Wearable Sensor Data Compression via Link Quality Adaptation
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Solution Overview
Problem
Wearable devices with multiple sensors face bandwidth limitations due to low power transmission requirements, where distance and interference reduce effective throughput, making it challenging to transmit high-rate data collected by multiple sensors simultaneously.
Innovation Solution
Implementing a low power compressible sensor system that applies compression algorithms to reduce transmission bitrate based on detected channel throughput and controls sample rates of sensors, using techniques like differential pulse-code modulation and adaptive delta pulse-code modulation, to optimize data transmission over low power communication channels like Bluetooth Low Energy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If sensor data is transmitted at high rate to overcome bandwidth limitations, then data throughput is improved, but power consumption increases
Solution Approach 1:
The system dynamically changes the sampling rate parameter of sensors based on detected channel throughput conditions. When channel quality is good, higher sampling rates are used to maximize data throughput. When channel quality degrades, the sampling rate is reduced to maintain acceptable throughput while minimizing power consumption. This parameter adaptation resolves the contradiction between maintaining high data throughput and reducing power consumption in wearable devices.
2Measurement precision
If multiple sensors transmit data simultaneously to increase data collection capability, then measurement capability is improved, but bandwidth requirements increase
Solution Approach 1:
The patent combines data from multiple sensors (accelerometer, gyroscope, barometer, etc.) and applies joint compression algorithms that exploit correlations between different sensor types. By merging the data streams and applying coordinated compression, the system maintains comprehensive multi-sensor measurement capability while reducing the total bandwidth required compared to transmitting each sensor stream independently.
3Quantity of substance
If compression algorithms are applied to reduce transmission bitrate, then bandwidth usage is reduced, but data processing complexity increases
Solution Approach 1:
The system employs dynamic compression strategies where the compression algorithm type and intensity are adjusted in real-time based on channel throughput conditions and sensor activity levels. During periods of high channel quality, lighter compression is applied. During low quality periods or high sensor activity, more aggressive compression is used. This dynamic adaptation reduces average transmission bitrate while managing processing complexity through context-aware algorithm selection.
Data Source
AI summary
Devices and methods to compress sensor data are generally described herein. An exemplary wearable device to compress sensor data may include a sensor including circuitry to sense sensor data, and a communication circuit to receive, from a remote device, a detected link quality of a low-power communication channel used to communicate with the remote device. The communication circuit further to transmit compressed data to a remote device over the low power communication channel. The wearable device may further include a compressible sensor data module to apply a compression algorithm to compress received sensor data based on the detected link quality to provide the compressed sensor data.