Wearable Sensor for Dyskinesia Monitoring via Evolutionary Algorithm
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for documenting and managing Levodopa-induced Dyskinesia in Parkinson's disease are inadequate due to inaccurate recording of symptom timing and complexity of medication regimens, leading to potential worsening of the condition and increased healthcare costs.
Innovation Solution
A wearable device equipped with sensors that detect 3-D motion, pitch, roll, and yaw, using evolutionary algorithms for data processing and classification, allowing for continuous, non-invasive monitoring and accurate analysis of dyskinesia without clinician presence, with data transmission and processing via wireless means.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual documentation of dyskinesia symptoms is used, then patients can provide some information, but accuracy and detail of symptom timing and characterization are insufficient
Solution Approach 1:
The system enables automatic self-monitoring where the wearable sensor device continuously records dyskinesia symptoms without requiring active patient participation. The device autonomously detects movements, processes data through evolutionary algorithms, and generates diagnostic reports, allowing patients to benefit from precise monitoring while minimizing their operational burden.
Solution Approach 2:
Manual documentation methods are replaced with electronic sensing systems. The wearable device uses accelerometers and gyroscopes to objectively detect and quantify dyskinesia movements, substituting subjective patient reporting with objective mechanical measurement and automated analysis.
2Loss of information
If detailed manual monitoring is implemented, then symptom characterization improves, but time consumption and cost increase significantly
Solution Approach 1:
The system performs automated data collection, processing, and analysis without requiring clinician time for manual monitoring. The evolutionary algorithm automatically classifies dyskinesia types and generates diagnostic reports, enabling complete symptom characterization while eliminating the need for prolonged clinician observation and documentation.
Solution Approach 2:
An automated computational intermediary (the evolutionary algorithm processing system) is introduced between the patient and clinician. This intermediary continuously analyzes sensor data, classifies symptoms, and prepares diagnostic information, replacing the need for direct clinician-patient monitoring interactions and significantly reducing time loss.
3Reliability
If multiple medication doses are adjusted based on dyskinesia patterns, then treatment effectiveness improves, but complexity of medication management increases
Solution Approach 1:
The system implements continuous feedback by monitoring dyskinesia symptoms in real-time and using evolutionary algorithms to identify patterns related to medication timing and dosage. This feedback mechanism provides clinicians with evidence-based guidance for adjusting multiple medication doses, improving treatment effectiveness while simplifying the decision-making process through automated pattern recognition.
Solution Approach 2:
The system performs preliminary analysis of dyskinesia patterns and medication response before clinicians make dosing decisions. By pre-processing sensor data and generating diagnostic reports that identify optimal adjustment strategies, the system prepares actionable insights in advance, reducing the complexity of managing multiple medication doses.
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
Enables accurate and continuous monitoring of dyskinesia, reducing distress to patients and saving clinician time, facilitating informed medication adjustments and reducing hospital admissions by providing precise data for treatment decisions.
Implementation Method 1
said sensor including a first detector to detect 3-D motion
Implementation Method 2
a second motion detector to detect pitch, roll and yaw of the device
Data Source
Figure 1
AI summary
The invention disclosed herein is to a device to assist in the determination of the presence and type of dyskinesia in a patient. The device includes a sensor, removably attachable to a patient's body, such as on a limb or torso, the sensor being capable of detecting 3-D motion. Data generated by the sensor is transferred to and retained in a data retention means. A processing means is included to process the generated data, along with a look-up table of processed data for already known dyskinesia conditions for comparison, The processing means employs an evolutionary algorithm in the classification of the data. Output means display the diagnosed condition to a user.