Sensorized Reflex Hammer for Quantitative Tendon Reflex Assessment
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Solution Overview
Problem
The existing methods for assessing deep tendon reflexes (DTRs) in neurological examinations are largely qualitative and prone to diagnostic imprecision and inaccuracy, leading to delayed diagnoses of conditions like Guillain Barre syndrome and Myasthenia gravis due to inter-rater reliability issues and the need for specialized neurologist training.
Innovation Solution
A smart reflex hammer equipped with force and acceleration sensors, along with circuitry, to quantify reflex measurements, providing quantitative data for accurate neurological assessments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If qualitative visual estimation method is used for DTR assessment, then the examination method is simple and easy to perform, but the diagnostic precision and accuracy are poor
Solution Approach 1:
The patent replaces the manual mechanical visual estimation method with an automated sensor-based measurement system. Force sensors and accelerometers objectively measure the mechanical response of tendons and limbs during reflex examination, eliminating subjective human visual estimation and providing precise quantitative data for diagnostic assessment.
Solution Approach 2:
The patent introduces sensors as intermediary devices between the examiner and the patient's reflex response. These sensors act as mediators that objectively capture and transmit physiological data, bridging the gap between the mechanical stimulus (hammer strike) and the physiological response (limb movement), thereby enabling precise measurement without direct human subjectivity.
2Reliability
If standardized quantitative measurement system is implemented, then diagnostic accuracy is improved, but the device complexity and cost increase
Solution Approach 1:
The patent transforms the qualitative parameter of reflex response into quantitative parameters through sensor measurements. By measuring force, acceleration, and movement parameters objectively, the system converts subjective visual estimates into standardized numerical data, enabling consistent and reliable inter-rater comparisons across different examiners and settings.
3Productivity
If manual visual assessment is used, then the examination process is quick and simple, but diagnostic delays occur due to imprecision
Solution Approach 1:
The patent implements real-time feedback through sensors that immediately capture and process reflex response data during the examination. This feedback mechanism provides instantaneous objective measurements of force and acceleration, enabling rapid and accurate diagnostic decisions without the delays associated with subjective visual assessment and specialist referral.
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
Enhances diagnostic precision by offering highly quantitative reflex measurements, enabling timely and accurate diagnosis of neurological conditions through standardized data analysis and machine learning algorithms.
Implementation Method 1
a force sensor coupled to the bumper and adapted to generate force data in response to force encountered by the bumper
Implementation Method 2
a first accelerometer coupled to generate head acceleration data in response to movement of the head
Implementation Method 3
a second accelerometer supported by the housing to generate limb acceleration data
Data Source
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AI summary
A system includes a first device having a handle, a head coupled to the handle, a bumper supported by a first end of the head and adapted to be used to strike a patient tendon, a force sensor coupled to the bumper and adapted to generate force data in response to force encountered by the bumper and to generate force data, a first accelerometer coupled to generate head acceleration data in response to movement of the head, and first circuitry to capture the force data and acceleration data. The system may further include second device having a housing adapted to be coupled to the patient limb, a second accelerometer supported by the housing to generate limb acceleration data, and second circuitry to capture the acceleration data.