Wearable Video Recorder for Automated Time Entry Creation
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Solution Overview
Problem
Current methods lack the ability to automatically determine a user's activity and create corresponding time entries, which is essential for industries where time tracking is crucial and for personal scheduling.
Innovation Solution
A system utilizing a machine learning model and computer vision, where a video recorder mounted on a wearable item captures the user's surroundings, identifies objects and people, and determines the activity and duration, automatically creating time entries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual time tracking methods are used, then users can record time entries, but the process is time-consuming and prone to human error
Solution Approach 1:
The system enables automatic self-service time tracking by using video recording and computer vision to autonomously capture user activities and generate time entries without manual intervention. The video recorder continuously records the user's field of view, and the processor automatically analyzes the video content to identify activities and create corresponding time entries.
Solution Approach 2:
The patent replaces manual mechanical time entry processes with automated computer vision and machine learning systems. Instead of manually recording time in spreadsheets or time tracking software, the system uses AI algorithms to automatically detect activities from video footage and generate time entries programmatically.
2Extent of automation
If automated activity detection is implemented, then time entries can be created automatically, but system complexity increases
Solution Approach 1:
The video recorder serves multiple functions: it records video for activity detection, captures images for object identification, and provides visual data for both activity classification and duration calculation. The processor performs multiple tasks including object detection, activity recognition, time calculation, and time entry generation, making the system multi-functional despite the additional complexity.
3Measurement precision
If video recording is used to capture user surroundings, then activity detection accuracy improves, but data processing requirements increase
Solution Approach 1:
The system performs preliminary video recording continuously in the background before actual activity detection is needed. This allows the video data to be pre-captured and buffered, enabling the processor to analyze pre-existing video content rather than capturing and processing video in real-time, thereby reducing peak computational energy demands.
Data Source
AI summary
Presented here are systems and methods to automatically determine an activity in which a person is engaged and to automatically create time entries based on the determination. The determination can be made using a machine learning model and/or computer vision. In one embodiment, video recorder can be mounted on an item worn by the user, such as glasses. The video recorder can record a video of the environment surrounding the user from the user's point of view, and a processor can identify objects and/or people within the video. Based on the identified objects and/or people, the processor can determine the activity in which the user is engaged as well as a duration of the activity. The processor can automatically create a time entry including the activity as well as the duration of the activity.