Towards Mobile Deep Venous Function Assessment: An Algorithmic Approach

Venous function assessments of the lower limb vascular bed are currently exclusively performed in medical offices and hospitals, requiring special equipment and trained personnel. Risk factors such as obesity, prolonged periods of sedentary activity, and older age, combined with rising life expectancy, are likely to contribute to a rising prevalence of deep venous insufficiencies. With potentially life-threatening secondary diseases, high treatment costs, and currently no curative therapy, these may become a growing burden on the health care system and public. Hence, an affordable monitoring system that is unobtrusive in everyday life, easy to use, and able to inform users and medical professionals about acute and prolonged negative deep venous health trends would be highly beneficial to counteract the aforementioned challenges. We therefore propose a novel lightweight algorithm that adapts the Light Reflection Rheography (LRR) using digital photoplethysmogram (dPPG) data collected wirelessly from a wearable sensor. Based on our col- lected test data of 18 subjects with 2 measurements taken per leg, our algorithm achieves an LRR segmentation performance of 86.15 % whilst being able to run on a smartphone. Integrated in our custom mobile app, our approach delivers a deep venous health progression control and critical early-stage information about pathological changes in the deep venous blood flow. Early medical results are consistent, comprehensible, and comparable with a medically performed LRR of one study subject with post-thrombotic deep vein damage, with a larger, more diverse follow-up study containing a medical ground truth planned for future work.