Health-Index Calculation TRIZ Cases

See how a food providing system uses feedback and self-service principles to balance nutrition,

See how an IoT-connected mattress topper uses AI, pressure sensors, and physiological monitorin

See how a toilet seat integrates ECG, pulse, weight, and temperature sensors to enable simultan

See how a toilet seat sensor uses wavelet transform to detect stable resting states, ensuring a

See how toilet-integrated force, ECG, and PPG sensors enable passive health monitoring and user

See how automated toilet usage tracking replaces memory-based tests to enable passive cognitive

A central bed controller uses sleep-state feedback to automatically adjust mattress firmness, position, and temperature for personalized comfort.

LED lamp modules show whether brakes, bed height, and siderails meet fall-risk safeguards, giving caregivers clear bed safety status.

By combining predicted head motion with recovery factors, this case improves vehicle motion sickness prediction and avoids unnecessary restrictions.

Sensor-driven vibrotactile and audio therapies detect anxiety or low alertness in vehicle occupants and trigger targeted in-cabin intervention.

By combining vital signs with state information, this case predicts low or high arousal early enough to warn users before vehicle behavior degrades.

Travel history screening flags high-risk riders and triggers disinfection, alerts, or scheduling changes to reduce infection spread in shared vehicles.

A central hub unifies data from mixed medical devices and auto-updates display layouts to cut manual entry, clutter, and alarm inconsistency.

Capacitive noise sensing and Rogowski coil signals enable passive integrity checks in GFCI and AFCI interrupters without added test circuitry.

Hair-based mineral and heavy metal analysis cuts pet health screening cost while guiding diet and risk monitoring with ML-based recommendations.

Aligning micro-photoelectric units with shielding units enables accurate biometric sensing in pixel gaps without disrupting OLED display output.

Centralizing data from board-in-cable and external medical devices helps reduce display clutter, duplicate entry, and caregiver distraction.

Combining blink parameters, eye-state networks, and context improves drowsiness estimation across users and changing conditions.

By aligning micro-photoelectric units with shielding gaps, the layout improves under-screen biometric and physiological detection without disrupting display pixels.

Weighted blink and eye-state networks use temporal context and user patterns to improve drowsiness estimation across varying conditions.

Wireless medical data is routed into vehicle audio and visual alerts so drivers can react sooner without checking a wearable display.

Multiple driver and vehicle data streams are used to predict mindfulness levels and trigger personalized countermeasures before stress affects driving.

Detects sudden physical condition changes by correlating heart rate and blood pressure shift timing from biosignal waveforms.

A monitoring hub shares patient data through a medical device cable to unify displays, automate synchronization, and reduce caregiver distraction.

A stacked micro-photoelectric layout and polarizer improve biometric and physiological sensing accuracy while avoiding pixel shielding.

A counter-based eye-state monitor bridges brief eye openings and sensor errors to trigger timely, reliable microsleep warnings.

A bidirectional hub and board-in-cable centralize data from disparate medical devices and optimize display layouts to reduce caregiver distraction.

Personalized rest prompts use live body signals and driver-specific learning to improve timing accuracy without causing unnecessary delays.

AI and IoT automate smoke compartment risk scoring and asset control, cutting manual ILSM inspections while maintaining compliance.

Continuous patient sensing triggers a second sensor to switch states when abnormalities appear, enabling timely detection with less cross-infection risk.

Fusing imaging, EHR, and biomarker data helps quantify aleatoric uncertainty in ambiguous assessments and improve clinical decisions.

Normalized analyte indicators are plotted as target and reference polygons so users can compare health status over time without expert interpretation.

By combining UV index and pollutant levels into a geolocation-specific metric, this case enables more accurate skin exposure guidance.

Real-time physiological and environmental sensing adjusts dive exposure limits to match individual respiratory stress and improve safety.

Visual glucose reporting combines median trends, variability, and low-glucose likelihood to guide faster hypoglycemia treatment decisions.

A stretchable mesh with partial sensors captures 3D shape change data for comfortable, durable erectile function measurement.

Gut bacteria DNA profiles and metabolic pathway scoring enable earlier, non-invasive ASD risk assessment in young children.

Sensor-tracked inhaler use and environmental data predict asthma rescue risk early, enabling timely alerts that help prevent exacerbations.

