Inertial Sensors TRIZ Cases

Passive shear-force isolation and a protrusion sensor improve muscle force detection by reducing cross-talk, motion noise, and interference.

A dedicated handheld unit turns shared heart rate data into synchronized vibration and RGB light, improving tactile communication beyond phones and wearables.

Dual accelerometers on the sternum and breast map 3D relative motion to guide bra fit that limits movement without uniform compression.

Correlating EGM noise with accelerometer motion helps distinguish true coughs from cardiac signal interference for more reliable monitoring.

Motion sensors capture heart wall vibrations to estimate tension continuously, avoiding invasive pressure monitoring and improving preload assessment.

LVET changes during reversible preload shifts reveal lost Frank-Starling reserve, helping time aortic valve replacement before irreversible damage.

High-resolution IMUs detect high-frequency muscle oscillations in real time, enabling early fatigue alerts and longitudinal performance tracking.

By comparing IPG and PPG signals, this wearable tracks myogenic responses more reliably than PPG alone during passive cardiovascular monitoring.

By comparing IPG and PPG signals, this wearable detects myogenic vasoconstriction and vasodilation despite motion and weak optical flow.

Deep learning trained on synthetic MCG and IMU motion data separates non-stationary magnetic noise for accurate unshielded measurements.

IMU motion tracking helps separate non-stationary noise from magnetocardiography signals, improving unshielded magnetic measurements.

Hand-worn proximity and IMU sensing flag maternal touch events so abdominal belt data can ignore false fetal movement readings.

A reversible sensor patch adds continuous vital sign monitoring to classic watches without changing their appearance.

Electrostatic sensors in glasses detect blink patterns and eye orientation while reducing noise, artifacts, and skin-contact errors.

Continuous filtering and max-value combining adapt pacemaker pacing rates to patient activity while reducing power use and noise-driven errors.

Standard-motion calibration links reference and wearable sensor data to correct personal variation and aging drift for accurate long-term sensing.

Chest rotation measured by a gyroscope is analyzed by energy spectral density to distinguish myocardial infarction from heartburn.

A filtered activity-to-rate module combines instant and averaged targets to adapt pacemaker heart rate with low power and less noise.

ECG and motion data are combined to place arrhythmia events in activity and posture context, reducing reliance on incomplete patient diaries.

Multiple implantable-device cardiac variables are modeled into a heart failure index to improve hospitalization prediction sensitivity and specificity.

Triggered calibration uses a primary wearable sensor to correct secondary sensors, preserving temperature accuracy as conditions and sensor drift change.

A removable wireless battery and retention pocket keep the monitor precisely positioned while enabling easy battery swaps for continuous wear.

Physical strain isolators decouple skin motion from wearable electrodes, reducing motion artifacts and improving biophysical signal quality.

A PLL-driven variable band-pass filter tracks biological signal frequency shifts to suppress noise and preserve measurement accuracy.

Gravity-vector alignment turns 3-axis chest acceleration into a robust 1D respiratory signal without controlled sensor orientation.

Pulse oximetry and motion data are combined to estimate metabolic rate more accurately during sedentary activity, including basal metabolism.

Multiple detector distances on a wrist PPG sensor identify vessel proximity and correct attenuation bias for steadier SpO2 and heart rate readings.

Voice, gesture, and anomaly-triggered time notations mark key moments in long recordings, speeding later retrieval and analysis.

Combining EMG and IMU sensors with AI analysis enables portable, real-time musculoskeletal load monitoring for rehabilitation and training.

EEG brain-wave sensing detects microsleep before eye or head cues appear, triggering alarms and optional dispatcher alerts.

Electrical current loops through gastric or intestinal tissue enable autonomous bioimpedance monitoring to detect fluid congestion early.

Motion sensors detect ECG attachment direction and send guidance to a user terminal so non-medical users can align leads correctly.

EEG signals are filtered and converted into audible patterns, enabling faster seizure recognition with less equipment and specialist support.

A detachable wearable pairs with a glucose sensor or activity tracker to combine continuous glucose, heart rate, and activity feedback.

Correlation screening, FFT fusion, and acceleration-guided neural processing reduce PPG noise and improve vital sign detection accuracy.

Wearables combine temperature sensing and Bluetooth proximity data to scale employee infection screening with remote risk assessment and alerts.

Sensor-equipped cane measurements and regression-based sway estimation enable continuous quantitative fall-risk assessment beyond the clinic.