Mobile Device Kinematic Data Acquisition for Neuromotor Assessment
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Solution Overview
Problem
Existing methods for acquiring kinematic data for neuromotor assessments face challenges in comparing results across different experiments, requiring improvements for effective detection, diagnosis, and research, especially in conditions like Parkinson's and Alzheimer's diseases.
Innovation Solution
A method and system utilizing a mobile computing device to present handwriting, speech, and natural movement tasks, acquiring kinematic data with external stimuli, processing it to characterize neuromotor performance, and transmitting the data for analysis, allowing for synchronized and comparative assessments across different muscle groups and modalities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple tasks from different modalities are presented on a single mobile device, then the ability to compare and correlate data from different neuromotor groups is improved, but the device complexity increases
Solution Approach 1:
The mobile computing device is designed to perform multiple functions by presenting different types of tasks (handwriting, speech, natural movement) using various sensors (touchscreen, accelerometer, gyroscope, microphone). This multi-functionality allows the single device to acquire kinematic data from multiple neuromotor groups and enable comparative analysis across different modalities, resolving the contradiction between versatility and device complexity.
2Reliability
If an external stimulus is provided as a trigger for tasks, then the synchronization and control of data acquisition is improved, but the time required for task execution increases
Solution Approach 1:
The system presents tasks to the subject in advance and uses external stimuli as triggers to initiate data acquisition at the appropriate moment. This preliminary preparation ensures that when the stimulus occurs, the device is ready to capture synchronized kinematic data across multiple sensors, improving reliability while minimizing the actual data collection time required during the task execution phase.
3Productivity
If kinematic data is processed on the mobile computing device, then the productivity of data analysis is improved, but the measurement precision may be compromised due to computational limitations
Solution Approach 1:
The data processing is divided into segments: initial processing and characterization of neuromotor performance are performed on the mobile device, while more complex analysis and validation can be performed on remote servers or workstations. This segmentation allows the mobile device to perform basic analysis efficiently without compromising measurement precision, as complex computations can be offloaded to more powerful systems.
Data Source
AI summary
Methods and systems for obtaining kinematic data from a subject for neuromotor assessment are presented herein. The method comprises presenting on a mobile computing device at least two of: a handwriting task, a speech task, and a natural movement task, each task executable by the subject with the mobile computing device, providing an external stimulus through the mobile computing device as a trigger to begin each task and acquiring kinematic data from the subject on the mobile computing device as the tasks are being performed. The acquired kinematic data may be stored locally, processed locally, and/or stored and transmitted remotely for processing.