  Activity monitors are widely used to measure various physical activities (PA)
as an indicator of mobility, fitness and general health. Similarly, real-time
monitoring of longitudinal trends in step count has significant clinical
potential as a personalized measure of disease related changes in daily
activity. However, inconsistent step count accuracy across vendors, body
locations, and individual gait differences limits clinical utility. The
tri-axial accelerometer inside PA monitors can be exploited to improve step
count accuracy across devices and individuals. In this study, we hypothesize:
(1) raw tri-axial sensor data can be modeled to create reliable and accurate
step count, and (2) a generalized step count model can then be efficiently
adapted to each unique gait pattern using very little new data. Firstly,
open-source raw sensor data was used to construct a long short term memory
(LSTM) deep neural network to model step count. Then we generated a new, fully
independent data set using a different device and different subjects. Finally,
a small amount of subject-specific data was domain adapted to produce
personalized models with high individualized step count accuracy. These results
suggest models trained using large freely available datasets can be adapted to
patient populations where large historical data sets are rare.
