Motion Assessment via Wireless Sensor Symbol Sequences
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Solution Overview
Problem
Athletes and patients face challenges in precisely repeating body motions during physical training or rehabilitation without direct supervision, as they often forget the correct execution of motions when not monitored.
Innovation Solution
A method and system that utilize wireless sensor networks to assess motions by segmenting sensor data into motion fragments, extracting features, assigning symbols, and forming symbol sequences to provide real-time feedback on the accuracy of motion execution, allowing for continuous assessment and improvement during physical exercise.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If wireless sensor networks are used to monitor body motions, then real-time feedback and motion assessment are enabled, but the complexity of the system increases due to multiple sensor nodes and data processing requirements
Solution Approach 1:
The patent segments the body into multiple regions, each monitored by dedicated sensor nodes. This segmentation allows distributed measurement of motion parameters across different body parts, improving assessment precision while managing system complexity through modular sensor placement and independent data processing at each node.
Solution Approach 2:
The patent introduces a base station as an intermediary component that receives data from multiple sensor nodes, performs centralized processing, and generates comprehensive motion assessments. This intermediary architecture reduces the computational burden on individual sensor nodes and simplifies the overall system design by centralizing complex data fusion and analysis functions.
2Reliability
If multiple sensor nodes are deployed to assess body motions, then comprehensive motion monitoring is achieved, but the amount of data to be processed and transmitted increases significantly
Solution Approach 1:
The patent performs preliminary data processing and feature extraction at each sensor node before data transmission to the base station. By pre-processing raw sensor data into meaningful motion features and patterns locally, the system reduces the volume of data to be transmitted and stored, minimizing information loss while maintaining monitoring reliability.
Solution Approach 2:
The patent merges data from multiple sensor nodes at the base station through integrated analysis that combines motion patterns, physiological parameters, and contextual information. This merging approach consolidates redundant information and creates a unified motion assessment, reducing overall data processing requirements while improving monitoring reliability through cross-validation of multiple data sources.
3Productivity
If real-time feedback is provided during motion execution, then motion quality improves, but the system requires continuous data processing and communication which consumes energy
Solution Approach 1:
The patent implements periodic data transmission and processing cycles rather than continuous real-time communication. Sensor nodes transmit data at optimized intervals based on motion detection thresholds and training phase requirements, enabling real-time feedback when needed while minimizing energy consumption during stationary or low-activity periods.
Solution Approach 2:
The patent dynamically adjusts system responsiveness and data processing intensity based on the training phase and motion detection algorithms. During learning phases, the system provides more frequent feedback with higher processing intensity, while during mastery phases it reduces communication frequency and energy consumption, adapting the balance between productivity and energy use.
Data Source
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AI summary
The invention regards a method and a feedback system for the assessment of motions of a versatile system that use a wireless sensor network attached to the versatile system. The method comprises a training phase and a feedback phase. In the training phase, a defined motion is conducted by the versatile system, whereby sensor data are gathered by the sensor nodes (31, 32, 33). After segmentation (202) of the sensor data into separate segments and the extracting (203) of features, a symbol is assigned to each of the motion fragments. A symbol sequence is formed which represents the complete motion conducted by the versatile system at a sensor node. After repeating at least once the conducting step and/or the analysing step, a reference symbol sequence (207) is formed at each sensor node (31, 32, 33). In the feedback phase, the defined motion is conducted again, and the collected sensor data are analyzed in a similar manner as in the training phase, wherein at each sensor node at least one motion fragment of the present motion is assessed (404) at least by comparing the symbol assigned to the motion fragment with a to be expected symbol of the reference symbol sequence. A feedback (405, 409, 410) is provided to the versatile system depending on the result of the assessment. The feedback refers to a motion fragment and may be provided while the motion is still carried out.