Invented by Colin P. Cornaby, Matthew M. Weaver, Digimarc Corp
In recent years, there has been a significant increase in the market for smartphone-based methods, systems, and applications for health monitoring. With the widespread adoption of smartphones and the advancements in technology, individuals now have the ability to monitor their health and well-being right from the palm of their hand.
One of the main reasons for the rapid growth in this market is the convenience and accessibility that smartphone-based health monitoring offers. Instead of having to visit a doctor or a healthcare facility for routine check-ups, individuals can now use their smartphones to track various health parameters such as heart rate, blood pressure, sleep patterns, and even mental health indicators.
The availability of numerous health monitoring apps and devices has made it easier for individuals to take control of their own health. These apps and devices are often equipped with sensors that can collect data in real-time, providing users with instant feedback and actionable insights. This allows individuals to make informed decisions about their lifestyle and make necessary changes to improve their overall well-being.
Furthermore, the integration of artificial intelligence and machine learning algorithms in smartphone-based health monitoring systems has significantly enhanced their capabilities. These algorithms can analyze large amounts of data and provide personalized recommendations based on individual health profiles. For example, if a person’s heart rate is consistently high during certain activities, the app can suggest alternative exercises that may be less strenuous.
The market for smartphone-based health monitoring is not only growing in terms of consumer demand but also in terms of industry investment. Major players in the technology and healthcare sectors are recognizing the potential of this market and are investing heavily in research and development. This has led to the development of more advanced and accurate sensors, as well as the integration of additional features such as ECG monitoring and blood glucose tracking.
The COVID-19 pandemic has further accelerated the growth of this market. With the need for remote healthcare solutions, smartphone-based health monitoring has become a crucial tool in monitoring symptoms and providing virtual consultations. This has led to an increased adoption of telehealth services and a greater reliance on smartphone-based health monitoring systems.
However, despite the numerous benefits and opportunities that smartphone-based health monitoring presents, there are also challenges that need to be addressed. Privacy and security concerns are at the forefront, as the collection and storage of sensitive health data raise concerns about data breaches and unauthorized access. Additionally, the accuracy and reliability of some smartphone-based health monitoring devices and apps have been questioned, highlighting the need for regulatory oversight and standardization.
In conclusion, the market for smartphone-based methods, systems, and applications for health monitoring is rapidly growing. The convenience, accessibility, and advancements in technology have made it easier for individuals to monitor their health and make informed decisions about their well-being. With continued investment and advancements in this field, smartphone-based health monitoring has the potential to revolutionize healthcare and empower individuals to take control of their own health. However, it is important to address privacy concerns and ensure the accuracy and reliability of these systems to fully realize their potential.
The Digimarc Corp invention works as follows
The present disclosure is aimed at consumer electronics devices with sensors, including smartphones and tablets. One implementation is aimed at a smartphone that operates in an image-discovery mode. The results of the image discovery mode can be displayed on a smartphone’s display screen. The smartphone can simultaneously operate an audio-discovery mode. The results of the audio discovery are saved in a discovery history but not displayed while the image discovery is active. There are also many other implementations and combinations.
Background for Smartphone-based methods, systems
The present technology expands in certain respects upon technology detailed in the above-detailed patent application. These previous works can be used to implement the present technology and can be incorporated into the current technology.
Referring to FIG. 1. An illustrative device 14 includes a processor 16, memory 18, one or several input peripherals 20 and one or two output peripherals 22. System 12 can also contain a network connection 24 and remote computers 26.
An illustrative device 14 can be a smartphone or tablet computer. However, any other electronic device that is consumer-grade can be used. The processor may include a microprocessor, such as an Atom device or A4 device. The operating system software and application software stored in the memory control part of the processor’s operation. ), data, etc. A hard drive or flash memory could be used as memory.
Input peripherals 20 could include a camera or a microphone. An interface system that converts analog signals from the camera/microphone into digital data is also possible. You can also use a touch screen or keyboard as an input peripheral. Output peripherals 22 include a speaker, display screen, and so on.
The 24th network connection can be wired (e.g. Ethernet, etc. ), wireless (WiFi, 4G, Bluetooth, etc. Or both.
In an exemplary operation device 14 receives a set digital content data through a microphone 20. The interface can be connected through the network connection 24 or any other means. You can use any content data; audio is an example.
