Challenge

An estimated half a billion people worldwide have a mental illness, and the cost in the United States alone reaches $500 billion a year. Many people benefit from psychological counseling, but gaps remain. Mental health assessment is highly subjective, and the focus on prevention is limited. Additionally, diagnoses are often missed, and there’s little real-time intervention.

Solution

Improving mental health with continuous patient monitoring has the potential to help patients suffering from mental health issues such as anxiety and depression. Sentio’s Feel program is a wristband and phone app that tracks users’ emotional states, offers regular mental and physical exercises, and connects them to a therapist once a week.

• The system uses machine learning and signal processing algorithms developed in MATLAB to identify patterns in data collected from the wearable device, such as combinations of biomarkers that indicate different emotional states.
• Sentio first started with psychology literature describing which physical signals are most indicative of which emotions. Then they fine-tuned the models by asking people wearing the emotion sensor to describe their feelings. The models also classified the readings, and if the emotion labels differed from what the user described, the models updated themselves to work better next time.
• These algorithms are deployed on cloud-based Amazon^® Web Services (AWS) servers that monitor the emotional state of the patient and feed results back to the app.