Wearable EMG Sensor with Accelerometer Noise Filtering
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Solution Overview
Problem
Current wearable technology devices, such as EMG sensors, fail to provide user-friendly data on muscle fatigue and are often too complex for general consumers to interpret, lacking a user-centric design and accuracy in measuring fatigue levels for various muscle activities.
Innovation Solution
A portable and wearable electromyography (EMG) sensor integrated into smart clothing, using electrodes and an accelerometer to monitor muscle fatigue, with real-time data analysis via a smartphone app, filtering noise and providing biologically driven fatigue measurements for any muscle activity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current wearable EMG sensors are used to monitor muscle fatigue, then muscle activity data can be collected, but the data is too complex and difficult for general consumers to interpret
Solution Approach 1:
The patent introduces a processor as an intermediary between the EMG sensor and the user interface. The processor automatically analyzes raw EMG signals, filters noise, and converts complex muscle activity data into simplified fatigue metrics and visual representations that consumers can easily understand without needing professional knowledge
Solution Approach 2:
The patent creates simplified copies or representations of the complex muscle fatigue data through visual interfaces. Instead of displaying raw EMG waveforms and numerical data, the system generates intuitive visual indicators such as fatigue level gauges, color-coded displays, and graphical representations that mirror the actual muscle state in an easily interpretable format
2Productivity
If existing fatigue monitoring systems are used, then some muscle activity data is collected, but the measurements are based on pre-set inputs rather than biologically driven factors leading to inaccuracies
Solution Approach 1:
The patent dynamically adjusts monitoring parameters based on biologically driven factors rather than using fixed pre-set inputs. The system continuously analyzes actual EMG signal characteristics, muscle activation patterns, and physiological responses to adaptively determine fatigue thresholds and metrics, ensuring measurements reflect true biological fatigue states rather than arbitrary predetermined values
Solution Approach 2:
The system performs self-calibration and self-adjustment by continuously monitoring the user's actual muscle responses and adapting its measurement criteria accordingly. Rather than relying on manufacturer-pre-set parameters, the system learns and adapts to each user's specific physiological characteristics, ensuring accurate fatigue measurement tailored to individual biological variations
3Ease of operation
If automated data collection is implemented, then manual data input is eliminated, but the data generated is too simplistic and lacks user-centric design
Solution Approach 1:
The patent segments the automated data collection process into multiple levels of analysis and presentation. The system collects comprehensive raw data, processes it through multiple analytical stages, and presents different levels of information detail based on user needs. This segmentation allows both simplified overview data for general users and detailed analytical data for those seeking deeper insights, preventing information loss while maintaining ease of use
4Measurement precision
If continuous EMG monitoring is performed to capture all muscle activities, then comprehensive fatigue data is obtained, but noise from non-muscle movements complicates the measurements
Solution Approach 1:
The patent converts the harmful noise from non-muscle movements into beneficial information by using it as a reference signal for noise cancellation algorithms. The system intentionally captures these extraneous signals and uses them to train and improve its noise filtering capabilities, thereby enhancing the accuracy of muscle fatigue measurements while maintaining comprehensive monitoring
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
Enables users to easily monitor and understand muscle fatigue levels in real-time, preventing injuries from overuse by providing actionable, consumer-oriented data through a user-friendly interface, improving the accuracy and accessibility of muscle fatigue monitoring.
Implementation Method 1
a portable and wearable electromyography (EMG) sensor that is configured to continually monitor electrical signals for local muscle fatigue
Implementation Method 2
an accelerometer, wherein the monitoring device is configured to be worn by a user
Data Source
AI summary
The present device is directed to a biometric electromyography (EMG) sensor device and methods for using the same to provide a user with the ability to monitor physical muscle fatigue and protect themselves against injury due to overuse. Embodiments of the present disclosure include an accelerometer for artifact noise removal and an EMG sensor that is used to collect EMG signals and surface EMG (sEMG) signals from electrodes placed onto a wearable device, which can be in tight contact with the skin due to its elasticity. This collected data can then be transmitted to an electronic device via a microcontroller. The electronic device may include an app that is continuously running to monitor user fatigue in real-time.