Combining electrochemical analyte and optical sensors supports single-site monitoring without multiple body locations, simplifying handling and manufacturing.
Serum IgG alone can misclassify treatment need; BCR sequencing adds repertoire diversity, germline identity, and mutation data for IgG-RT decisions.
Stored high-precision measurements correct later low-precision BIA readings, improving the reliability of body water, fat, and muscle data.
Heartbeat-based compensation separates respiratory and cardiogenic components in summed measurements for more accurate ventilation monitoring.
Accelerometers, gyroscopes, and PPG signals correct for posture and hemodynamic variation to produce reliable systolic and diastolic readings.
Using only EMG data, a machine-learning model estimates sleep stages without scalp electrodes, improving comfort and simplifying PSG monitoring.
Separate worksheets assess fine and gross motor skills, sound, language, and social-emotional regulation to guide early referrals.
Cloud analysis turns wearable data into timely health and development insights, reducing manual caregiver reporting.
Standardized sample and metric data structures connect diverse sensors to modular storage, analysis, and caregiver visualization without device-specific programming.