K-Ary Delta Encoding for Low-Power Physiological Monitors
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Solution Overview
Problem
Remote physiological monitors face challenges with short battery life and limited storage capacity due to the large amounts of data generated by multiple sensors, leading to frequent battery replacements and reduced ability to store data without compression, which is computationally intensive and drains battery power.
Innovation Solution
Implementing low-power encoding techniques such as delta transition (DDT) encoding that achieve nearly two-fold data compression rates with minimal energy consumption, using k-ary trees to encode deltas between successive signal samples, reducing the need for frequent battery replacements and extending device runtime.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple sensors are sampled several times per second to capture physiological data, then measurement precision and data quality are improved, but the quantity of data generated increases significantly, exceeding the capacity of small storage devices
Solution Approach 1:
The patent extracts only the essential information from raw physiological signals by identifying and recording key events (such as R-peaks in ECG, breath peaks in PPG) and their temporal relationships, rather than storing all raw sensor data. This selective extraction reduces data volume while preserving clinically relevant information.
Solution Approach 2:
The patent transforms physiological signals from continuous time-domain waveforms into discrete event-based representations with specific parameters (event type, timestamp, amplitude, inter-event intervals). This parameter transformation compresses data by representing complex waveforms with simplified characteristic values.
2Quantity of substance
If lossless compression techniques are used to reduce local storage requirements, then storage capacity is improved, but computational complexity increases, causing increased battery drain
Solution Approach 1:
The patent segments physiological signals into discrete, identifiable events (beats, breaths, motion episodes) rather than processing continuous streams. Each event is independently characterized and stored with minimal metadata, enabling simple compression without requiring complex global optimization algorithms.
Solution Approach 2:
The patent creates simplified copies of physiological signals in the form of event markers and temporal patterns that capture the essential characteristics without replicating the full signal complexity. These event-based representations serve as efficient proxies for the original high-volume data.
3Reliability
If continuous data acquisition is maintained to ensure no physiological events are missed, then measurement completeness is improved, but energy consumption increases, requiring frequent battery replacements
Solution Approach 1:
The patent implements periodic sampling at low rates for multiple sensors simultaneously, rather than continuous high-rate sampling of individual sensors. The system checks multiple physiological parameters at intervals, ensuring that significant events are captured while minimizing overall power consumption during idle periods.
Solution Approach 2:
The system uses motion sensors and signal analysis to automatically detect periods of low activity or sleep states, then reduces sampling rates or enters low-power modes during these periods. The device self-adjusts its power consumption based on the physiological state being monitored, maintaining reliability while conserving energy.
Data Source
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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 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.