Development of a dynamic pulsatile phantom for the photoplethysmographic waveform at the radial artery.

Significance: Cardiovascular disease remains one of the leading causes of death in the United States. Wearable optical systems are known to have errors and biases for individuals with different skin tones as well as different levels of obesity. By enabling the development and validation of wearable technologies across diverse populations, we advance equitable healthcare solutions and foster the creation of more reliable, personalized health monitoring systems. Aim: We aim to develop a dynamic wrist phantom replicating the radial artery pulse, addressing physiological variations such as skin tone and obesity that impact wearable health technologies. Approach: A silicone-based phantom mimics human tissues' mechanical and optical properties. A cam-driven pulsatile flow system simulated physiological blood flow, with key waveform features controlled by mechanical components. Optical properties were adjusted using titanium dioxide and carbon black to match Fitzpatrick skin tones I to VI, whereas radial artery depth variations simulated the effects of obesity. The phantom system incorporated a blood-mimicking fluid to replicate the optical absorption characteristics of whole blood. Results: The phantom successfully replicated photoplethysmography (PPG) waveforms at heart rates ranging from 59 to 118 beats per minute, demonstrating physiologically representative features such as systolic and diastolic peaks. Signal degradation was observed with increasing vessel depth and darker skin tones, consistent with real-world challenges in wearable device accuracy. The alternating signal/baseline signal ratio of the PPG signal decreased by up to 77.8% for darker skin tones and deeper vessels. The phantom also validated its performance against commercial wearables, supporting its utility in device testing. Conclusions: This dynamic wrist phantom provides a robust platform for evaluating optical devices under controlled and representative conditions, addressing critical gaps in inclusivity and accuracy.