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Activity recognition using wearable sensors has become essential for a variety of applications. Tri-axial accelerometers are the most widely used sensor for activity recognition. Although various features have been used to capture patterns and classify the accelerometer signals to recognise activities, there is no consensus on the best features to choose. Reducing the number of features can reduce the computational cost and complexity and enhance the performance of the classifiers. This paper identifies the signal features that have significant discriminative power between different human activities. It also investigates the effect of sensor placement location, the sampling frequency, and activity complexity on the selected features. A comprehensive list of 193 signal features has been extracted from accelerometer signals of four publicly available datasets, including features that have never been used before for activity recognition. Feature significance was measured using the Joint Mutual Information Maximisation (JMIM) method. Common significant features among all the datasets were identified. The results show that the sensor placement location does not significantly affect recognition performance, nor does it affect the significant sub-set of features. The results also showed that with high sampling frequency, features related to signal repeatability and regularity show high discriminative power. [ABSTRACT FROM AUTHOR]

Highlights: [•] A new feature selection method called Feature Interaction Maximisation is proposed. [•] It employs interaction information with maximum of the minimum criterion. [•] It uses forward greedy search to select the features one by one. [•] It is evaluated in terms of the classification accuracy of the selected features. [•] The result shows that the proposed method outperforms IG, mRMR, DISR, and IGFS. [ABSTRACT FROM AUTHOR]