Human Activity Recognition with Wearable Sensors

Architecture

§ Data Signals [1]

Minh Nguyen, Liyue Fan, Cyrus Shahabi Integrated Media Systems Center University of Southern California

Classifier Method

Related Research §  [1] O. D. Lara and M. A. Labrador. A survey on human activity recognition using wearable sensors. IEEE Communications Surveys & Tutorials 15(3), pp.1192-1209. 2013; 2012.DOI:10.1109/SURV.2012.110112. 00192. §  [2] J. Parkka, J. Parkka, M. Ermes, M. Ermes, P. Korpipaa, J. Mantyjarvi, J. Peltola and I. Korhonen. Activity classification using realistic data from wearable sensors. IEEE Transactions on Information Technology in Biomedicine 10(1), pp. 119-128. 2006. . DOI: 10.1109/TITB.2005.856863.

Introduction §  Motivation:

+ The exceptional development of wearable sensors/devices

+ Human interaction with the devices as part of daily living

+ Human activity data analysis + Useful healthcare services

§  Devices’ Accelerometers & Machine Learning algorithms to recognize locomotion type §  Providing users with human performance status

Conclusion & Future Work

n Evaluation for each classification method: Activity Confusion/Overall Accuracy

n Application for Healthcare Informatics

IMSC Retreat 2015

Communication

Sen

sor Acceleration 3-axis accelerometer

Environmental Attribute

Light, temperature, noise, location

Physiological Signal

Heart rate, respiration rate, galvanic skin response

Data Collection

Data Preprocessing

Feature Extraction Classifier Recognition

Result

Integration Storage

Human Activity Data Signals

Data Segmentation

Mean, Standard Deviation,

Energy, etc.

Decision Tree, NB, kNN, etc.

Walk, sit, stand, lie,

etc.

Decision Tree [2] K-nearest Neighbors

Naïve Bayesian Support Vector Machine

+ Activity A - Features Xi + Independence assumption between features

+ Calculating distances between points + Finding k nearest feature points of feature points

+ Decision Tree (C4.5, ID3) + Root 1: Input (extracted features) + Node 1, 2, 3, 4, 5, 6: Based on the value of the features, the nodes decide which activity is labelled

+ SVM finds the hyperplane that maximizes the margin between the data points

Other Methods

+ Neural Networks, HMM, Fuzzy Basis Function