The Research of Alcohol Drinking State Analyzing Based on Smart Watch Data

Activity recognition is mainly used in health care, authentication and many other fields. Activity recognition based on a smart watch usually performs much better than any other means of activity recognition in these areas. With its small size, high integration and multi-function, smart watch holds more advantages over the devices used in image based activity recognition, for example, a monitor, which is usually location fixed and view limited. In this paper, we collected data of five behaviors, i.e. stand, walk, run, upstairs, downstairs from the accelerometer on a smart watch. These data are classified with CART decision tree. Experimental results prove that the proposed method improves the accuracy of activity recognition.

In the theoretical part of this article, we provide a brief introduction to differenttypes of repeated measure designs and methods to analyze repeatedly measured data, with aparticular focus on continuous time modelling of intensive longitudinal data (ILD) withN≥1 analysis. We built on the distinction betweenwithin-person versus between-person effects, and how this is addressed in static versusdynamic models. Further, we elaborate on the distinction between discrete time dynamicmodels versus continuous time dynamic models. In particular, we deal with continuous timestructural equation models (CTSEM), and we provide a brief introduction into theunderlying math. Since smart devices have become useful tools in monitoring health, we usethe applied part of this article for explaining how to retrieveN=1 bivariate ILD from popular smart watches and how toprepare them for CTSEM (including N>1 multivariateextensions). We show how to specify a cross-lagged panel CTSEM using the R package ctsem,how to fit the specified model to the retrieved data, and how to interpret the results.Limitations of CTSEM are discussed, too. Monitoring and forecasting industrial healthrepresent important issues for organizations.