1. BAYESIAN APPROACH FOR INDOOR HUMAN ACTIVITY MONITORING
Gamze Uslu Özgür Altun Şebnem Baydere
Department of Computer Engineering, Yeditepe University
Abstract
The number of elderly people living in the world is increasing day by day. Due to the fact that movement capabilities decrease by age, their lives get harder. In addition to this, there are many disabled people, who need help to improve their life quality standard. Therefore, they have to use additional devices or systems to ease their life.
Activity monitoring plays a crucial role in the lives of disabled and elderly people to provide them assistance. In this study, a system with real time activity monitoring capability is designed and implemented by addressing the two phases (data acquisition and data classification) of the monitoring task. The main focus is recognizing the action of an elderly or disabled people. To accomplish this goal, actions which are desired to be detected are defined and a naive Bayes classifier including training and prediction phases is employed. In the training phase, tri-axis acceleration data obtained from accelerometer sensor(eZ430 Chronos) recorded as training data. For each action, a pattern is generated from the training set and stored in a database. In the prediction phase, a sample real time data of unknown type is compared to the actions in the database and the most probable action is classified as the detected action.
eZ-430 Chronos
Sit after lie
Training Phase
In this phase, our system is trained to detect the activities. Each action repeated many times and the acceleration data recorded as the training data. The training data are exposed to normal distribution to extract mean and standard deviation. These values are inserted into the database per action after squaring standard deviation values to obtain variance.
Walk
Activities
Our monitoring system can detect the simple and composite actions. If an action contains more than one subaction of different characteristic, it is regarded as a composite action. Lie, sit and stand, are simple actions. Walk, stand after sit and sit after lie are composite actions.
Sit
Stand
Naive Bayesian Classification
Naive Bayesian classifier handles the classification in training and prediction phases. In training phase, the training data are processed to calculate the parameters of a probability distribution. In prediction phase, the posterior probability of an unknown sample is calculated for every class and the sample is classified to be in a class whose posterior probability is the greatest.
Samples for Composite Actions
Future Work
As future work, other classification techniques, use of multiple Chronos’ and combination of different sensors will be considered in activity monitoring scenarios. Complex actions such as falling or bumping will be studied. Mapping of daily activities to continuous patterns will be studied to identify abnormal activities in regular patterns.
Activity Monitoring State Diagram
References
Park K., Becker E., Vinjumur J.K., Le Z., Makedon F., Human Bahavioral Detection and Data Cleaning in Assisted Living Environment using Wireless Sensor Networks, PETRA '09: Proceedings of the 2nd International Conference on PErvasive Technologies Related to Assistive Environments, June 2009
Bao L., Intille S.S., Activity Recognition from User-Annotated Acceleration Data, Pervasive Computing, Second International Conference, PERVASIVE 2004, Vienna, Austria, April 21-23, 2004, Proceedings. Lecture Notes in Computer Science 3001 Springer 2004, ISBN
Flach P.A., Lachiche N., Naive Bayesian Classification of Structured Data, Machine Learning, 57(3). ISSN , pp. 233–269. December 2004
Results
Action name
Correct detection
Confusion
Walk 25
Sit_stand
Sit
Stand 10
Walk(3),
Sit_stand(12)
Lie 24
Lie_sit(1)
Lie_sit 5
Sit_stand(1),
Lie(19)
Contact
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Example acceleration data graphic of walking (above) and sit after lie (below) according to X, Y, Z axis