Biopotential Microchip for Gesture Control
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Solution Overview
Problem
Biopotential sensing wearable devices face challenges in arranging electrodes to effectively gather biopotential signals and other relevant data, such as acceleration and angular rate data, due to limited surface area and volume, and in processing these signals efficiently.
Innovation Solution
A wearable device with a biopotential microchip that includes analog inputs, differential amplifiers, ADCs, an accelerometer, a gyroscope, and a processor to process biopotential, acceleration, and angular rate data, which are then transmitted to a machine learning classifier to generate gesture outputs, along with dynamic signal pathway rearrangement capabilities using switches or multiplexers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple electrodes and signal processing components are arranged on a wearable device, then gesture detection capability is improved, but device complexity increases
Solution Approach 1:
The patent combines multiple signal processing functions (differential amplification, ADC conversion, gesture recognition) into an integrated biopotential chip that processes signals from multiple electrodes simultaneously. This merging of functions reduces the overall system complexity while maintaining high gesture detection accuracy through unified signal processing.
Solution Approach 2:
The biopotential chip serves multiple functions: it amplifies differential signals from electrode pairs, converts analog signals to digital data, and performs gesture recognition algorithms. This multi-functionality allows a single component to handle complex signal processing tasks that would otherwise require multiple separate components, thereby reducing device complexity.
2Measurement precision
If biopotential chip surface area is increased to gather more signals, then signal gathering capability is improved, but device volume increases
Solution Approach 1:
The patent transitions from spatial arrangement (increasing surface area) to functional integration (stacking signal processing functions in vertical layers on the chip). Multiple signal processing stages are implemented in three-dimensional integration, allowing enhanced signal gathering capability without proportionally increasing the chip's footprint area.
Solution Approach 2:
The biopotential chip integrates smaller functional units within a compact structure, where differential amplifiers, ADCs, and gesture recognition circuits are nested within the chip substrate. This nested arrangement allows multiple signal processing functions to coexist in a small volume, maintaining enhanced signal gathering capability while minimizing device volume.
3Measurement precision
If multiple data types are processed simultaneously, then gesture recognition accuracy is improved, but processing time increases
Solution Approach 1:
The biopotential chip performs preliminary processing of biopotential signals (differential amplification and ADC conversion) before data leaves the chip. By pre-processing signals and converting them to digital format at the source, the system reduces the computational burden on downstream processors, enabling faster handling of multiple data types without sacrificing gesture recognition accuracy.
Solution Approach 2:
The integrated biopotential chip enables continuous processing of multiple data streams (biopotential signals, acceleration data, angular rate data) simultaneously through parallel processing pathways. This continuous action approach allows the system to maintain high gesture recognition accuracy by constantly analyzing all data types without sequential processing delays.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enhances gesture detection accuracy and adaptability by efficiently processing multiple data types and improving signal quality across the device, allowing for precise gesture recognition and control.
Implementation Method 1
at least one of the one or more analog inputs being coupled to a respective differential amplifier configured to amplify differences in signals between pairs of electrodes
Implementation Method 2
an accelerometer, the accelerometer being disposed onboard the biopotential microchip and configured to output acceleration data indicating an acceleration of the portion of the user's arm
Implementation Method 3
a gyroscope, the gyroscope being disposed onboard the biopotential microchip and configured to output angular rate data indicating an angular rate of the portion of the user's arm
Data Source
AI summary
Disclosed are methods, systems and non-transitory computer readable memory for gesture control. For instance, a system may include a wearable device configured to be worn on a portion of an arm of a user. The wearable device may include a plurality of electrodes disposed on an interior of the wearable device and configured to obtain biopotential signals from the user's arm; and a biopotential chip. The biopotential microchip may be configured to output, directly or indirectly, biopotential data, acceleration data, and/or angular rate data, or derivatives thereof (“gesture data”), to a machine learning classifier. The machine learning classifier may be configured to generate, based on the gesture data, a gesture output indicating a gesture performed by the user. In some cases, the plurality of electrodes may include one or more wristband electrodes and/or a plurality of hub electrodes in a hub. In some cases, the hub may be curved.