Augmented Movelet Method for Activity Classification Using Smartphone Gyroscope and Accelerometer Data.

Emily J Huang,Jukka-Pekka Onnela

doi:10.3390/s20133706

Abstract

Physical activity, such as walking and ascending stairs, is commonly used in biomedical settings as an outcome or covariate. Researchers have traditionally relied on surveys to quantify activity levels of subjects in both research and clinical settings, but surveys are subjective in nature and have known limitations, such as recall bias. Smartphones provide an opportunity for unobtrusive objective measurement of physical activity in naturalistic settings, but their data tends to be noisy and needs to be analyzed with care. We explored the potential of smartphone accelerometer and gyroscope data to distinguish between walking, sitting, standing, ascending stairs, and descending stairs. We conducted a study in which four participants followed a study protocol and performed a sequence of activities with one phone in their front pocket and another phone in their back pocket. The subjects were filmed throughout, and the obtained footage was annotated to establish moment-by-moment ground truth activity. We introduce a modified version of the so-called movelet method to classify activity type and to quantify the uncertainty present in that classification. Our results demonstrate the promise of smartphones for activity recognition in naturalistic settings, but they also highlight challenges in this field of research.

Highlights

Many researchers have recently advocated for a more substantial role for large-scale phenotyping as a route to advances in the biomedical sciences
We extended the movelet method to smartphone accelerometer and gyroscope data for the purpose of distinguishing between walking, ascending stairs, descending stairs, standing, and sitting
We developed a new extension to the method for assessing the accuracy of the activity classification at each timepoint

Summary

Introduction

Many researchers have recently advocated for a more substantial role for large-scale phenotyping as a route to advances in the biomedical sciences. Of the many different phenotype classes, precise capture of social, behavioral, and cognitive markers in naturalistic settings has traditionally presented special challenges to phenomics because of their temporal nature, contextual dependence, and lack of tools for measuring them objectively. Smartphones have at least three distinct advantages compared to other approaches to social, behavioral, and cognitive phenotyping: (1) the availability of these devices makes it possible to implement large studies without requiring additional subject instrumentation; (2) reliance on sensor data makes the process unobtrusive and poses no burden on the subject, making long-term followup possible; and (3) the combination of the previous two factors makes it possible, at least in principle, to obtain these markers prospectively from a cohort of interest at a low cost. In psychiatry, activity monitoring can be used to assess

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Conclusion