Abstract: A classification model is generated based on historical exercise information. User exercise information is classified into an exercise category using the classification model. Recommendations based on the exercise category is identified. A customized exercise recommendation is determined from the identified recommendations based on a comparison of the user exercise information and expected progress data. This customized recommendation is provided to a user device for consumption.

Abstract: A classification model is generated based on historical exercise information. User exercise information is classified into an exercise category using the classification model. Recommendations based on the exercise category is identified. A customized exercise recommendation is determined from the identified recommendations based on a comparison of the user exercise information and expected progress data. This customized recommendation is provided to a user device for consumption.

Abstract: Embodiments herein provide systems, methods, and computer-readable medium for providing customized exercise-related recommendations. Utilizing machine learning algorithms, a classification model may be trained with fitness-related information (e.g., exercise information, user profile information, and/or vital sign information) of a group of users. The classification model may be configured to output a classification for input data (e.g., fitness-related information of a particular user). A recommendation corresponding to a classification may be identified and provided to a particular user. User compliance with provided recommendations and subsequent user progress may be tracked to determine when recommendations were effective at bringing about a desired result (e.g., progressing the user toward a goal). Additionally, the system may determine when classifications have been inaccurately determined and/or when expected progress data has not provided a realistic path by which a user may progress toward a goal.

Abstract: Embodiments herein provide systems, methods, and computer-readable medium for providing customized exercise-related recommendations. Utilizing machine learning algorithms, a classification model may be trained with fitness-related information (e.g., exercise information, user profile information, and/or vital sign information) of a group of users. The classification model may be configured to output a classification for input data (e.g., fitness-related information of a particular user). A recommendation corresponding to a classification may be identified and provided to a particular user. User compliance with provided recommendations and subsequent user progress may be tracked to determine when recommendations were effective at bringing about a desired result (e.g., progressing the user toward a goal). Additionally, the system may determine when classifications have been inaccurately determined and/or when expected progress data has not provided a realistic path by which a user may progress toward a goal.

Abstract: Methods and systems for displaying data are described. The display system extracts first data from a first data set, such as management system data. The first data includes one or more variables between a start time and an end time that may be user-defined. The display system uses the extracted data to populate a temporary database and process the data in the temporary database, such that there is no need to unduly interfere with the functionality of the management system. The data in the temporary database is processed to generate a plot value for each variable for each of a plurality of time slices. Finally, the plot values are presented as a series of images using a video processor to generate an intuitive visual representation of the data.

Abstract: A system is described that generates reports from very large data sets. The reports are generated in real-time (or close to real time). Data from the large data set is replicated to a buffer as it arrives in the system. Once sufficient data is obtained (e.g. when the buffer is filled), the data is processed to generate a report. The report may summarize the data obtained and may be stored for later use. By storing summary data instead of the full data, the data storage requirements are reduced.

Abstract: A method and system is provided in which new types of devices (such as sensors) can be integrated into a network in an automated manner. The system includes a repository for device information. When a new device is detected in a network, a management system for the network obtains device information from the repository and uses that information to integrate the device into the network.

Abstract: A method and system is provided in which new types of devices (such as sensors) can be integrated into a network in an automated manner. The system includes a repository for device information. When a new device is detected in a network, a management system for the network obtains device information from the repository and uses that information to integrate the device into the network.

Abstract: A system is described that generates reports from very large data sets. The reports are generated in real-time (or close to real time). Data from the large data set is replicated to a buffer as it arrives in the system. Once sufficient data is obtained (e.g. when the buffer is filled), the data is processed to generate a report. The report may summarize the data obtained and may be stored for later use. By storing summary data instead of the full data, the data storage requirements are reduced.

Abstract: Methods and systems for displaying data are described. The display system extracts first data from a first data set, such as management system data. The first data includes one or more variables between a start time and an end time that may be user-defined. The display system uses the extracted data to populate a temporary database and process the data in the temporary database, such that there is no need to unduly interfere with the functionality of the management system. The data in the temporary database is processed to generate a plot value for each variable for each of a plurality of time slices. Finally, the plot values are presented as a series of images using a video processor to generate an intuitive visual representation of the data.