Building the future of medicine, one data point at a time
Detect and monitor health conditions and changes in physiology using our range of custom biomarkers, all collected via Empatica’s medical-grade wearable sensors.Talk to our team
Pioneering new approaches in clinical trials and healthcare
Empatica’s biomarkers and wearables are already being used in hundreds of studies to continuously collect health information from patients and study participants, and indicate changes in their health status. This information is enabling clinicians, patients and researchers to gain an unprecedented understanding of human health, provide better care, and advance research behind treatments.
Imagine a world where patients don’t have to do the hard work
Support continuous measurements outside the physical confines of the clinical environment
Biomarkers collected using advanced sensors can provide additional accuracy and objective data without burdening the patient with additional self-reports
Complete patient view
Receive an immediate, complete view of every patient’s disease progression and response to treatments
Custom biomarkers can enable a deeper understanding and improved care for dozens of chronic conditions
We develop accurate physiological and behavioral biomarkers to improve healthcare diagnostics via continuous, passive and unobtrusive health monitoring that fits in everyday life
Vital signs and physiology
Equipped with a custom PPG, EDA sensors, gyroscopes, accelerometers and a peripheral thermometer, our wrist-worn devices have been designed to accurately and objectively observe physiological biomarkers 24 hours a day:
- Pulse Rate
- Ventral EDA
- Peripheral temperature
- Oxygen saturation
Our custom biomarkers are built in partnership with some of the world’s top institutions, and are validated in a clinical setting
Uses Electrodermal Activity and motion to automatically detect Generalized Tonic Clonic Seizures (GTCS) and provides insight into autonomic arousal following each seizure event, which has been found to correlate with Post-ictal Generalized EEG Suppression (PGES), an important biomarker for SUDEP.
Activity/motion and EDA changes when a seizure occurs.
Actigraphy-based rest detection
Provides objective measures of rest quality, such as efficiency, fragmentation and movements, within the onset and offset of the identified rest period. Detects the boundaries of periods of rest based on the quantity of movement extracted from the accelerometer sensor.
Fragmentation while a person is resting, quantity of movement extracted from the accelerometer.
Provides insight into the autonomic activity and brain dynamics during sleep through non-invasive monitoring of physiological signals overnight, and provides an overview on sleep architecture composed by light sleep (NREM 1/2), deep sleep (NREM 3), REM and wake stages.
Brain activity and movement during sleep. An overview of sleep stages.
Validation in collaboration with: Brigham and Women's Hospital Boston (2015- ongoing) Outpatient study 50+ older adults (E4+ PSG).
Boston Children's Hospital (Ongoing) Inpatient study 20+ children (E4 + PSG).
Detects and analyzes movements to determine physical activity intensity and compute distances covered during daily movements. Automatically recognizes number of steps and distinguishes between low intensity and high intensity activity, such as walking, running or other sports.
Analysis of movement (intensity, duration) while doing physical activity (sports, running, walking, etc).
Detects autonomic arousal in response to external stimuli and provides insight about the sympathetic nervous system by monitoring EDA and temperature changes/trends/values. Used in collaboration with NEC on a study involving 300 subjects in a large-scale effort to measure and combat stress in the workplace.
Autonomic arousal and temperature responses to external stimuli.
Provides insight into physiological signals associated with activity, rest, and emotional responses against Hamilton Depression Rating Scale Score (HDRS) changes. Detects both short-term and long-term patterns that enable identifying triggers and provide accurate forecasts for depression. Original research conducted by Empatica’s Chief Scientist Rosalind Picard at MIT with Empatica technology.
Accurate monitoring of changes in gold standard depression scores (HDRS) over a typical 8-week treatment period.
Predicted vs. Actual HDRS vales: Pearson correlation 0.834, p< 0.0001
Used in detecting digital phenotypes related to suicidal thoughts, measuring skin conductance levels to assess changes in physiological distress.
Study conducted by Harvard University Clinical Psychologist Dr. Matthew K. Nock involving hundreds of individuals wearing Embrace2.
Researchers at Duke University used the E4 in a study to develop a model for predicting and determining the severity of viral respiratory infections before the onset of symptoms. Results showed accuracies of up to 92% in predicting H1N1 and 88% for rhinovirus 1 , with the model also determining the severity of the infections. This innovation will contribute to preventing the spread of infections such as the common cold and the flu, with the possibility of being applied to SARS-CoV1.
Our autonomic measures are relevant to a range of medical conditions that affect the lives of billions
- Affect dysregulation
- Alzheimer's disease
- Anxiety disorder
- Attention deficit and hyperactivity disorder
- Autism disorders
- Endocrine disorders
- Hot flashes
- Post-traumatic stress disorder
- Sensory processing and modulation
- Sexual dysfunction
- Sleep disorders
- Oppositional defiant disorder
- Bipolar disorder
- Cystic fibrosis
- Pain management
- Parkinson's disease
- Temporary paralysis
- Thyroid disfunction
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