Dynamic Neuromuscular Sensor Array for Artifact Mitigation
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Solution Overview
Problem
Existing neuromuscular sensor systems face challenges in providing high-fidelity data due to factors like ambient electromagnetic radiation, imperfect skin contact, and electromagnetic interference, which can lead to noisy and inaccurate sensor readings, affecting user immersion and gesture-based input in XR applications.
Innovation Solution
The system selectively activates and differentially pairs neuromuscular sensors based on real-time performance evaluations and conditions, using a dynamically configurable array of sensors to improve data fidelity and reduce computational resource consumption, while also mitigating artifacts in sensor data through real-time processing and statistical modeling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all neuromuscular sensors are continuously activated to ensure high data fidelity, then measurement precision is improved, but power consumption increases
Solution Approach 1:
The system dynamically configures the sensor array by selectively activating only the subset of neuromuscular sensors needed for the current task or gesture, rather than continuously activating all sensors. This dynamic activation/reactivation approach maintains measurement precision when needed while reducing power consumption during idle or less demanding periods.
Solution Approach 2:
The system changes the operational parameters of the sensor array by adjusting which sensors are active based on task requirements, user behavior patterns, and signal quality metrics. This parameter change enables the system to optimize the balance between measurement precision and power consumption adaptively.
2Measurement precision
If a large array of neuromuscular sensors is used to improve gesture recognition accuracy, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The large sensor array is segmented into multiple independent or semi-independent groups or channels. Each segment can be independently activated or configured based on the specific gesture or task being performed, reducing the effective complexity while maintaining the capability for high-precision gesture recognition when needed.
Solution Approach 2:
The sensor array is designed with universal functionality where the same physical sensors can serve multiple purposes - detecting different gesture types, different muscle groups, or serving as backup sensors. This multi-functionality reduces the need for specialized sensors for each gesture type, thereby reducing overall device complexity.
3Reliability
If real-time artifact mitigation processing is applied to all sensor signals, then reliability is improved, but computational resources consumed increase
Solution Approach 1:
Artifact mitigation processing is applied selectively to specific sensor signals based on their individual quality metrics, artifact levels, and relevance to the current task. Rather than uniformly processing all signals, the system applies processing only where needed, improving signal reliability while minimizing computational resource consumption.
Solution Approach 2:
The system uses feedback from signal quality monitoring to dynamically adjust the level and type of artifact mitigation processing applied to each sensor channel. When signal quality is high and artifacts are minimal, processing is reduced or skipped; when quality degrades or artifacts increase, processing intensity is increased accordingly.
Data Source
AI summary
The disclosed systems and methods are generally directed to interpreting neuromuscular signals. The system includes (A) a plurality of neuromuscular sensors that detect a plurality of neuromuscular signals from a user and (B) at least one multiplexer in communication with the plurality of neuromuscular sensors and that is capable of dynamically adjusting neuromuscular sensor processing based on neuromuscular signal characteristics. A computer processor is programmed to (i) receive a set of neuromuscular signals from the plurality of neuromuscular sensors, (ii) determine, via a real-time system, at least one signal characteristic included in a neuromuscular signal, where the neuromuscular signal is associated with a first neuromuscular sensor included in the plurality of neuromuscular sensors; and (iii) dynamically reconfigure the processing of neuromuscular signals from the plurality of neuromuscular sensors based on an output from the multiplexer. Various other methods, systems, and computer-readable media are also disclosed.