NutriTrack consists of a non-invasive wearable device that employs bioelectrical impedance analysis (BIA) and near-infrared spectroscopy (NIRS) to estimate the levels of key micronutrients in the body.

These measurements are transmitted to a smartphone app that uses AI algorithms to analyze the data in real-time.

The app provides personalized dietary recommendations, tracks historical nutrient data, and alerts users to potential deficiencies.

The system leverages machine learning to improve accuracy over time by correlating user-reported data, such as dietary intake and lifestyle factors, with biometric readings.

NutriTrack allows for continuous micronutrient monitoring without the need for invasive tests, offering users real-time insights into their nutritional health.

This proactive approach empowers individuals to make informed dietary adjustments and improve their well-being.

Unlike sporadic blood tests, NutriTrack provides dynamic feedback and integrates seamlessly into daily health routines, ultimately reducing the risk of deficiencies and associated health complications.

The required technologies, such as BIA and NIRS, are advancing rapidly, making them more cost-effective for consumer applications.

Regulatory approval for a wearable device that monitors health metrics will be necessary, which could be a hurdle.

However, similar health wearables have successfully navigated these processes.

Initial development costs may be high, but the growing interest in health tech and availability of funding in this sector offer avenues for financial support.