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Sensia turns heart rate, respiration rate, sleep patterns and other wearable signals into stress, cognitive and emotional states. One API call, real-time results.
Interactive illustration: move the inputs to see how the outputs respond. To see the request format, try the Playground or read the documentation.
Developed from neuroscience and data science research at Politecnico di Milano.
REST API. Send signals, get JSON back. Works with any stack.
POST https://api.sensia.bio/predict { "current_session": { "HR_day_1h": [68, 73, 71, 75, 78, 74, 79, 73, …], "RR_day_1h": [17, 18, 20, 17, 16, 22, 21, 19, …], "HR_night": [57, 58, 53, 53, 54, 59, 54, 57, …], "RR_night": [15, 15, 15, 16, 14, 14, 15, 13, …], "sleepTimeSeconds": 25200, "deepPercentage": 18.5, "remPercentage": 22.3, "lightPercentage": 59.2, "awakeSleepSeconds": 900, "deepSleepSeconds": 4496, "lightSleepSeconds": 14386, "remSleepSeconds": 5419, "age": 28, "height_cm": 175, "weight_kg": 72, "sex": "NULL" } }
{
"inference_time_ms": 1.216,
"stress": 34,
"memory": 42.5,
"reasoning": 49.8,
"attention": 58.8,
"C_SCORE": 50.4,
"valence": "High",
"arousal": "Low"
}
Sensia turns the physiological data your trial already collects, heart rate, respiration, sleep and recovery, into digital biomarkers of cognition, emotion, stress and treatment response. Exploratory, not diagnostic. Built to run alongside your existing protocol.
We don’t add another device to the trial. We make the data sponsors already collect more valuable.
Scales, questionnaires and clinician assessments capture how a patient is doing at a handful of discrete time points. The physiology sponsors already collect is continuous, and most of it goes untranslated into anything interpretable about mental state or treatment response.
Sensia sits between the physiological data your trial already collects and the decisions your team has to make. It is a software biomarker layer, not a new data source.
Three exploratory questions a retrospective analysis can start to answer.
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The value isn't more data. It's reduced uncertainty, reached earlier in a program.
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Each answer narrows uncertainty for the next development decision.
An illustration of the kind of structure exploratory analysis can reveal: patients who share a diagnosis may not look the same physiologically, and distinct patterns can emerge from the signal alone.
The chart and phenotypes shown here are illustrative. Phenotypes are patterns in physiological data, not diagnoses and not a validated clinical classification.
Observe, explain, predict, select: a path we walk only as far as the evidence takes us.
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Exploratory insight today. Validated stratification and enrichment only as the evidence supports them.
Ambition, paced by the evidence each step actually requires.
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Sensia’s clinical insights are exploratory today: they are not diagnoses and not a validated clinical classification. We follow a staged path. Independent validation on public datasets is underway. Clinical validation, a pharma retrospective pilot and a prospective shadow study are planned. We would consider validated stratification or enrichment only after sufficient evidence accumulates across those steps.
Every plan includes the full prediction API. Start for free, and upgrade as your request volume grows.
Sensia grew out of neuroscience and data science research at Politecnico di Milano.
Illustrative simulation to explore how Sensia reads physiological signals. For the real API, see the documentation ↗
Looking for the technical version? The technical playground with the real api call and Json contracts, is available at playground.sensia.bio ↗
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