Wearable Sensor Array for Accurate Hand Position Detection
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Solution Overview
Problem
Conventional systems for determining finger and hand position often require separate devices and struggle to accurately account for small movements, leading to erroneous gesture identification and complex signal processing, particularly with electromyography data.
Innovation Solution
A sensor array integrated into a wearable device that generates composite data packages from pressure sensors on the wrist, using a controller to process and filter sensor signals, combine them into stable patterns, and classify hand positions for precise gesture recognition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional systems use separate devices (wristband, forearm band, camera) to track hand position, then the system can detect arm movement, but additional devices are required and the system cannot accurately account for small movements
Solution Approach 1:
The patent combines multiple sensing functions (accelerometer, gyroscope, pressure sensors, capacitive touch sensors) into a single integrated wearable device. This merging eliminates the need for separate wristbands, forearm bands, and cameras while providing comprehensive hand position detection through coordinated data from multiple sensor types within one device.
Solution Approach 2:
The integrated wearable device performs multiple functions simultaneously: motion tracking via accelerometers and gyroscopes, pressure detection through pressure sensors, and touch input recognition through capacitive touch sensors. This multi-functionality allows the single device to replace multiple specialized devices while accurately capturing both gross and fine hand movements.
2Reliability
If conventional systems use aerial motion data or EMG data to track hand position, then the system can detect arm movement, but small movements during gestures are not accounted for leading to erroneous gesture identification
Solution Approach 1:
The patent segments the hand detection task into multiple measurement components: gross arm motion captured by accelerometers and gyroscopes, and fine local movements captured by pressure sensors and capacitive touch sensors positioned at specific hand locations. This segmentation allows the system to detect both large-scale and subtle movements without interference.
Solution Approach 2:
Pressure sensors and capacitive touch sensors are strategically positioned at specific locations on the hand to detect local movements and pressure distributions. This local quality approach enables the system to detect subtle gestures and small movements at specific hand regions while filtering out noise from larger body movements through signal processing.
3Measurement precision
If conventional systems use EMG data to determine hand position, then the system can measure muscle electrical stimulation, but complex signal processing is required to translate signals into meaningful hand position
Solution Approach 1:
The patent replaces complex EMG signal processing with more direct mechanical sensing approaches. Pressure sensors directly measure applied force and contact pressure, while capacitive touch sensors directly detect touch input and hand position. These direct mechanical measurements eliminate the need for complex electrical signal translation and processing while providing equally accurate hand position data.
Solution Approach 2:
Instead of indirectly inferring hand position through EMG muscle stimulation signals, the patent uses direct copying of mechanical hand state through pressure sensors and capacitive touch sensors. These sensors directly copy the physical pressure distribution and contact characteristics of the hand, providing a more direct and simpler pathway to accurate hand position determination.
4Stability of the object's composition
If the sensor array filters sensor signals to stabilize them, then small movements are filtered out, but the filtering process modifies signal components
Solution Approach 1:
The patent employs feedback mechanisms where sensor signals are continuously monitored and adjusted. The system uses the raw sensor data to generate stabilized signals for hand position determination, while simultaneously using this information to refine and adjust the filtering process. This feedback loop ensures that signal stabilization does not permanently modify essential components, allowing for adaptive filtering that maintains accuracy.
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
This approach provides accurate and reliable finger and hand position determination, reducing errors and simplifying signal processing by filtering out small movements and stabilizing sensor signals, allowing for precise gesture recognition without the need for additional devices.
Implementation Method 1
generates a set of sensor signals in response to a position of a user's hand and/or fingers
Data Source
AI summary
Systems, apparatuses, and/or methods to determine finger and/or hand position. For example, sensors on a wrist of a user may provide sensor signals. A filter may generate filtered sensor signals from the sensor signals, wherein each filtered sensor signal may include a modified signal component. A sorter may sort each filtered sensor signal in a composite pattern based on each modified signal component. A classifier may determine finger and/or hand position based on the composite pattern. In one example, a function of a computing platform may be controlled based on the finger and/or hand position.