Wearable Kinematic Sensor Motor Symptom Assessment
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Solution Overview
Problem
Current methods for continuous monitoring of motor symptoms in movement disorders like Parkinson's disease and essential tremor are limited, as they lack systems and automatic methods to generate impairment indices, which are crucial for guiding therapy and continuous symptom monitoring.
Innovation Solution
The use of wearable kinematic sensors, such as accelerometers and gyroscopes, to collect and process signals, calculating impairment indices through power spectral density analysis, including artifact rejection and spectral estimation, to characterize movement disorders.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If wearable kinematic sensors are used to continuously monitor motor symptoms, then measurement precision and continuity are improved, but device complexity and data processing requirements increase
Solution Approach 1:
The patent segments the complex monitoring task into distinct frequency bands (e.g., tremor band, dyskinesia band, bradykinesia band), each analyzed separately to quantify specific motor symptoms. This segmentation allows the complex continuous data to be broken down into manageable, clinically relevant metrics without requiring overly complex processing systems.
Solution Approach 2:
The patent introduces spectral analysis as an intermediary processing layer between the raw sensor data and clinical interpretation. By using power spectral density analysis and frequency band integration as intermediaries, the system transforms complex continuous kinematic data into simplified impairment indices that are easier to interpret clinically.
2Measurement precision
If continuous monitoring is implemented to capture varying motor states, then measurement precision is improved, but loss of time and data processing burden increase
Solution Approach 1:
The patent implements periodic spectral analysis at defined intervals throughout the monitoring period, rather than continuous full-spectrum analysis. This periodic approach captures the varying motor states over time while reducing the overall computational burden by analyzing data in discrete time windows rather than continuously processing every data point.
Solution Approach 2:
The patent performs preliminary filtering and frequency domain transformation on the raw sensor data as it is collected, preparing the data in advance for subsequent impairment index calculation. This preliminary processing reduces the computational load for later analysis and enables faster generation of clinical metrics when needed.
3Measurement precision
If multiple kinematic sensors are deployed to capture comprehensive movement data, then measurement precision is improved, but device complexity and energy consumption increase
Solution Approach 1:
The patent extracts only the relevant frequency components and kinetic energy metrics from the full sensor data stream, rather than processing all raw data equally. By using spectral analysis to identify and extract specific frequency bands associated with different motor symptoms, the system reduces energy consumption while maintaining measurement precision for clinically relevant parameters.
Data Source
AI summary
Disclosed embodiments include an apparatus for generating a plurality of movement impairment indices from one or more kinematic signals to characterize movement disorders. Additionally we disclose methods for generating a plurality of movement impairment indices from one or more kinematic signals obtained from one or more kinematic sensors, said methods implemented in a digital computer with one or more processors in order to characterize movement disorders based on spectral analysis, regularity metrics, and time-frequency analysis.