An adaptive Kalman-based Bayes estimation technique to classify locomotor activities in young and elderly adults through accelerometers

An accelerometer-based system able to classify among different locomotor activities during real life conditions is here presented, and its performance evaluated. Epochs of walking at different speeds, and with different slopes, and stair descending and ascending, are detected, segmented, and classified by using an adaptation of a naïve 2D-Bayes classifier, which is updated on-line through the history of the estimated activities, in a Kalman-based scheme. The feature pair used for classification is mapped from an ensemble of 16 features extracted from the accelerometer data for each activity epoch. Two different versions of the classifier are presented to combine the multi-dimensional nature of the accelerometer data, and their results are compared in terms of correct recognition rate of the segmented activities, on two population samples of different age. The classification algorithm achieves correct classification rates higher than 90% and higher than 92%, for young and elderly adults, respectively.