Traversal Vector Encoding for Wearable Logic Compression
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Wearable devices face limitations in implementing complex gesture recognition and activity classification due to resource constraints, as current compression algorithms lead to exponential increases in storage complexity, preventing scalable combination of logical outputs.
Innovation Solution
A method for encoding and decoding a one-dimensional traversal vector is introduced, where a block diagram is converted into a chain equation, then encoded into a traversal vector, and decoded using a stack-based approach, reducing memory requirements by scaling linearly with the number of operands and operators.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple decision trees are implemented to increase gesture complexity, then gesture recognition capability is improved, but storage complexity increases exponentially
Solution Approach 1:
The patent implements nested decision trees where intermediate outputs from one decision tree become inputs to another decision tree. This nesting approach allows complex gesture recognition to be achieved by combining multiple decision trees sequentially, reducing the exponential storage complexity that would result from implementing all combinations simultaneously. The nested structure enables scalable complexity management.
Solution Approach 2:
The patent segments the gesture recognition system into multiple independent decision trees, each handling specific aspects of gesture classification. By dividing the complex recognition task into smaller, manageable segments that can be combined through logical operations, the system achieves high gesture recognition capability while maintaining linear rather than exponential storage requirements.
2Adaptability or versatility
If compression algorithms are applied to combine logical outputs, then solution diversity is improved, but storage complexity increases exponentially
Solution Approach 1:
The patent performs preliminary compression of decision tree outputs using lookup tables that store pre-computed intermediate results. By pre-compressing and storing only the essential intermediate outputs rather than all possible combinations, the system maintains solution diversity while avoiding exponential storage complexity. The lookup tables enable efficient combination of logical outputs with linear storage requirements.
3Quantity of substance
If large decision trees are compressed using existing algorithms, then memory usage is reduced, but the inherent if-then-else structure limits solution complexity
Solution Approach 1:
The patent introduces intermediate lookup tables as mediators between the compressed decision tree structures and the final classification outputs. These lookup tables enable efficient memory usage by storing only critical intermediate results, while simultaneously allowing complex solution combinations through the nested decision tree architecture. The intermediary structure breaks the limiting if-then-else constraint by enabling direct access to intermediate outputs for combination.
Data Source
AI summary
Devices and methods are provided that facilitate encoding and decoding of a one dimensional traversal vector for use in a wearable device. An ensemble network of nodes and node groups and combinatorial logic operations to be applied to them is provided by the customer, for example, via a graphical user interface. The ensemble is related to the operation of the wearable device. Off-line, the ensemble is converted into a chain equation and the chain equation is encoded into a one dimensional traversal vector. The traversal vector is then deployed to a chip on a wearable device where it is decoded to process real-time data.