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Human Activity Recognition (HAR) using wearable devices such as smart watchesembedded with Inertial Measurement Unit (IMU) sensors has various applicationsrelevant to our daily life, such as workout tracking and health monitoring. Inthis paper, we propose a novel attention-based approach to human activityrecognition using multiple IMU sensors worn at different body locations.Firstly, a sensor-wise feature extraction module is designed to extract themost discriminative features from individual sensors with Convolutional NeuralNetworks (CNNs). Secondly, an attention-based fusion mechanism is developed tolearn the importance of sensors at different body locations and to generate anattentive feature representation. Finally, an inter-sensor feature extractionmodule is applied to learn the inter-sensor correlations, which are connectedto a classifier to output the predicted classes of activities. The proposedapproach is evaluated using five public datasets and it outperformsstate-of-the-art methods on a wide variety of activity categories.

Attention-Based Sensor Fusion for Human Activity Recognition Using IMU Signals