Wirelessly networked systems of implantable and non-implantable medical devices with networking protocols, software, and hardware that allow for communications and energy transfer between different the medical devices (free standing, implants and wearables) using ultrasonic waves. The networks and methods of use are used to construct cardiac pacing, deep brain stimulation, and neurostimulation networks based on ultrasonic wide band technology.

A smart mirror can show live or recorded streaming video of an instructor performing a workout in a package that is attractive and unobtrusive enough to hang in a living room. The smart mirror includes a mirror surface with a fully reflecting section and a partially reflecting section. A display behind the partially reflecting section shows the video when the smart mirror is on and is almost invisible when the smart mirror is off. The smart mirror also has a speaker, a microphone, and a camera to enable a user to view the video content and interact with the instructor. The smart mirror may connect to the user's smart phone, a peripheral device (e.g., a Bluetooth speaker) to augment user experience, a biometric sensor to provide biometric data to assess user performance, and/or a network router to connect the smart mirror to a content provider, an instructor, and/or other users.

A system provides communications between local device installations and a remote monitoring computer and includes a plurality of computerized devices communicating on a bi-directional network. At least one local device transmits local device data to a remote monitoring computer on the bi-directional communications network. An interface computer receives the local device data and formats the local device data for further transmission to the remote monitoring computer, wherein the interface computer has at least one processor and computer memory storing interface software that implements a method of translating data of any kind into a preferred format. The method includes storing the local device data in the computer memory of the interface computer; transforming the local device data into a formatted data set; and transmitting the formatted data set to at least one remote monitoring computer.

In an approach for monitoring data usage in an IoT network, a processor identifies a set of IoT devices. A processor collects a set of baseline readings for the set of IoT devices. A processor collects a set of IoT data from the set of IoT devices as a user answers a survey question. A processor compares the set of baseline readings to the set of IoT data. A processor derives an emotional state of the user while answering the survey question. A processor builds a CART model using the set of baseline readings, the set of IoT data, and a set of survey data. A processor applies the emotional state as a weight to the CART model. A processor outputs the weighted CART model.

In an approach for monitoring data usage in an IoT network, a processor identifies a set of IoT devices. A processor collects a set of baseline readings for the set of IoT devices. A processor collects a set of IoT data from the set of IoT devices as a user answers a survey question. A processor compares the set of baseline readings to the set of IoT data. A processor derives an emotional state of the user while answering the survey question. A processor builds a CART model using the set of baseline readings, the set of IoT data, and a set of survey data. A processor applies the emotional state as a weight to the CART model. A processor outputs the weighted CART model.

Systems and methods for increasing a user's task performance and personal wellbeing resulting from caring for indoor plants are disclosed. Internet-of-Things sensors and devices monitor measurements relating to environmental factors and a user's care patterns associated with designated indoor plants. User-performance sensors and devices measure the user's wellbeing and the user's task performance, both in caring for the plants and in other productivity-driven tasks. Recommendations for the plant care and prescriptions for the users are generated using machine-learning models and provided to the user through notifications to alter the user's behavior in a desirable way, both in plant care and in real-world task performance.

The present disclosure provides a preset management method of a gas pipeline network for a smart gas, which is performed by a smart gas management platform. The smart gas management platform comprises a smart user service management sub-platform, a smart operation management sub-platform and a smart gas data center. The method comprises: obtaining, by the smart gas data center, a region feature of each region within a target range through a smart gas sensing network platform; determining, by the smart operation management sub-platform, a gas demand degree of the each region based on the region feature of the each region; determining, by the smart operation management sub-platform, a region as a first-class region based on the gas demand degree in the region meeting a preset condition, and determining a gas pipeline network opening scheme for the region.

Provided is a learning support system which can enhance a learning effect by sharing a matter of interest among learners viewing a learning content. The learning support system includes: a learning content display unit configured to play and display a teaching material video; a learner information reporting unit configured to acquire biological information of a learner during play of the teaching material video and report the biological information as learner information; a region-of-attention identification unit configured to identify a region-of-attention of the learner based on the biological information; a learner information display creation unit configured to generate a screen in which the region-of-attention is superimposed on the teaching material video of another learner belonging to the same cluster as the learner; and a learner information transmission unit configured to transmit the screen to the another learner.

According to certain embodiments, an electronic device comprises a communication module; a plurality of sensors and configured to obtain sensing data; at least one processor operatively connected to the plurality of sensors and the communication module; and a memory operatively connected to the at least one processor, wherein the memory stores instructions that, when executed, cause the at least one processor to perform a plurality of operations comprising: transmitting the sensing data to a server through the communication module; receiving, from the server, information on a similarity between the sensing data and a first cluster among a plurality of clusters clustering data related to user activities, through the communication module, wherein the similarity is identified based on a center similarity score between the sensing data and the first cluster, a score that is a function of a variance of the first cluster, a score that is a function a distance between the first cluster and other clusters, and an intersection score between the first cluster and a second cluster adjacent to the first cluster; and executing a function corresponding to the sensing data based on the similarity.