Wearable Sensor Device for Objective Spinal Movement Assessment
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Solution Overview
Problem
Current methods for evaluating spinal conditions rely on subjective patient feedback and cumbersome clinical tools, which can provide inaccurate and incomplete data, lacking objective measurement of movement-related information essential for neurological and musculoskeletal health assessment.
Innovation Solution
A wearable electronic device with sensors, such as inertial measurement units and electrocardiogram sensors, collects movement data and classifies it into activity categories, generating scores that can be used to propose a diagnosis through a connected computing system, utilizing machine learning to improve accuracy and reduce clinician burden.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If patient feedback and clinical evaluation tools like ODI are used to assess spinal conditions, then subjective assessment can be obtained, but the data accuracy and objectivity deteriorate
Solution Approach 1:
The patent replaces subjective mechanical assessment methods (patient self-reporting, clinician evaluation) with automated sensor-based measurement systems. Wearable sensors with accelerometers, gyroscopes, and magnetometers objectively capture spinal movement data, eliminating the subjectivity inherent in patient feedback while providing precise, quantifiable measurements of spinal condition.
Solution Approach 2:
The system enables automated self-assessment where the wearable device automatically collects, processes, and analyzes spinal movement data without requiring clinician intervention for data collection. The device performs self-calibration and automatically generates assessment reports, reducing the burden on clinical staff while maintaining high measurement precision.
2Reliability
If comprehensive clinical measurement tools like ODI are implemented, then assessment thoroughness is improved, but clinician burden and time consumption increase
Solution Approach 1:
The wearable device continuously collects and pre-processes spinal movement data in the background before clinical assessments are needed. Sensors automatically monitor spinal parameters throughout the day, pre-computing movement patterns and anomalies so that clinicians receive ready-analyzed data rather than raw information requiring extensive processing during limited consultation time.
Solution Approach 2:
The system introduces an intermediary automated analysis layer between data collection and clinical interpretation. Machine learning algorithms and processing units act as intermediaries that filter, analyze, and prioritize spinal movement data, presenting only clinically relevant findings to physicians. This intermediary processing maintains comprehensive assessment quality while dramatically reducing the time clinicians must spend on data analysis.
3Ease of operation
If subjective patient responses are used for spinal assessment, then ease of data collection is improved, but data completeness and reliability deteriorate
Solution Approach 1:
The patent substitutes subjective patient reporting with objective sensor-based measurement systems. Wearable devices with multiple sensors (accelerometers, gyroscopes, magnetometers) automatically capture comprehensive spinal movement data including range of motion, movement patterns, and abnormal motions, eliminating the incompleteness and subjectivity of patient self-reporting while maintaining ease of use through automatic data collection.
Data Source
AI summary
A system for assessing a spinal disorder includes a wearable electronic device having one or more sensors and an assessment system. The wearable electronic device is configured to be positioned on a portion of a lower back of a wearer, and the one or more sensors are configured to obtain patient data associated with the wearer. The system receives patient data from the one or more sensors, where the patient data includes movement data associated with movement of the lower back of the wearer, classifies the movement data into an initial grouping, further classifies the at least a portion of the movement data into one of the plurality of activity categories, generate a score corresponding to the at least a portion of the movement data based on the activity category to which the movement data is classified, and cause the score to be displayed via a client electronic device.