An intraocular transponder coil tracks eye movement to measure visual field boundaries objectively, reducing clinic dependence and patient effort.

User-linked encryption records let a measurement device manage limited pairing storage without unintended key deletion across terminals.

Real-time visual, physiological, and environmental sensing improves migraine risk assessment accuracy while enabling timely alerts.

Machine learning triggers biochemical sensor deployment only when decompensation risk rises, improving heart failure monitoring accuracy.

Real-time UV sensing and body-area risk mapping personalize exposure limits and alert users before overexposure occurs.

Stored high-precision reference values correct simplified BIA measurements, improving body composition accuracy without complex repeat scans.

Wearable and environmental data drive wakefulness detection so apps can mute or limit notifications during sleep and restore access when awake.

Depth and thermal sensing automate patient zone and hygiene monitoring, improving accuracy, scalability, and real-time clinical oversight.

Quadrant-based OCT retinal thickness analysis quantifies rTSD and links visual pathway degeneration to the affected brain hemisphere.

A variable-length light strip guides running pace and lap tracking, improving group test reliability in noisy settings and for hearing-impaired users.

Combining FRI scores with fetal base excess trends improves early neurological injury risk prediction so intervention can begin before labor damage escalates.

Blood amino acid concentrations are used to objectively assess livestock health, growth potential, stress, appetite, and feed efficiency.

HRV events and lifestyle context are linked through causal inference to identify acute stressors behind maladaptation risk.

Multiple analyte indicators are normalized into polygon patterns, making status comparison readable without professional interpretation.

Automated CT-based cardiac volumetry replaces manual measurements to identify early atrial fibrillation and heart failure risk more reliably.

Tumor mutation profiling helps identify bladder cancer patients more likely to respond to PD-L1 antibody therapy, improving treatment selection.

Machine-learned ECG time-frequency features estimate echocardiography diastolic parameters, improving screening without complex ultrasound.

Comet-shaped GUI icons show vital sign direction and rate of change, helping clinicians spot deterioration quickly without scanning EMRs.

Worksheet-based screening checks motor, language, speech, and social-emotional deficits early to guide referrals before reading failure.

SNP-based nutrient profiling tailors fortified food to individual intake gaps, improving nutritional effectiveness without full genetic analysis.

An inside-container code confirms medication access, enabling symptom-linked dose adjustment and better adherence tracking.

Combining external safety, demographic, and wellness data enables normalized facility benchmarking and clearer identification of medical liability risks.

Machine learning on SNP and multi-omics data improves lupus phenotype classification despite heterogeneous high-scale disease records.

Motor impairment detection during neuropsychological testing enables workflow changes and score adjustment to preserve cognitive assessment accuracy.

Phased attention, associative, and integrative training on a user terminal improves cognitive assessment and supports mild impairment care.

Real-time carbohydrate and medicament-on-board updates help automated delivery personalize faster and improve short-term glucose control.

Surface electrodes combine EMG monitoring and uterine stimulation to identify postpartum hemorrhage risk early and trigger adaptive contraction support.

Reference models from longitudinal biopsies enable reliable aneuploidy-based ITH scoring from one tumor sample for prognosis and therapy resistance prediction.

Gene expression clustering separates lupus nephritis into molecular endotypes, improving disease stage classification and therapy targeting.

Noninvasive brush biopsy images and biomarker data are analyzed with AI to score early oral cancer risk and distinguish benign from malignant lesions.

Continuous pressure and motor-signal analysis estimates LVEDP and contractility, enabling timely support adjustment and heart recovery weaning.

Distributed immunity credentials, biometrics, and secure tokens enable real-time access checks without exposing full patient health data.

Voice-responsive avatars guide registration and vital sign monitoring to improve user comfort while reducing latency in sensitive clinical workflows.

Multi-gene mutation profiles classify likely CPI response or resistance in lung cancer, improving patient selection beyond TMB and PD-L1.

Contractility transients in cardiomyocytes reveal pro-arrhythmia and inotropic risk without complex electrophysiology, improving drug safety screening.

Longitudinal ctDNA and time-to-event modeling improves tumor signal differentiation and survival prediction from non-invasive cancer monitoring.

Sensor-based tracking of patient movement on beds or stretchers generates objective mobility scores for more accurate pressure ulcer risk assessment.