2020/12/11 by Arvind Pillai, Pillai, Arvind, Halsey Lea +6
Computer Science · Engineering · Medicine · #Context-Aware Activity Recognition Systems #FOS: Computer and information sciences #FOS: Electrical engineering #I.2.0 #Machine Learning (cs.LG) #Non-Invasive Vital Sign Monitoring #Physical Activity and Health #Signal Processing (eess.SP) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2012.08975
openalex publication_date 2020/12/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.