Inertial Sensors TRIZ Cases

Integrated sensors and detachable electronics in a foot orthotic enable comfortable, real-time biomechanical monitoring across different shoes.

An implantable monitor applies a pretrained ECG model to track LV dysfunction and ejection fraction continuously without frequent clinic visits.

Template-matched PPG and BCG segments plus deep and morphological features improve wearable cardiac output estimation while limiting power use.

Orientation transformation aligns misaligned earbud IMU data with smartwatch signals to improve multi-wearable activity recognition.

Chest-mounted acceleration sensing detects leg contact timing and front-back gait changes to identify lameness early without stressing the animal.

Motion sensors identify activity type and select a noise reference to filter PPG artifacts, improving heart rate and respiration accuracy.

Short thoracic impedance signals are cleaned with signal quality checks, autocorrelation, and zero-crossing to extract RR and TV despite noise.

A removable wireless battery aligns with a wearable monitor for secure retention, easy replacement, and continuous physiological monitoring.

Wearable sensors and a cloud AI engine predict vertical ground reaction force in real time to correct subtle gait abnormalities outside the lab.

Condition-triggered calibration uses a primary wearable sensor to correct secondary sensors and keep temperature readings accurate over time.

Adaptive notch and comb filtering uses nearby accelerometer motion periods to suppress PPG artifacts and improve wearable heart-rate accuracy.

Interconnected wearable modules and cloud analysis improve sleep tracking while easing multi-device integration and power demands.

Real-time eye and head tracking recalibrates HMD visual field tests to correct tilt and flat-display curvature errors.

Facial video, speech, and motion analysis are combined to speed acute stroke recognition and improve detection accuracy for timely intervention.

BCG and ECG sensing in a wearable tracks apneic episodes and compensatory heart output to support continuous home cardiac risk monitoring.

Synchronized ECG, pulse oximetry, photoplethysmography, and bioimpedance capture enables portable real-time monitoring with long-range wireless data transfer.

A cable-driven handle and effector assembly mimics hand motion to add surgical dexterity without bulky robots or complex control.

Multiple optical channels are ranked by signal quality and power use so wearable sensors can collect physiological data with less energy.

Bio-impedance spectroscopy tracks breast tissue changes to measure milk flow in real time without infant weigh-back or feeding disruption.

Sensor-based posture and angle guidance helps non-medical users align wearable ECG leads correctly for reliable signal acquisition.

Motion sensors classify intrinsic head-wearable movement and compare it with reference patterns to flag early signs of neurodegenerative disorder.

Cardiac activity from optical heart rate sensors aligns dual wearable recordings, correcting clock drift for precise scratching monitoring.

Adaptive heart rate state detection applies selective filtering to suppress motion noise, improving wearable accuracy with lower processing load.

Heart-rate-based measurement switching balances physiological data accuracy and sensor power use to extend wearable battery life.

AV conduction monitoring triggers His-bundle pacing only during intermittent or rate-related block, preserving synchrony while reducing battery drain.

A hand-positioning calibration fixture measures IMU axis offsets and corrects motion-capture data for more accurate limb and joint positioning.

Green-light PPG with adaptive acceleration-based filtering removes motion artifacts in real time using one sensor and simple processing.

Derived signal alignment matches wearable sensor data with motion-capture or force-plate references without APIs or proprietary firmware.

Chest vibration intervals are reconstructed with healthy-subject autoencoders, and correlation scoring helps identify heart failure non-invasively.

Derived acceleration, angular velocity, marker, and force signals align wearable and reference motion data without APIs or firmware coupling.

A frontal accelerometer isolates the cough waveform pattern to detect coughs accurately while reducing processing load and device complexity.

Acceleration-guided adaptive filtering removes motion artifacts from PPG signals in real time, improving heart rate accuracy without multiple sensors.