Citation

Bharti P, Panwar A, Gopalakrishna G, Chellappan S. IEEE J. Biomed. Health Inform. 2018; 22(3): 686-696.

Copyright

(Copyright © 2018, Institute of Electrical and Electronics Engineers)

DOI

10.1109/JBHI.2017.2692179

PMID

28410113

Abstract

In a 2012 survey, in the United States alone, there were more than 35; 000 reported suicides with approximately 1; 800 of being psychiatric inpatients. Recent CDC (Centers for Disease Control and Prevention) reports indicate an upward trend in these numbers. In psychiatric facilities, staff perform intermittent or continuous observation of patients manually in order to prevent such tragedies, but studies show that they are insufficient, and also consume staff time and resources. In this paper, we present the Watch-Dog system, to address the problem of detecting self-harming activities when attempted by in-patients in clinical settings. Watch-Dog comprises of three key components - data sensed by tiny accelerometer sensors worn on wrists of subjects; an efficient algorithm to classify whether a user is active vs. dormant (i.e., performing a physical activity vs. not performing any activity); and a novel decision selection algorithm based on random forests and continuity indices for fine grained activity classification. With data acquired from 11 subjects performing a series of activities (both self-harming and otherwise), Watch-Dog achieves a classification accuracy of 98%, 94% and 70% for same-user 10-fold cross-validation, cross-user 10-fold cross-validation and cross-user leave-one-out evaluation respectively. We believe that the problem addressed in this paper is practical, important and timely. We also believe that our proposed system is practically deployable, and related discussions are provided in this paper.

Language: en