Improving Wearable Activity Sensors using Hidden Markov Models for Outpatient Physical-Therapy Applications
Session Number
F04
Advisor(s)
Mark Albert, Loyola University Jessie Pichleap, Loyola University
Location
A-115
Start Date
28-4-2016 9:50 AM
End Date
28-4-2016 10:15 AM
Abstract
The purpose of this investigation is to improve the accuracy of the software used in recognizing patient activities in physical therapy in order to allow for the collection of objective and continuous activity data in a manner which is convenient for the subject. Patients’ activities are traditionally predicted simply using static classifiers such as Support Vector Machines (SVMs). Studies have shown the static classifiers are prone to mistakes which an additional model can correct, a Hidden Markov Model (HMM). In our experiments, the accuracy of the activity recognition software was determined using data containing patient’s movements-such as walking, running, sitting, etc. Our results have confirmed this hypothesis, with an increase to 86% accuracy with the HMM vs. 84% accuracy without. Due to the success of the model, doctors will be able to access unbiased and continuous data regarding how patients go about their prescribed activities without having to meet with them regularly or rely on inaccurate patient reports. By using HMMs to correct mistakes made by static classifiers analyzing activity recognition data doctors will have more accurate profiles of patients’ activities as well as the patient’s reactions to treatments while increasing their comfort and freedom.
Improving Wearable Activity Sensors using Hidden Markov Models for Outpatient Physical-Therapy Applications
A-115
The purpose of this investigation is to improve the accuracy of the software used in recognizing patient activities in physical therapy in order to allow for the collection of objective and continuous activity data in a manner which is convenient for the subject. Patients’ activities are traditionally predicted simply using static classifiers such as Support Vector Machines (SVMs). Studies have shown the static classifiers are prone to mistakes which an additional model can correct, a Hidden Markov Model (HMM). In our experiments, the accuracy of the activity recognition software was determined using data containing patient’s movements-such as walking, running, sitting, etc. Our results have confirmed this hypothesis, with an increase to 86% accuracy with the HMM vs. 84% accuracy without. Due to the success of the model, doctors will be able to access unbiased and continuous data regarding how patients go about their prescribed activities without having to meet with them regularly or rely on inaccurate patient reports. By using HMMs to correct mistakes made by static classifiers analyzing activity recognition data doctors will have more accurate profiles of patients’ activities as well as the patient’s reactions to treatments while increasing their comfort and freedom.