MiiCare Ltd (20240237923). Mobility Analysis simplified abstract
Contents
- 1 Mobility Analysis
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 Mobility Analysis - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Key Features and Innovation
- 1.6 Potential Applications
- 1.7 Problems Solved
- 1.8 Benefits
- 1.9 Commercial Applications
- 1.10 Prior Art
- 1.11 Frequently Updated Research
- 1.12 Questions about Audio-Based Mobility Measurement Technology
- 1.13 Original Abstract Submitted
Mobility Analysis
Organization Name
Inventor(s)
Kelvin Summoogum of London (GB)
Mobility Analysis - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240237923 titled 'Mobility Analysis
Simplified Explanation
The patent application describes a method for measuring the mobility of a subject using audio signals, machine learning algorithms, and neural networks.
- Receiving audio signals from microphones.
- Classifying overlapping regions of the audio signal as containing the sound of a footstep.
- Determining consecutive footsteps of a subject.
- Analyzing the footsteps to determine a mobility factor using a neural network.
Key Features and Innovation
- Utilizes audio signals to measure subject mobility.
- Employs machine learning algorithms for classification.
- Uses neural networks for mobility factor analysis.
Potential Applications
- Healthcare monitoring for gait analysis.
- Security systems for detecting movement.
- Sports performance analysis for athletes.
Problems Solved
- Provides a non-invasive method for measuring mobility.
- Automates the process of analyzing footsteps.
- Enhances accuracy in determining mobility factors.
Benefits
- Improved monitoring of subject mobility.
- Efficient analysis of footstep patterns.
- Enhanced data-driven insights for various applications.
Commercial Applications
Title: Audio-Based Mobility Measurement Technology
This technology can be applied in healthcare facilities, security systems, and sports performance centers to enhance monitoring and analysis capabilities.
Prior Art
There may be prior art related to audio-based gait analysis systems, machine learning algorithms for audio classification, and neural network applications in mobility assessment.
Frequently Updated Research
Research on improving the accuracy and efficiency of audio-based mobility measurement systems is ongoing, with advancements in machine learning algorithms and neural network architectures.
Questions about Audio-Based Mobility Measurement Technology
1. How does this technology improve upon traditional methods of measuring mobility?
This technology offers a non-invasive and automated approach to analyzing footstep patterns, providing more accurate and efficient mobility assessments.
2. What are the potential limitations of using audio signals for mobility measurement?
Using audio signals for mobility measurement may be affected by background noise, varying acoustic environments, and the need for calibration to ensure accuracy.
Original Abstract Submitted
a method for measuring the mobility of a subject, the method comprising: receiving an audio signal from one or more microphones: for each of a plurality of overlapping regions of the audio signal, classifying the region as containing the sound of a footstep using a first supervised learning algorithm, determining that two or more of the regions classified as containing the sound of a footstep correspond to a series of two or more consecutive footsteps of a subject; and using a first neural network, analysing the determined two or more regions to determine a mobility factor.
