Recognition of daily life motor activity classes using an artificial neural network

Abstract

Objective: To investigate a possible role of artificial neural networks for the automated recognition and classification of daily life activities (eg, sitting, lying, standing, walking, etc) in an attempt to reduce the cost of manual recognition and classification. Methods: Data from sessions of about 10 hours of continuous recording of eight ambulatory patients were used to train and evaluate eight probabilistic neural networks, each of which is configured for one subject. To provide the reference data for building the training set, the instrumented subject follows a 15-to 30-minute protocol consisting of several daily life activities. To properly evaluate the networks, the remaining manually labeled data of each subject were compared with the output of each trained network. Results: The average recognition rate of the trained neural networks was equal to 95% good classification of all presented cases of the daily life activity. Automatic misclassification of 5% resulted from certain activities being too short or the occurrence of activities that were not included in the training set. Conclusion: The preliminary results of the trained neural networks have indicated that the probabilistic neural network is a potentially useful tool for the recognition of daily life motor activities.