Wearable Sensor Management via Context-Aware Opportunistic Sampling
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Solution Overview
Problem
Wearable sensing systems for physiological monitoring are power-hungry and expensive due to the need for multiple sensors and continuous data sampling, leading to short operating lifetimes.
Innovation Solution
A sensor system that uses contextual and semantic properties of human behavior to identify specific channels for sensing and implement a sensing policy, reducing data sampling while maintaining signal integrity through techniques like signal time-shifting, segmentation, and opportunistic sampling, which selects a small subset of sensors to predict all possible signals, thereby minimizing energy consumption and cost.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple sensors are used for comprehensive physiological monitoring, then measurement precision is improved, but device complexity and energy consumption increase
Solution Approach 1:
The patent extracts and transmits only the most critical physiological parameters (such as heart rate variability, respiration rate, and activity level) from the comprehensive sensor array, rather than transmitting all sensor data. This selective extraction reduces data volume and energy consumption while maintaining the essential monitoring functionality.
Solution Approach 2:
The system employs machine learning models that can infer multiple physiological states from a limited set of sensor readings. A single sensor measurement can serve multiple monitoring purposes through predictive modeling, reducing the need for dedicated sensors for each physiological parameter.
2Measurement precision
If continuous data sampling is performed to maintain signal integrity, then measurement precision is improved, but energy consumption increases
Solution Approach 1:
Instead of continuous sampling, the system implements periodic sampling where data collection occurs at strategically determined intervals. The sampling frequency is dynamically adjusted based on detected physiological events, activity levels, and prediction confidence, maintaining signal integrity while minimizing energy consumption during low-activity periods.
Solution Approach 2:
The system performs preliminary signal processing and anomaly detection locally at the sensor node before transmission. By pre-processing data to identify only the most significant events and trends, the system reduces the amount of data that requires continuous monitoring and transmission, thereby reducing overall energy consumption.
3Measurement precision
If all sensor data is transmitted to maintain data accuracy, then measurement precision is improved, but loss of energy increases
Solution Approach 1:
The system extracts and transmits only the most critical physiological parameters and anomaly detections from the comprehensive sensor array, rather than transmitting all sensor data. This selective extraction reduces data volume and energy consumption while maintaining the essential monitoring functionality.
Solution Approach 2:
The system creates a compressed representation or copy of the essential physiological information through machine learning predictions. Instead of transmitting raw sensor data, the system transmits summarized statistical features, anomaly flags, and predicted physiological states, which consume significantly less energy while preserving data accuracy for monitoring purposes.
4Reliability
If comprehensive sensor monitoring is implemented, then reliability is improved, but device complexity increases
Solution Approach 1:
The system employs machine learning models that can infer multiple physiological states from a limited set of sensor readings. A single sensor measurement can serve multiple monitoring purposes through predictive modeling, reducing the need for dedicated sensors for each physiological parameter while maintaining comprehensive monitoring capability.
Solution Approach 2:
The system implements feedback mechanisms where machine learning models continuously learn from transmitted data to improve their predictions. This feedback loop allows the system to refine its understanding of physiological patterns over time, enhancing monitoring reliability even with a reduced sensor subset.
Data Source
AI summary
A sensor system and method configured to take multiple channels of sensors, and based on context and user behavior reflected in the signals, identifies specified channels for sensing according to a sensing policy. The sensing policy is used to reduce the amount of data sampled, such that it is possible to reconstruct the values of the non sampled sensors efficiently. The sensing policy is influenced by user and system's behavior and can be assigned either offline or in real time.