User Activity Heat Maps for Predictive Home Appliance Control
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Solution Overview
Problem
Conventional methods for controlling home appliances based on user location are limited by the user's immediate location, failing to account for the user's activity radius and space, leading to inefficient appliance control.
Innovation Solution
An electronic device estimates a user's activity space using location data through a millimeter wave sensor, generating a heat map and clustering data to determine the user's activity radius, allowing for optimal appliance control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional sensors are installed in large space to collect user location data, then user location can be determined, but the user's location can only be determined where the sensor is directly installed, limiting the activity space estimation
Solution Approach 1:
The patent introduces an intermediary computational model (heat map generation algorithm) that mediates between the limited sensor data and the desired comprehensive activity space estimation. The heat map acts as an intermediary representation that propagates location probability beyond direct sensor coverage areas, allowing estimation of user activity in spaces where sensors are not directly installed.
Solution Approach 2:
The patent transitions from direct spatial measurement (2D sensor coverage) to a probabilistic dimension (heat map intensity values representing location probability). By adding the dimension of probability density, the system can estimate user activity in areas beyond direct sensor line-of-sight, effectively expanding the measurable space through mathematical transformation.
2Ease of operation
If home appliance control is limited to user's immediate location, then control can be implemented, but efficient control of home appliances is limited
Solution Approach 1:
The patent performs preliminary actions by pre-calculating and storing heat maps representing user activity patterns in advance. These pre-computed activity spaces enable the system to proactively control home appliances before the user actually arrives at a location, improving both convenience and efficiency by eliminating real-time computation delays.
Solution Approach 2:
The patent implements feedback mechanisms where user location data continuously updates the heat map, which in turn refines appliance control decisions. The system monitors user movement patterns, adjusts activity space estimates based on accumulated data, and optimizes appliance control strategies iteratively, creating a closed-loop system that improves efficiency over time.
3Loss of information
If heat map is generated based on cumulative location data set, then user activity space can be estimated, but energy efficiency of home appliances requires optimal control based on activity space
Solution Approach 1:
The patent applies local quality by controlling home appliances differently in different regions of the activity space. Instead of uniform control, the system adjusts appliance operation based on the heat map intensity at specific locations, optimizing energy consumption by activating appliances only in high-probability user presence areas while maintaining comfort in those zones.
Data Source
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AI summary
An electronic device according to an embodiment of the present disclosure may comprise a memory configured to store location data of a user; and at least one of processors configured to: obtain a cumulative location data set based on the location data, generate a heat map representing a location distribution of the user based on the cumulative location data set, and obtain an activity space of the user based on the generated heat map.