Activities data modeling in human internet-of-things platforms
A platform models and correlates physical activities based on users' interactions with a simple grip-metaphor design, enabling multi-dimensions actionable information to improve the health, performance and well-being of connected grip users within like-minded communities. For example, the platform captures multi-dimensional datasets generated from activities of each of a plurality of users on the online human internet of thing platform, where the activities include physical interactions with connected grips systems connected to the online human internet of thing platform. The platform then filters the captured multi-dimensional datasets into a plurality of categories and scores the filtered multi-dimensional data by the human internet of thing platform. Finally, the platform generates a multi-dimensional information modeling for each user based on the scored multi-dimensional data.
1. A method, comprising:
obtaining a video for a fitness-related activity as performed by a reference user, the video having a frame rate;
obtaining first data characterizing effort of the reference user for the fitness-related activity, wherein the first data are synchronized with the video;
playing the video to a non-reference user as the non-reference user performs the fitness-related activity; and
while playing the video to the non-reference user as the non-reference user performs the fitness-related activity:
capturing second data characterizing effort of the non-reference user for the fitness-related activity,
comparing the second data to the first data, to determine a difference between the effort of the non-reference user and the effort of the reference user, and
adjusting the frame rate of the video being played to the non-reference user based on the difference.
2. The method of claim 1 , wherein:
the comparing comprises determining that the non-reference user is slower than the reference user; and
the adjusting comprises reducing the frame rate in response to determining that the non-reference user is slower than the reference user.
3. The method of claim 1 , wherein:
the comparing comprises determining that the non-reference user is faster than the reference user; and
the adjusting comprises increasing the frame rate in response to determining that the non-reference user is faster than the reference user.
4. The method of claim 1 , further comprising repeatedly performing the capturing, the comparing, and the adjusting while playing the video to the non-reference user as the non-reference user performs the fitness-related activity.
5. The method of claim 1 , wherein:
the non-reference user performs the fitness-related activity using an item of fitness equipment that comprises a grip having embedded sensors; and
capturing the second data comprises capturing sensor data using the embedded sensors in the grip.
6. The method of claim 1 , wherein:
the non-reference user performs the fitness-related activity using an item of fitness equipment that comprises dual-handle grips having embedded sensors; and
capturing the second data comprises capturing sensor data using the embedded sensors in the dual-handle grips.
7. The method of claim 1 , wherein the video comprises video from multiple angles, the video from the multiple angles being synchronized with the first data.
8. The method of claim 7 , wherein playing the video comprises displaying the video on virtual-reality (VR) goggles worn by the non-reference user based on a direction in which the non-reference user faces.
9. The method of claim 8 , wherein:
the video from the multiple camera angles comprises forward video and backward video; and
displaying the video on the VR goggles comprises displaying the forward video when the non-reference user faces forward and displaying the backward video when the non-reference user faces backward.
10. The method of claim 1 , wherein the video comprises video from multiple angles and viewpoints, the video from the multiple angles and viewpoints being synchronized with the first data.
11. The method of claim 1 , wherein:
the first data are multi-dimensional with respective dimensions selected from the group consisting of motion data, physiological data, and environmental data; and
the second data are multi-dimensional with respective dimensions selected from the group consisting of motion data, physiological data, and environmental data.
12. The method of claim 1 , wherein:
obtaining the video comprises recording the video while the reference user performs the fitness-related activity using an item of fitness equipment that comprises a grip having embedded sensors; and
obtaining the first data comprises capturing data using the embedded sensors while recording the video.
13. The method of claim 1 , wherein:
obtaining the video comprises recording the video while the reference user performs the fitness-related activity using an item of fitness equipment that comprises dual-handle grips having embedded sensors; and
obtaining the first data comprises capturing data using the embedded sensors while recording the video.
14. The method of claim 1 , wherein the fitness-related activity is selected from the group consisting of an individual fitness exercise, a coach-driven session, a competitive event, and a team-based competitive event.