Multi-sensor information fusion based on machine learning for real applications in human activity recognition: State-of-the-art and research challenges.

This paper firstly introduces common wearable sensors, smart wearable devices and the key application areas. Since multi-sensor is defined by the presence of more than one model or channel, e.g. visual, audio, environmental and physiological signals. Hence, the fusion methods of multi-modality and multi-location sensors are proposed. Despite it has been contributed several works reviewing the stateoftheart on information fusion or deep learning, all of them only tackled one aspect of the sensor fusion applications, which leads to a lack of comprehensive understanding about it. Therefore, we propose using a more holistic approach in order to provide a more suitable starting point from which to develop a full understanding of the fusion methods of wearable sensors. Specifically, this review attempts to provide a more comprehensive survey of the most important aspects of multi-sensor applications for human activity recognition, including those recently added to the field for unsupervised learning and transfer learning. Finally, the open research issues that need further research and improvement are identified and discussed. • Machine learning combines heterogeneous features into multi-sensor information fusion. • Deep learning algorithms have been proposed for automatic feature representation. • Transfer learning and domain adaptation keep the generalization performance up. • Hyper-parameters tunning has been used for providing effective fusion strategies. • Encryption and authentication approaches are necessary to protect privacy of users. [ABSTRACT FROM AUTHOR]