  The use of observed wearable sensor data (e.g., photoplethysmograms [PPG]) to
infer health measures (e.g., glucose level or blood pressure) is a very active
area of research. Such technology can have a significant impact on health
screening, chronic disease management and remote monitoring. A common approach
is to collect sensor data and corresponding labels from a clinical grade device
(e.g., blood pressure cuff), and train deep learning models to map one to the
other. Although well intentioned, this approach often ignores a principled
analysis of whether the input sensor data has enough information to predict the
desired metric. We analyze the task of predicting blood pressure from PPG pulse
wave analysis. Our review of the prior work reveals that many papers fall prey
data leakage, and unrealistic constraints on the task and the preprocessing
steps. We propose a set of tools to help determine if the input signal in
question (e.g., PPG) is indeed a good predictor of the desired label (e.g.,
blood pressure). Using our proposed tools, we have found that blood pressure
prediction using PPG has a high multi-valued mapping factor of 33.2% and low
mutual information of 9.8%. In comparison, heart rate prediction using PPG, a
well-established task, has a very low multi-valued mapping factor of 0.75% and
high mutual information of 87.7%. We argue that these results provide a more
realistic representation of the current progress towards to goal of wearable
blood pressure measurement via PPG pulse wave analysis.
