Wearable Receiver Sensing for Wandering-Frequency Ingestible Sensors
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Solution Overview
Problem
Wearable receiver assemblies face challenges in analyzing data from ingestible event markers (IEMs) and physiological metrics due to limited resources and high performance requirements, particularly in detecting high-frequency in-body electric signals and high-resolution physiological data like electrocardiogram (ECG) data.
Innovation Solution
A wearable device with a processor and algorithms that process discontinuous data frames of ingestible sensor and physiological data interspersed in time gaps, optimizing resource usage and enhancing accuracy through parallel and serial data processing, using a combination of local and remote device processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If continuous high-resolution physiological data is collected and processed, then measurement precision is improved, but use of energy increases
Solution Approach 1:
The patent segments continuous physiological data collection into discontinuous frames separated by time gaps. The receiver collects physiological data in discrete frames rather than continuously, reducing energy consumption while maintaining measurement precision through strategic sampling of high-resolution data at critical moments.
Solution Approach 2:
The system implements periodic data collection with regular time gaps between frames. This periodic action pattern allows the receiver to alternate between active data collection and low-power idle states, achieving energy-efficient operation while preserving the ability to process high-resolution physiological metrics when needed.
2Measurement precision
If high-frequency in-body electric signals are detected, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent segments the detection task by separating IEM signal detection from physiological data processing into distinct operational phases. The receiver detects IEM signals during specific time windows between physiological data frames, reducing the complexity of simultaneous processing while maintaining high-frequency detection capability.
Solution Approach 2:
The system uses time gaps between data frames as intermediaries to separate different detection functions. During these time gaps, the receiver can focus on IEM detection without the complexity of simultaneous physiological processing, using the temporal separation as a mediator to simplify the overall system architecture.
3Use of energy by moving object
If discontinuous data with time gaps is processed, then use of energy is reduced, but measurement precision deteriorates
Solution Approach 1:
The patent changes the temporal parameter of data collection from continuous to discontinuous with controlled time gaps. By adjusting the size and frequency of these time gaps, the system optimizes the balance between energy consumption and measurement precision, ensuring that physiological metrics remain accurate despite the interrupted data stream.
Solution Approach 2:
The system implements feedback mechanisms that monitor the quality of discontinuous physiological data frames. When frames are received, the processor evaluates their quality and adjusts subsequent data collection timing accordingly, using feedback to maintain measurement precision while maximizing energy savings from the discontinuous collection pattern.
Data Source
AI summary
A system, a wearable device, and a method are provided which can increase the accuracy of physiological metrics while detecting if the patient ingested digital medicine and/or improve performance of the wearable device. The wearable device can comprise machine executable instructions that when executed by the processor, cause the processor to perform various algorithms, such as, for example, at least one of a step count algorithm, a body angle algorithm, a heart rate algorithm, a peak finder algorithm, an adaptive thresholding algorithm, a heart rate variability algorithm, a R-R cleaning Algorithm, a deltaR-R cleaning algorithm, a merge twin interval algorithm, a split tall intervals algorithm, an absorb short intervals algorithm, a bimodal detection algorithm, and a resting algorithm.


