A New IoT-Edge Accelerator With On-Chip Learning Toward Secured Blood Flow Volume Monitoring Based on PPG and Personalized Models

A hardware accelerator at edge for estimating blood flow volume (BFV) based on photoplethysmography (PPG) from an IoT sensor is successfully designed with on-chip learning for personalization. The success of estimating BFV via PPG helps greatly hemodialysis patients to monitor constantly and ubiquitously the quality of their arteriovenous fistulas or grafts. This accelerator with a BFV estimating algorithm implemented at edge enables faster fine-tuning computation toward favorable accuracy and secure operation without personal biological PPG data transmitted over the fly. The accelerator was built to a chip with a neural network (NN) designed via field programmable gate array (FPGA), consisting mainly of a forward unit for estimating BFV and a backward propagation by an inferencing model. The inferencing is built upon the data collected off-line, and then updated via transfer learning at edge based on additional personal data, i.e., toward personalization of the inference model. In the NN, PPG signal is first filtered through a band-pass filter to remove the noise, while power spectrum density (PSD) and direct current (DC) components are extracted as features to predict BFV. The method of cross-domain clock (CDC) with different clock operations in forward and backward propagations is employed in the accelerator to increase computing efficiency. The results show a significant accuracy increase in predicting BFV from 0.932 by the pretrained model to 0.985 by the personalized model executed in the developed accelerator chip at edge, and superior to all others reported in prior arts.