필봇(Feelbot): 실시간 감정 분석 및 회복 인터페이스

In telemedicine or mental healthcare environments, it is difficult to understand a patient's emotional state, and there is a high risk of missing signs of crisis. Existing video consultations or questionnaires are subjective and provide delayed responses, making it difficult to know a patient's emotional changes in real-time. In particular, the demand for non-face-to-face mental health management has increased since COVID, but there is a lack of adequate technical support.

PhilBot collects emotional signals in real-time, such as facial expressions, heart rate, and voice tone, using IoT sensors like cameras and wearables. An AI emotion analysis engine processes this data in real-time to quantify stress or anxiety levels and immediately provides customized breathing exercises, music, and counseling messages tailored to the situation.

For example, if a patient shows signs of anxiety, a remote calming protocol can be executed, or an alert can be sent to the person in charge. Unlike existing text questionnaires, real-time automatic intervention is possible, which differentiates it by managing the patient's condition even in the absence of a psychiatrist or counselor. It also has the advantage of predicting individual triggers based on accumulated emotional data.

B2B adopters include remote healthcare service providers and hospitals (psychiatric departments), and it can also be used in mental health programs in schools and companies (B2B). Ultimately, individual users (B2C) can also subscribe in the form of a mental health management app.

Global application is easy because it is based on facial expressions and bio-signals without language barriers. In addition to mental health, it can be expanded to emotion AI for customer centers and remote monitoring of dementia patients. However, the speed of introduction into the medical field varies depending on whether medical device certification is obtained, and compliance with regulations is required due to privacy issues (collection of video and audio data).

It attracted attention at the CES showcase because it is a field with great social need, but many view it as still in the proof-of-concept stage. There is a lack of market verification regarding AI precision and clinical effects, so there is some caution rather than excessive expectations. The technical implementation itself is impressive, and the potential to become a key part of the therapy market in the future is positively evaluated.