Stethoscope TRIZ Cases

A system analyzes spectral properties of recorded utterances to determine upper airway anatomical parameters.

A movable diaphragm footing slides along an angled annular groove to adjust sound intensity, reducing assembly effort and preventing leakage.

Multi-sensor heart sound locator determines optimal auscultation positions via vibration signal analysis.

A heart sound extraction device acquires piezoelectric signals from a sheet sensor to isolate cardiac vibrations without direct contact.

Neural networks classify acoustic heart signals into categories, then fit data to dynamic state models to resolve detection accuracy versus processing time.

Electronic system detects fundamental frequency in vowel sounds to extract cardiovascular heartbeat information from audio signals.

A heart sound recognition circuit identifies intra-heart sound energy and time indications using multiple waveforms.

Heart sound variability analysis detects atrial fibrillation episodes using amplitude and morphology variations of the first heart sound.

Integrally molded polymeric chest piece with groove positions diaphragm to maintain acoustic quality while reducing production costs.

A headset with microphones passively detects vibrations from cerebral vasculature to extract unique diagnostic signals.

Cross-correlation filtering isolates noise in auscultatory sound signals, ensuring reliable coronary artery disease detection despite sensor detachment.

Real-time rigidity and elasticity monitoring replaces qualitative surgeon assessment to quantify prosthesis press-fit fixation.

Aligns multichannel accelerometer signals via covariance analysis to eliminate R wave detection and reduce processing power.

Multiple microphones isolate loudspeaker and external noise to improve detection accuracy during movement.

Dual accelerometer sensors estimate signal-to-noise ratio by compensating for unequal time shifts and gains to resolve imbalances.

Detection apparatus at cleaning stations records stethoscope usage and sanitization cycles, reducing nosocomial infection risks from cross-contamination.

A chest signal recording system uses a remote control device to generate guiding information for users during acoustic data capture.

A diagnosis device extracts feature parameters from breathing sound data to classify diseases using a pre-trained differential model.

An acoustic catheter stethoscope captures blood flow sounds to identify biomedical conditions through signal processing.

Automated vibratory insertion tools standardize acetabular cup placement by monitoring interface forces to prevent bone fractures during installation.

Injection molding encloses the sound conducting assembly in one step, eliminating manual insertion and ensuring uniform wall thickness.

A mobile circuitry segments heart signals to compute spectral width and peak frequency for automated murmur classification.

Ambulatory medical device uses multiple sensor metrics to detect worsening cardiac conditions and determine stimulation parameters.

Signal processing circuit detects physical activity transitions to classify heart sound characteristics for cardiac monitoring.

A biological examination apparatus extracts up-down and front-back motion components from magnetic field distance data to generate two-dimensional trajectory graphs.

Convolutional neural networks analyze auditory spectrograms to detect respiratory abnormalities, forecasting event severity without doctor presence.

A sensor detects skin displacement noise to calculate exercise metrics without accelerometers.

A physiological sound examination device calculates power ratios between frequency bands to determine sensor placement appropriateness.

A detection system generates composite heart sound metrics from S1 and S3 components to identify worsening heart failure events.

A stethoscope head uses protruding poles to adjust the vibrating diaphragm position for multi-frequency audio detection.

A heart beat model trained via machine learning detects cardiac signals using respiratory cycle phase information.

A compact wireless patch integrates multiple clinical-grade sensors and a communication interface to collect physiological data from subjects.

A stethoscope adapter uses a front diaphragm and sound tunnel to transmit vibrations to an inline microphone.

An email server mediates external requests to protected medical devices, bypassing institutional firewalls while maintaining data security.

Extraction of electronics from the stethoscope into the mobile phone resolves complexity and cost barriers in remote diagnosis.

Acoustic feature extraction and machine learning detect early aortic valve remodeling, reducing reliance on costly imaging equipment.

A physiological sound examination device measures lung sounds at two positions to calculate power ratios and isolate specific acoustic signals.

A heart activity monitoring system uses an alignment mechanism to position microphones at specific auscultatory areas for accurate audio signal capture.

An end effector uses an inflatable member and pressure sensor to detect tissue blood pressure fluctuations, reducing anastomotic leak risks.

A single-sensor adaptive gain control unit suppresses background noise and prevents musical artifacts by dynamically adjusting amplification thresholds.

Acoustic sensing inside the GI tract eliminates skin irritation from external sensors while maintaining signal fidelity during movement.

A wearable device uses polarized light and acoustic sensing to measure vital signs simultaneously.

A conformable eartip features a tapered sealing surface and deformable core for versatile ear canal fitting.

A smart stethoscope integrates heart sound, ECG, pulse wave, and oxygen saturation sensors into a single foldable device for user-selectable measurement modes.

Acoustic peak frequency detection generates inhaler flow profiles without physical microphone attachment.

A blood pressure measurement device calculates corrected values using a dedicated congestion determination section.

Manually disconnectable acoustic tubing segments isolate the chestpiece from the headset to prevent cross-contamination between patients and medical professionals.

A decision support tool calculates spectral roughness from digital respiratory sounds to forecast patient deterioration risk.

Heart sound metrics resolve weak electrical signals to improve atrial fibrillation detection accuracy.