Shenzhen Bi’an Mind Technology, founded in 2021, develops emotion recognition algorithms and smart wearable tech. The company combines physiological signals with AI to bring emotion-sensing capabilities directly into devices, while also exploring uses in healthcare, education, and high-risk industries.
At its core, the approach is straightforward: use physiological data to continuously track and analyze people’s emotional states.
Core technology: emotion recognition based on autonomic nervous signals
The core technology of Bi’an Mind lies in its self-developed AI emotion recognition algorithm. The approach is based on the principles of sympathetic and parasympathetic nervous system activity.
By collecting physiological signals such as heart rate variability (RRI) and breathing rate, and combining them with the Valence-Arousal model, the algorithm can classify and assess basic emotional states such as anger, sadness, anxiety, and happiness.
Compared with emotion recognition methods that rely on facial expressions or voice features, this approach emphasizes physiological data, which in theory can reduce interference caused by cultural differences or deliberate control of expressions.
It allows for more discreet and continuous monitoring. However, its accuracy, generalizability, and adaptability across different populations still need to be validated in large-scale real-world scenarios.
Product forms — combining algorithms and hardware
Building on its algorithm capabilities, the company has launched several hardware and system products. The smart emotion watch features an RRI monitoring module that tracks emotional states, stress, and energy levels around the clock to generate visual reports.
It also integrates tools such as mindfulness exercises and psychological assessments, creating a closed loop of monitoring, intervention, and feedback. Key challenges for these products include the long-term stability of emotion data, users’ willingness to consistently engage with their psychological data, and the ability to quantify the effectiveness of interventions.
The emotion screening and early warning system, which targets healthcare institutions, educational organizations, and high-risk industries, offers tools for group-level emotion analysis. It generates metrics such as stress indices and autonomic nervous system balance reports and provides real-time alerts when abnormal fluctuations occur.
In practice, these systems function more as decision-support tools, with their value depending on compliant data collection, privacy safeguards, and the ability of administrators to interpret and act on the insights.
Industry applications and implementation scenarios
Bi’an Mind has taken its emotion recognition technology into several specialized fields. In early childhood education and general teaching, wearable devices monitor teachers’ emotional fluctuations, which can inform training programs and help optimize job assignments.
For high-risk industries such as nuclear power and mining, the technology tracks workers’ fatigue and stress levels, providing additional data to support safety management decisions.
In healthcare and mental health, the company partners with psychological service platforms to offer long-term data tracking for patients with depression and anxiety, helping clinicians assess treatment outcomes.
Because emotion data is highly sensitive in both healthcare and safety settings, these applications relies on carefully balancing algorithm performance, regulatory compliance, and ethical considerations.
From supportive feature to core human-computer interaction
Looking at broader technology trends, emotion recognition is shifting from a purely supportive function to a central element of human-computer interaction. Bi’an Mind’s approach, which leverages physiological data modeling, highlights this evolution.
The ultimate impact of such systems will depend not only on algorithm accuracy but also on how industries collectively understand the value of emotional data and the limits of its use.
