Activity Template Transfer for Smart Environment Setup
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Solution Overview
Problem
The establishment of new smart environments is hindered by a time-intensive and computationally costly learning process required to recognize activities based on sensor data, as each environment's unique layout and resident dynamics necessitate extensive manual data labeling and sensor mapping.
Innovation Solution
Transfer learning is employed by generating activity templates from existing smart environments and mapping them to new environments, leveraging known correlations in sensor data to recognize activities without extensive manual annotation or sensor mapping, using machine learning models like naïve Bayes classifiers, hidden Markov models, and conditional random fields, and combining data from multiple environments to improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual data labeling and sensor mapping are performed for each new smart environment, then activity recognition accuracy is improved, but setup time and computational effort increase significantly
Solution Approach 1:
The patent creates activity templates from sensor data in existing smart environments and copies these templates to new environments. Instead of manually relabeling data for each new environment, the system replicates proven activity patterns from source environments, significantly reducing setup time while maintaining recognition accuracy through template matching and adaptation.
Solution Approach 2:
The system performs preliminary learning and template generation in existing smart environments before deploying to new environments. By pre-computing activity templates and their associated sensor patterns in advance, the system eliminates the need for time-consuming manual labeling during the initial setup of new environments, directly addressing the time loss problem.
2Reliability
If sensor data is manually labeled and processed for each new smart environment, then activity recognition reliability is improved, but computational effort and setup complexity increase
Solution Approach 1:
The patent copies activity templates and their associated sensor patterns from existing environments to new environments. This copying approach maintains reliability by transferring proven activity recognition models while reducing complexity, as the templates are pre-processed and ready for direct application without requiring complex manual setup procedures.
Solution Approach 2:
The system segments the complex task of activity recognition into discrete activity templates, each representing a specific activity pattern with its associated sensor data. This segmentation allows the system to handle complexity by processing and transferring individual template units rather than managing the entire complex sensor environment at once, simplifying the setup process while maintaining recognition reliability.
3Loss of time
If activity templates are generated and mapped from existing environments, then setup time is reduced, but the system must handle varying layouts and resident dynamics
Solution Approach 1:
The patent employs dynamic adaptation mechanisms that allow activity templates to be adjusted when transferred to new environments with different layouts. The system dynamically modifies template parameters, sensor mappings, and activity patterns to accommodate variations in floor plans, sensor placements, and resident behaviors, maintaining both time efficiency and adaptability.
Solution Approach 2:
The system changes key parameters of activity templates when transferring them to new environments. By adjusting parameters such as spatial coordinates, sensor thresholds, and temporal patterns, the system adapts templates to varying layouts and resident dynamics while preserving the core activity recognition functionality, thus reducing setup time without sacrificing adaptability.
Data Source
AI summary
Activity templates are generated from one or more existing smart environments (e.g., source spaces) based on sensor data from the one or more existing smart environments that corresponds to known activities. A target activity template is then generated for a new smart environment, e.g., the target space. The source space activity templates are then mapped to the target activity templates to enable recognition of activities based on sensor data received from the target space.