This article was co-authored by staff writer, Rain Kengly. Rain Kengly is a Technology Writer. As a storytelling enthusiast with a penchant for technology, they hope to create long-lasting connections with readers from all around the globe. Rain graduated from San Francisco State University with a BA in Cinema.
Do you want to send your location to a friend on WhatsApp using your mobile device? You'll be able to send a one-time pin of your current location, or you can share your live location for up to eight hours at a time. You can stop sharing your location at any time. This will show you how to send a map with a pin of your current location to a contact in WhatsApp using your iPhone, iPad, or Android device.
This article was co-authored by staff writer, Rain Kengly. Rain Kengly is a Technology Writer. As a storytelling enthusiast with a penchant for technology, they hope to create long-lasting connections with readers from all around the globe. Rain graduated from San Francisco State University with a BA in Cinema. This article has been viewed 878, 907 times.
Digital Bread Crumbs: Following Your Cell Phone Trail
If you're using WhatsApp on your iPhone or iPad, you can easily share your current location with anyone you're chatting with. Before you get started, you'll need to make sure location services are enabled. Open your iPhone's Settings app, tap "WhatsApp, " select "Location, " and then choose "Always." Now, open WhatsApp and tap the "Chats" tab. Select a conversation with the person who needs your location, or tap the plus sign to create a new message. You can also share your location in a group chat. When the conversation appears, tap the plus sign at the bottom and select "Location" to open the map. Tap "Share Live Location, " then choose how long you want your location to be available to the other person or people in the chat. Last, tap "Send" to share your location. When the selected time period is over, your live location will once again be private. If you want to stop sharing your location before the time expires, return to the chat, tap "Stop Sharing, " and then "Stop Sharing" again to confirm. You can share your current location in any WhatsApp chat on Android. Before you get started, make sure your Android's location services are enabled. To do this, open your app list and tap the Settings icon, which looks like a gear or cog. Then, tap "Apps & Notifications, " select "Advanced, " tap "App permissions, " and then tap "Location." If the switch next to WhatsApp is turned off, tap it to turn it on. Now, close your Settings and open WhatsApp. Tap the "Chats" tab and select any individual or group chat. Then, tap the paperclip icon, select "Location, " and tap "Share live location." Select how long you want the people in the conversation to be able to see your location—after this time period expires, your location will go back to private. Last, tap "Send" to share your location. If you want to stop sharing your location before it expires automatically, return to the conversation, tap "Stop sharing, " and then tap "Stop" to confirm. To learn how to share your location on WhatsApp using an Android phone, keep reading!Comparison of Metabolites and Gut Microbes between Patients with Ulcerative Colitis and Healthy Individuals for an Integrative Medicine Approach to Ulcerative Colitis—A Pilot Observational Clinical Study (STROBE Compliant)
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Feature papers represent the most advanced research with significant potential for high impact in the field. A Feature Paper should be a substantial original Article that involves several techniques or approaches, provides an outlook for future research directions and describes possible research applications.
Apple Watch Series 8 Gps 45mm Midnight Aluminum Case With Midnight Sport Band
Editor’s Choice articles are based on recommendations by the scientific editors of journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to readers, or important in the respective research area. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.
Department of Translational Neurosciences, Pacific Neuroscience Institute and Saint John’s Cancer Institute at Providence Saint John’s Health Center, Santa Monica, CA 90404, USA
The increasing usage of smart wearable devices has made an impact not only on the lifestyle of the users, but also on biological research and personalized healthcare services. These devices, which carry different types of sensors, have emerged as personalized digital diagnostic tools. Data from such devices have enabled the prediction and detection of various physiological as well as psychological conditions and diseases. In this review, we have focused on the diagnostic applications of wrist-worn wearables to detect multiple diseases such as cardiovascular diseases, neurological disorders, fatty liver diseases, and metabolic disorders, including diabetes, sleep quality, and psychological illnesses. The fruitful usage of wearables requires fast and insightful data analysis, which is feasible through machine learning. In this review, we have also discussed various machine-learning applications and outcomes for wearable data analyses. Finally, we have discussed the current challenges with wearable usage and data, and the future perspectives of wearable devices as diagnostic tools for research and personalized healthcare domains.
Gps Aren't Just Exhausted: We Are Broken
Wearables, which refer to smart consumer devices that record digital health data, are becoming an integral part of our daily lives. This reflects the growing health consciousness among people. Wearable biosensors are low-price, non-invasive, and non-irritating devices that function by continuously measuring a person’s physiological parameters in real time [1, 2], which can be used for the early as well as in-depth diagnosis of several conditions. They also facilitate personalized patient health monitoring outside the clinical setting, which is an advantage considering the restricted movements during the COVID-19 pandemic [3, 4]. More than 500 health-related sensors are available in the market [1, 2, 3, 4, 5], and the sale of such devices has experienced more than a 20% annual growth rate, with an estimated market size of more than EUR 150 billion by 2028 [6]. Wearable devices are available in different forms that are in contact with different body parts, and are also available as devices attached with fabrics. Based on their point of contact, they can be categorized into head, limb (which includes arms), leg, eye, and torso wearable devices [7]. Based on their probing method, they can also be categorized as skin-based or biofluid-based [8]. Apart from consumer devices, wearable devices are available for specialized monitoring, such as wearable smart insoles for diabetic foot monitoring, devices for real-time heart attack detection, and smart-digital stethoscope systems [9], the use of which is often suggested by clinicians. In this review, we have primarily focused on skin-based wrist-wearable consumer devices that provide continuous data, which are used for the diagnosis of several disease conditions.
