Physiological Signal Delta Encoding for Low-Power Remote Monitors
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Remote physiological monitors face challenges with short battery life and limited storage capacity due to the large amount of data generated by multiple sensors, leading to frequent battery recharging and reduced ability to store data without compression, which is often computationally intensive and power-consuming.
Innovation Solution
A system and method for low-power encoding of continuous physiological signals using a k-ary tree structure, where delta values are encoded based on node indices and depths, allowing for efficient storage and compression with minimal energy consumption, including the use of delta tokens and a processor programmed to determine the appropriate encoding based on delta values and tree architecture.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data is captured from multiple sensors at high sampling rates, then measurement precision and data quality are improved, but the quantity of data generated increases significantly
Solution Approach 1:
The patent extracts only the essential information from raw sensor data by computing delta values (differences between consecutive samples) and encoding them using a k-ary tree structure. This extraction process removes redundant information while preserving the critical physiological signal characteristics, achieving compression without significant loss of measurement precision.
Solution Approach 2:
The patent transforms the data representation by changing from absolute sample values to delta values (differences between consecutive samples). This parameter transformation reduces the effective data range and enables more efficient encoding, as delta values typically have a smaller magnitude and higher predictability than raw physiological signals.
2Quantity of substance
If lossless compression techniques are used to reduce local storage requirements, then storage capacity is improved, but computational intensity and power consumption increase
Solution Approach 1:
The patent segments the compression process into two distinct stages: (1) a low-power encoding stage performed locally on the device using simple delta computation and k-ary tree traversal, and (2) a more intensive decoding stage performed on a connected computing device. This segmentation allows the mobile device to use minimal power while still achieving lossless compression.
Solution Approach 2:
The patent introduces an intermediary encoding scheme (delta values with k-ary tree indices) that serves as a compact representation bridging the raw data and the final compressed form. This intermediary representation requires minimal processing power to generate but enables efficient storage and later lossless reconstruction using the stored index sequence.
3Duration of action of moving object
If battery capacity is increased to extend runtime, then duration of action is improved, but device size and weight increase
Solution Approach 1:
The patent converts the limitation of small battery capacity into a benefit by implementing an encoding system that reduces the total data volume stored. The harm of limited power is transformed into an advantage through efficient data representation, where the small battery becomes sufficient for extended monitoring periods because less power is consumed for data storage operations.
4Measurement precision
If sampling rate is increased to capture more physiological detail, then measurement precision is improved, but data generation rate and storage requirements increase
Solution Approach 1:
The patent performs preliminary processing of the high-rate sensor data by computing delta values and encoding them into the k-ary tree structure in real-time as data is acquired. This preliminary action reduces the data volume before it needs to be stored or transmitted, preventing the accumulation of large data volumes even at high sampling rates.
Data Source
AI summary
In accordance with some embodiments of the disclosed subject matter, mechanisms (which can, for example, include systems, methods, and media) for low-power encoding of continuous physiological signals are provided. In some embodiments, a system comprises: a physiological sensor; and a remote monitor comprising: a battery; memory storing a k-ary tree including a root with k branches corresponding to k delta values, k nodes at a first depth below the Leads root node each having k branches corresponding to the k delta values the nodes indexed to indicate the lateral position of the node within the depth; a processor programmed to: receive a first sample value from the sensor; receive a second sample value; calculate a difference between the second first sample values; determine that the delta corresponds to a first delta of the k delta values; encode a sequence of deltas based on a depth and node index.