Motion Sensor Data Encoding for Gait Analysis
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Solution Overview
Problem
Current systems for studying human gait and movement using inertial sensors face challenges in battery autonomy due to high energy consumption from real-time data transmission, leading to limited operation time and reduced precision in data processing, especially when miniaturization is desired.
Innovation Solution
A method of encoding motion sensor data using processors integrated with sensors, which calculates descriptors from time series data and transforms them into a data tensor coded on at most eight bits, allowing for reduced energy consumption and efficient data transfer while maintaining detailed analysis and real-time processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If real-time transmission of raw sensor data is implemented, then detailed analysis of movement is achieved, but energy consumption increases significantly
Solution Approach 1:
The patent applies preliminary action by performing data processing and encoding operations on the mobile device before transmission. The raw sensor data is pre-processed, encoded into compressed representations, and only essential features are transmitted to the remote device, thereby reducing the energy cost of transmission while preserving analytical value.
Solution Approach 2:
The patent extracts only the most relevant features and characteristics from the raw sensor data through encoding operations. Instead of transmitting complete raw datasets, the system extracts essential movement parameters and transmits only these compressed representations, reducing transmission energy while maintaining analysis precision.
2Duration of action of moving object
If battery size is increased to extend operation duration, then autonomy is improved, but device dimensions and weight increase
Solution Approach 1:
The patent changes the parameter of data representation from raw high-resolution formats to compressed encoded formats. This parameter change in data structure allows for more efficient storage and transmission, reducing the energy consumption rate and extending battery autonomy without requiring larger battery capacity or increased device volume.
3Use of energy by moving object
If data is encoded to reduce transmission size, then energy consumption decreases, but data accuracy may be compromised
Solution Approach 1:
The patent applies local quality by using different encoding strategies for different aspects of the data. Critical movement parameters are encoded with higher precision while less critical information uses more aggressive compression. This selective approach maintains data accuracy for essential measurements while reducing overall data size and transmission energy.
4Productivity
If all processing algorithms are integrated into the measurement device, then real-time processing capability is improved, but device complexity and size increase
Solution Approach 1:
The patent segments the processing architecture into two parts: encoding algorithms integrated into the mobile measurement device for real-time data compression, and advanced analysis algorithms executed on remote devices with greater computational resources. This segmentation enables real-time processing capability while distributing complexity across multiple devices.
Data Source
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AI summary
The invention relates to a device (20) for encoding data generated by one or more motion sensors (21), a system (5) for determining the displacement of a mobile entity, in particular a user, and a method (1) for encoding data generated by one or more motion sensors (21) coupled to a mobile entity such as a shoe, comprising: acquiring (200) data; computing (300) a plurality of descriptors over a time window (31); coding each of the descriptors on at most eight bits, said bits associated with a time window (31) being representative of a motion phenomenon of the mobile entity such as the shoe; and generating (500) a data tensor comprising the bits obtained for each of the time windows (31), the data tensor comprises an encoding of a combination of motion phenomena representative of a movement of the mobile entity.