Autonomous learning approach to characterizing motion behavior

Abstract

Unattended falls can lead to serious medical issues among the elderly, especially when motor functions may become inactive. Motion sensors like accelerometer can aid in automatic characterization and classification of human motion. Un-Classified motion can be accounted for anomaly that when reported to the online knowledge builder can correct the existing model or estimate additional classes into that model. In this paper we develop an alert system using low power Intel Quark D1000 MCU that characterizes the motion behavior using Logistic Model Trees (LMT) and estimates an anomaly in motion behavior while augmenting the model using online learning. The goal is to build a useability model where an unclassified behavior (corresponding to accelerometer data) can be logged and upon additional intervention can be re-evaluated for re-classification. This will lead to autodetecting un-desired motion activities (like falls) and avoid false positives to activities of daily life.