and detects HFrEF using the generated 12-lead ECG. Results: We included 137,673 patients with 458,745 ECGs and 38,643 patients with 88,900 ECGs from hospital A for developing the ECGT2T and HFrEF detection models, respectively. The area under the receiver operating characteristic curve of AI for detecting HFrEF using smartwatch ECG was 0.934 (95% confidence interval 0.913–0.955) with 755 patients from hospital B. The sensitivity, specificity, positive predictive value, and negative predictive value of AI were 0.897, 0.860, 0.258, and 0.994, respectively. Conclusions: An AI-enabled smartwatch 2-lead ECG could detect HFrEF with reasonable performance.
49
Nowell, William Benjamin, Jeffrey R. Curtis, Sandra K. Nolot, et al. "Digital Tracking of Rheumatoid Arthritis Longitudinally (DIGITAL) Using Biosensor and Patient-Reported Outcome Data: Protocol for a Real-World Study." JMIR Research Protocols 8, no. 9 (2019): e14665. http://dx.doi.org/10.2196/14665.
Texto completoResumen
Background Rheumatoid arthritis (RA) is a condition with symptoms that vary over time. The typical 3- to 6-month interval between physician visits may lead to patients failing to recall or underreporting symptoms experienced during the interim. Wearable digital technology enables the regular passive collection of patients’ biometric and activity data. If it is shown to be strongly related to data captured by patient-reported outcome (PRO) measures, information collected passively from wearable digital technology could serve as an objective proxy or be complementary to patients’ subjective experience of RA symptoms. Objective The goal of this study is to characterize the extent to which digital measures collected from a consumer-grade smartwatch agree with measures of RA disease activity and other PROs collected via a smartphone app. Methods This observational study will last 6 months for each participant. We aim to recruit 250 members of the ArthritisPower registry with an RA diagnosis who will receive a smartwatch to wear for the period of the study. From the ArthritisPower mobile app on their own smartphone device, participants will be prompted to answer daily and weekly electronic PRO (ePRO) measures for the first 3 months. Results The study was launched in December 2018 and will require up to 18 months to complete. Study results are expected to be published by the end of 2021. Conclusions The completion of this study will provide important data regarding the following: (1) the relationship between passively collected digital measures related to activity, heart rate, and sleep collected from a smartwatch with ePROs related to pain, fatigue, physical function, and RA flare entered via smartphone app; (2) determine predictors of adherence with smartwatch and smartphone app technology; and (3) assess the effect of study-specific reminders on adherence with the smartwatch. International Registered Report Identifier (IRRID) DERR1-10.2196/14665
50
Muh. Miftakhun Nizar, Ronggo Alit, and Firza Prima Aditiawan. "IMPLEMENTASI METODE MOORA PADA SISTEM PENDUKUNG KEPUTUSAN PEMILIHAN SMARTWATCH TERBAIK." Jurnal Informatika dan Sistem Informasi 2, no. 1 (2021): 34–42. http://dx.doi.org/10.33005/jifosi.v2i1.269.
Texto completoResumen
Smartwatch atau bisa disebut jam pintar merupakan teknologi terbaru dari jam tangan digital maupun analog. Jam tangan ini merupakan jam tangan pintar yang menerapkan konsep IoT (Internet Of Things) dan memiliki fitur canggih seperti fitness atau health tracker, navigasi GPS, notifikasi pengingat, bahkan hiburan. Saat ini banyak smartwatch yang beredar mulai dari brand ternama hingga biasa saja dan harga yang beragam mulai dari Rp.45.700 hingga Rp. 19.999.000. Penelitian ini menggunakan metode MOORA untuk proses perhitungan sistem pendukung keputusan dalam pemilihan smartwatch terbaik. Metode MOORA digunakan karena metode MOORA memiliki tingkat fleksibilitas dan kemudahan untuk dipahami dalam memisahkan subjektif dari suatu proses evaluasi kedalam kriteria bobot keputusan dengan beberapa atribut pengambilan keputusan. Sistem pendukung keputusan ini dibangun menggunakan framework CodeIgniter dan database MySQL. Hasil penelitian menunjukkan sistem dapat berkerja dengan baik sesuai fungsinya, dan dapat menghasilkan hasil akhir Apple Watch Series 6 Aluminum 44mm dengan nilai 2.6638231757.