Limb Rotation Signal Segmentation for Motor Skill Monitoring
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Solution Overview
Problem
Current methods for evaluating motor skills in patients with neurodegenerative diseases like Parkinson's disease, such as the Leg Agility task, face challenges in accurately assessing movements with anomalies like tremors and hesitations due to the reliance on wavelets analysis that struggles with relative maxima and minima, leading to subjective and unreliable results.
Innovation Solution
A sensing device attached to the limb processes data to generate a rotation signal, segmenting each iteration of movement by determining start, peak, and end times based on null-velocity instants, using conditions on relative values to accurately identify kinematic quantities without assuming maximum duration, enabling reliable and objective evaluation of movements with anomalies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If wavelets analysis is used to evaluate movements, then automated quantitative evaluation is achieved, but measurement precision deteriorates due to inability to handle relative maxima and minima from tremors and hesitations
Solution Approach 1:
The method segments the rotation signal into multiple iterations by identifying null-velocity instants that separate individual movement cycles. This segmentation allows precise identification of start, peak, and end times for each iteration, enabling accurate measurement of kinematic quantities even when tremors or hesitations create additional local maxima and minima within each segment.
Solution Approach 2:
The method extracts only the null-velocity instants from the signal processing, using these specific points to define iteration boundaries. By focusing on these extracted key moments rather than attempting to process the entire continuous signal with wavelets, the system achieves precise measurement while maintaining automated evaluation.
2Adaptability or versatility
If subjective evaluation by examiner is used, then flexibility in assessment is maintained, but reliability deteriorates due to personal perception and experience variations
Solution Approach 1:
The system performs self-service automated evaluation by automatically processing the rotation signal to identify null-velocity instants and calculate kinematic quantities without requiring examiner intervention. This eliminates subjective perception and experience variations while maintaining the ability to assess different movement types through configurable parameters.
Solution Approach 2:
The method replaces the mechanical system of human examiner observation and subjective judgment with an automated signal processing system that objectively identifies null-velocity instants and calculates kinematic quantities. This substitution ensures consistent, reliable measurements while maintaining evaluation flexibility through programmable parameters.
3Device complexity
If maximum duration assumption is made for movement iterations, then processing simplicity is achieved, but measurement precision deteriorates when actual duration varies due to motor difficulties
Solution Approach 1:
The method dynamically identifies iteration boundaries by detecting null-velocity instants in the actual signal rather than assuming fixed duration. This dynamic approach adapts to varying movement speeds and durations caused by motor difficulties, ensuring precise iteration identification while maintaining processing efficiency through algorithmic simplicity.
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
The method provides accurate and reliable kinematic data for each movement iteration, even with anomalies, allowing for objective and frequent monitoring of motor skills, enabling more precise assessment of motor impairments and reducing the need for clinical evaluations.
Implementation Method 1
a sensing device suitable for being fixed to the limb and for providing data indicative of the movement of the limb... processing the data so as to generate a rotation signal θ(t) indicative of the rotation of the part of human body in the plane xy
Implementation Method 2
The data provided by the sensing device comprise first inertial data provided by an accelerometer of a sensing device cooperating with the part of human body, second inertial data provided by a gyroscope of the sensing device
Data Source
AI summary
The movement of a part of human body, e.g. a limb, is monitored. The movement includes N iterated rotations in a plane. A sensing device is fixed to the limb and provides data indicative of the limb movement. These data are processed to generate a rotation signal. Then, portions of the rotation signal corresponding to each movement iteration are identified. To this purpose, a plurality of null-velocity instants is identified, wherein the angular velocity of the sensing device is null. Then, each null-velocity instant is classified as a candidate start/end time or a candidate peak time. Then, a start time, end time and peak time for each iteration are determined as a combination of two candidate start/end times and a candidate peak time that fulfils certain conditions on the values of the rotation signal in correspondence of candidate start/end times and candidate peak times.