Accelerometry-based Recognition of the Placement Sites of a Wearable Sensor

Overview

Journal Pervasive Mob Comput

Publisher Elsevier

Date 2015 Jul 28

PMID 26213528

Citations 11

Authors

Andrea Mannini

Angelo M Sabatini

Stephen S Intille

Affiliations

Soon will be listed here.

Abstract

This work describes an automatic method to recognize the position of an accelerometer worn on five different parts of the body: ankle, thigh, hip, arm and wrist from raw accelerometer data. Automatic detection of body position of a wearable sensor would enable systems that allow users to wear sensors flexibly on different body parts or permit systems that need to automatically verify sensor placement. The two-stage location detection algorithm works by first detecting time periods during which candidates are walking (regardless of where the sensor is positioned). Then, assuming that the data refer to walking, the algorithm detects the position of the sensor. Algorithms were validated on a dataset that is substantially larger than in prior work, using a leave-one-subject-out cross-validation approach. Correct walking and placement recognition were obtained for 97.4% and 91.2% of classified data windows, respectively.

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