Data fusion and multiple classifier systems for human activity detection and health monitoring: Review and open research directions

By Henry Friday Nweke, Teh Ying Wah, Ghulam Mujtaba, Mohammed Ali Al-Garadi. Information Fusion, 2019

358 citations recorded by the source corpus.

Abstract Activity detection and classification using different sensor modalities have emerged as revolutionary technology for real-time and autonomous monitoring in behaviour analysis, ambient assisted living, activity of daily living (ADL), elderly care, rehabilitations, entertainments and surveillance in smart home environments. Wearable devices, smart-phones and ambient environments devices are equipped with variety of sensors such as accelerometers, gyroscopes, magnetometer, heart rate, pressure and wearable camera for activity detection and monitoring. These sensors are pre-processed and different feature sets such as time domain, frequency domain, wavelet transform are extracted and transform using machine learning algorithm for human activity classification and monitoring. Recently, deep learning algorithms for automatic feature representation have also been proposed to lessen the burden of reliance on handcrafted features and to increase performance accuracy. Initially, one set of sensor data, features or classifiers were used for activity recognition applications. However, there are new trends on the implementation of fusion strategies to combine sensors data, features and classifiers to provide diversity, offer higher generalization, and tackle challenging issues. For instances, combination of inertial sensors provide mechanism to differentiate activity of similar patterns and accurate posture identification while other multimodal sensor data are used for energy expenditure estimations, object localizations in smart homes and health status monitoring. Hence, the focus of this review is to provide in-depth and comprehensive analysis of data fusion and multiple classifier systems techniques for human activity recognition with emphasis on mobile and wearable devices. First, data fusion methods and modalities were presented and also feature fusion, including deep learning fusion for human activity recognition were critically analysed, and their applications, strengths and issues were identified. Furthermore, the review presents different multiple classifier system design and fusion methods that were recently proposed in literature. Finally, open research problems that require further research and improvements are identified and discussed.

Subfields: Biomedical Engineering, Computer Networks and Communications, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/800cd71393a568128e3797b832f3005a664e662f

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In Biomedical Engineering

In Computer Networks and Communications

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Citations in the corpus

Deep-Learning-Enhanced Human Activity Recognition for Internet of Healthcare Things (2020, background): “where θ is the DQN parameter and γ ∈ [0, 1] is the discount”
Human Activity Recognition With Smartphone and Wearable Sensors Using Deep Learning Techniques: A Review (2021, background): “3069927 complex, and postural activities of human behaviors in the area of Ambient Assisted Living (AAL), sports injury detection, well-being management, medical diagnosis and especially in elderly care [1].”
Vision-based human activity recognition: a survey (2020, background): “On the other hand, reviews on vision-based Ambient Assisted Living, patient monitoring like [123, 127, 139], fall detection and abnormal human activity recognition such as [18, 39] were proposed in the literature.”
Human Activity Recognition using Inertial, Physiological and Environmental Sensors: A Comprehensive Survey (2020, background): “[18] (2019) provide a detailed analysis of data/sensors fusion and multiple classification systems tech-”