The system 12 processes digital content data to create corresponding identification data. This can be done by using a digital watermark process or a fingerprinting algorithm. data (e.g., file names, header data, etc.). This data is used to identify the content data received from other data (e.g. other audio or video).
By referencing this identification data the system determines which software should be invoked. Indexing a table, database or other data structure with this identification data is one way to accomplish this. This will allow you to identify the correct software. FIG. 2 shows an illustration of a table. 2.
In certain cases, the data structure might identify a single program. If this is the case, the software will be launched. The software does not need to be installed on the device. Apps that are cloud-based may be available. If the software is not available, it can be downloaded from an online repository such as the iTunes Store, installed, and launched. The device can also subscribe to the software-as-service version of the app. Depending on the implementation, the user may be asked permission to participate in certain actions. In other cases, such actions are carried out without disturbing the user.
Sometimes, the data structure can identify multiple software programs. Different programs might be specific to certain platforms. In this case, device 12 could simply choose the program that corresponds to that platform (e.g. Android G2, iPhone 4, etc.). The data structure might identify other programs that are compatible with a particular platform. The device might check this situation to see if there are any already installed. It can launch the program if it is found. The device can choose between two programs if it finds them. The device might prompt the user to choose one or both. The device can choose to download an alternative program if none are available. This is done using an algorithm or user input. The application is launched once it has been downloaded and installed.
(Sometimes, the data structure might identify different programs that serve various functions?all of which are related to the content. One app could be used to find lyrics. An app that relates to the biography of a musician could be another. An app that allows you to purchase the content could be another option. Each type of software can include multiple alternatives.
Note: The device may have an already installed application that is technically suitable to work with the received content (e.g. to render an MPEG4 file or an MP3 file). There may be many or more programs that are technically compatible with certain operations. The content might indicate that only a small subset of the possible software programs should be used.
Software in the device 14 could enforce content-identified software selection. The system could also treat software identification as a preference that can be overridden by the user. In some cases, the user might be given an incentive to use content-identified software. Alternately, the user might be charged a fee or other impediment to use any software not identified by the content.
Sometimes, the system might not render certain content on a particular device (e.g. because there is no suitable app or hardware capability), but it may invite the user transfer the content to another device that has the required capability and may implement such transfer. Ansel Adams may have been sceptical of large format photographs being used as screen savers on a low resolution smartphone display. The software might suggest that the user instead of trying to display the images on a small format, low resolution smartphone display, it will ask the user to transfer the images to a larger format HD display at home.
The system might render the content in a limited manner, instead of rendering it completely. A video could be rendered in a series or still frames, such as from scene transitions. The system can also transfer the content to a place where it can be enjoyed more effectively. If hardware considerations allow (e.g. screen resolution is sufficient),?the software can be downloaded and installed.
As illustrated by the table in FIG. “As shown in FIG. 2 (which data structure might be located in the memory 18 or in a remote computing system 26, the indication of software could be based upon one or more contextual elements?in addition to content identification data.
Context” is a formal definition. “Context” is any information that can be used as a way to describe an entity’s situation (a person, place, or object that is relevant to the interaction between an application and a user), including applications themselves.
Computing context can include network connectivity, memory availability and CPU contention. User context (profile, location, actions and preferences, friends nearby, social networks, situation, etc.) Physical context (e.g. lighting, noise levels, traffic, heard sounds, recognized voices, etc. Temporal context (time, day, month and season), history of the above, etc. History of the above etc.
The taxonomy for contexts is a progression from simple to complex. It starts with location and then moves on to physical contexts (such as temperature, motion, 3D ambient sound, infrared and video), user or device actions, proximity (e.g. to people, buildings, boundaries, jurisdictions or other devices), somatics (e.g. live datastreams containing biometric data), data feeds (e.g. RSS feeds, notifications and updates from social networks, etc.), and
In the illustrated tableau, rows 32-34 correspond to the same content (i.e. the same content ID), but they indicate that different software should be used. Depending on whether the context is indoors and outdoors. The software is identified with a five symbol hex identifier, while the content is identified using six hex symbols. Other identifiers, with a longer or shorter length, may be used.
Row 36 shows two software items?both of these are invoked. One includes a second descriptor?an identifier for a YouTube video that will be loaded by software FF245. This software is intended for users in a daytime context and those aged between 20-25.
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