A. provides a personalization layer that transforms raw wearable sensor data into individualized human‑state intelligence, enabling accurate stress measurement for each user.
Funding
$50K raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Wearable devices typically deliver generic stress scores that are calibrated for the average user, resulting in low accuracy and limited usefulness for individuals who need reliable human‑state intelligence. The lack of on‑device personalization also raises privacy concerns, as raw sensor data is often transmitted to external services.
Solution
A.R.I.A. provides a personalization layer that sits between raw wearable sensor streams and downstream applications, converting the data into individualized stress measurements. A five‑minute guided calibration session adds approximately ten percentage points to lab‑grade accuracy, raising it from around 56 % to over 66 % for each user. All processing occurs on the device, ensuring that personal data never leaves the user’s hardware. The API delivers explainable and auditable results, making the reasoning behind each stress score transparent. It is hardware‑agnostic, supporting any wrist‑worn sensor and validated on consumer devices such as Samsung wearables. Additional optional voice analysis can differentiate emotional states like happiness and sadness, while a longitudinal “emotional map” aggregates personalized insights over time.
Target Audience
Primary customers are wearable manufacturers, health‑tech developers, and enterprise wellness platforms that require accurate, privacy‑preserving stress analytics for their users.
Features
- Five‑minute guided calibration that boosts stress detection accuracy by ~10 percentage points per individual
- On‑device computation that keeps raw sensor data private and eliminates the need for cloud transmission
- Hardware‑agnostic API compatible with any wrist‑worn wearable sensor, validated on consumer devices
- Explainable output with audit trails, allowing developers and clinicians to understand why a stress score was generated
- Optional voice‑analysis module (opt‑in) that enriches emotion detection beyond wrist‑only signals
- Longitudinal emotional mapping that builds a unique, user‑specific profile of stress and affect over time
- Model‑agnostic design; performance is consistent across Random Forest, XGBoost, deep‑learning, or other architectures