Exploiting photoplethysmography signals (PPG) for non-invasive blood pressure (BP) measurement is interesting for various reasons. First, PPG can easily be measured using fingerclip sensors. Second, camera based approaches allow to derive remote PPG (rPPG) signals similar to PPG and therefore provide the opportunity for non-invasive measurements of BP. Various methods relying on machine learning techniques have recently been published. Performances are often reported as the mean average error (MAE) on the data which is problematic. This work aims to analyze the PPG- and rPPG based BP prediction error with respect to the underlying data distribution. First, we train established neural network (NN) architectures and derive an appropriate parameterization of input segments drawn from continuous PPG signals. Second, we use this parameterization to train NNs with a larger PPG dataset and carry out a systematic evaluation of the predicted blood pressure. The analysis revealed a strong systematic increase of the prediction error towards less frequent BP values across NN architectures. Moreover, we tested different train/test set split configurations which underpin the importance of a careful subject-aware dataset assignment to prevent overly optimistic results. Third, we use transfer learning to train the NNs for rPPG based BP prediction. The resulting performances are similar to the PPG-only case. Finally, we apply different personalization techniques and retrain our NNs with subject-specific data for both the PPG-only and rPPG case. Whilst the particular technique is less important, personalization reduces the prediction errors significantly.

Despite numerous available treatment strategies, the management of complex proximal humeral fractures remains demanding. Impaired bone quality and considerable comorbidities pose special challenges in the growing aging population. Complications after operative treatment are frequent, in particular loss of reduction with varus malalignment and subsequent screw cutout. Locking plate fixation has become a standard in stabilizing these fractures, but surgical revision rates of up to 25% stagnate at high levels. Therefore, it seems of utmost importance to select the right treatment for the right patient. This article provides an overview of available classification systems, indications for operative treatment, important pathoanatomic principles, and latest surgical strategies in locking plate fixation. The importance of correct reduction of the medial cortices, the use of calcar screws, augmentation with bone cement, double-plate fixation, and auxiliary intramedullary bone graft stabilization are discussed in detail.