Adaptive Down-Sampling for Wearable Gesture Recognition
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Solution Overview
Problem
Conventional gesture recognition in wearable devices is inefficient due to high power consumption and computational resource usage, and is prone to noise and interference, especially for non-periodic and short-duration gestures.
Innovation Solution
The system employs adaptive down-sampling and normalization of sensor measurements to generate a feature vector, using a signal processor module with a resampler and normalizer to reduce variations in gesture speed and duration, and a gesture recognizer module to classify gestures effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If conventional gesture recognition methods are used, then gesture recognition functionality is provided, but power consumption and computational resource usage are high
Solution Approach 1:
The gesture recognition process is divided into distinct stages: raw sensor data acquisition, down-sampling processing, feature extraction, and classification. By segmenting the processing pipeline and applying different processing intensities at different stages, the system reduces overall computational load and power consumption while maintaining recognition accuracy.
Solution Approach 2:
The patent extracts only the most relevant features from the sensor data after down-sampling, rather than processing the complete raw dataset. This selective extraction of essential features (such as statistical moments, frequency characteristics) reduces computational resources required for gesture classification while preserving recognition performance.
2Measurement precision
If conventional gesture recognition methods are used, then gesture detection is performed, but recognition accuracy is reduced due to noise and interference
Solution Approach 1:
The patent applies down-sampling and preprocessing operations before feature extraction and classification. This preliminary action removes redundant data and reduces noise impact early in the processing chain, making subsequent recognition more accurate and robust against interference.
Solution Approach 2:
The down-sampling process, which reduces data resolution, is used strategically to filter out high-frequency noise while preserving the essential gesture characteristics. By converting the potential harm of data loss into a beneficial noise-filtering mechanism, the system improves signal-to-noise ratio for gesture detection.
3Loss of information
If high-resolution sensor data is processed, then detailed gesture information is captured, but computational complexity and processing time increase
Solution Approach 1:
Instead of uniformly processing all sensor data at high resolution, the patent applies different processing strategies to different portions of the data. Critical gesture phases are processed with higher fidelity while less important segments are down-sampled more aggressively, optimizing the balance between information retention and computational complexity.
Solution Approach 2:
The system performs partial processing on the complete dataset by down-sampling to a reduced resolution that is sufficient for gesture recognition purposes. This partial action approach processes only the essential information needed for accurate classification, avoiding the excessive computational burden of processing every detail.
Data Source
AI summary
Systems and methods for recognizing a gesture in a wearable device are disclosed. The system may sense a plurality of sensor measurements during a gesture sensing session, and down-sample the measurements using an adaptive down-sampling interval. The adaptive down-sampling interval may be determined based at least on a fractional part of a ratio of a frame length of the sensor measurements to a specified target length shorter than the frame length. The magnitude of the down-sampled measurements is normalized, and a feature vector may be generated using the normalized measurements. A gesture recognizer module may associate a gesture with the feature vector using gesture classification.