Wearable devices contain different types of sensors that collect data on step counts, heart rate, sleep duration, calories burnt, stress, and oxygen levels [10]. The parameters measured, collected, and stored by the device could be helpful in the identification of health conditions by detecting deviations from the corresponding baseline values. This technology can also be used to track and connect the user’s daily activities, such as cycling, running, biking, and walking, in combination with GPS, thus providing the location of the activity as well. These data can be affected by other external or environmental factors, such as temperature, altitude, and humidity, imposing additional dimensions on the data. These data are transferred to a cloud-based storage and are accessible by users’ mobile devices as well as to researchers and clinicians for precision diagnostics. To analyze this massive amount of multivariate time-series data, the conventional statistical approach is often inadequate, specifically in the context of making a diagnosis from the unseen data. In this case, machine-learning (ML) algorithms are beneficial and are being used to predict health events, intervention, and prevention [11, 12]. In this review, we discuss the use of various ML algorithms for the analysis of data provided by wearable devices.
Recent research highlights that wearable devices can work as digital diagnostic tools due to their usefulness in detecting several diseases. The remotely accessed, real-time-monitored, continuous data recording in a personalized manner has made wearables an effective tool for the diagnosis of physiological conditions. It has been found that the physiological parameters obtained from these biosensors can be used in the detection of Lyme disease [1], respiratory infections, cardiovascular disorders [13, 14], neurological disorders [15], coronavirus diseases [16, 17, 18], Parkinson’s disease [19], diabetes, liver diseases, and others. Not only can they detect physiological diseases, but wearables can also be employed to
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Wearables, which refer to smart consumer devices that record digital health data, are becoming an integral part of our daily lives. This reflects the growing health consciousness among people. Wearable biosensors are low-price, non-invasive, and non-irritating devices that function by continuously measuring a person’s physiological parameters in real time [1, 2], which can be used for the early as well as in-depth diagnosis of several conditions. They also facilitate personalized patient health monitoring outside the clinical setting, which is an advantage considering the restricted movements during the COVID-19 pandemic [3, 4]. More than 500 health-related sensors are available in the market [1, 2, 3, 4, 5], and the sale of such devices has experienced more than a 20% annual growth rate, with an estimated market size of more than EUR 150 billion by 2028 [6]. Wearable devices are available in different forms that are in contact with different body parts, and are also available as devices attached with fabrics. Based on their point of contact, they can be categorized into head, limb (which includes arms), leg, eye, and torso wearable devices [7]. Based on their probing method, they can also be categorized as skin-based or biofluid-based [8]. Apart from consumer devices, wearable devices are available for specialized monitoring, such as wearable smart insoles for diabetic foot monitoring, devices for real-time heart attack detection, and smart-digital stethoscope systems [9], the use of which is often suggested by clinicians. In this review, we have primarily focused on skin-based wrist-wearable consumer devices that provide continuous data, which are used for the diagnosis of several disease conditions.
Wearable devices contain different types of sensors that collect data on step counts, heart rate, sleep duration, calories burnt, stress, and oxygen levels [10]. The parameters measured, collected, and stored by the device could be helpful in the identification of health conditions by detecting deviations from the corresponding baseline values. This technology can also be used to track and connect the user’s daily activities, such as cycling, running, biking, and walking, in combination with GPS, thus providing the location of the activity as well. These data can be affected by other external or environmental factors, such as temperature, altitude, and humidity, imposing additional dimensions on the data. These data are transferred to a cloud-based storage and are accessible by users’ mobile devices as well as to researchers and clinicians for precision diagnostics. To analyze this massive amount of multivariate time-series data, the conventional statistical approach is often inadequate, specifically in the context of making a diagnosis from the unseen data. In this case, machine-learning (ML) algorithms are beneficial and are being used to predict health events, intervention, and prevention [11, 12]. In this review, we discuss the use of various ML algorithms for the analysis of data provided by wearable devices.
Recent research highlights that wearable devices can work as digital diagnostic tools due to their usefulness in detecting several diseases. The remotely accessed, real-time-monitored, continuous data recording in a personalized manner has made wearables an effective tool for the diagnosis of physiological conditions. It has been found that the physiological parameters obtained from these biosensors can be used in the detection of Lyme disease [1], respiratory infections, cardiovascular disorders [13, 14], neurological disorders [15], coronavirus diseases [16, 17, 18], Parkinson’s disease [19], diabetes, liver diseases, and others. Not only can they detect physiological diseases, but wearables can also be employed to
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