From Data Acquisition to Data Fusion: A Comprehensive Review and a Roadmap for the Identification of Activities of Daily Living Using Mobile Devices

By Ivan Miguel Pires, Nuno M. Garcia, Nuno Pombo, Francisco Flórez-Revuelta. 2016

154 citations recorded by the source corpus.

This paper focuses on the research on the state of the art for sensor fusion techniques, applied to the sensors embedded in mobile devices, as a means to help identify the mobile device user’s daily activities. Sensor data fusion techniques are used to consolidate the data collected from several sensors, increasing the reliability of the algorithms for the identification of the different activities. However, mobile devices have several constraints, e.g., low memory, low battery life and low processing power, and some data fusion techniques are not suited to this scenario. The main purpose of this paper is to present an overview of the state of the art to identify examples of sensor data fusion techniques that can be applied to the sensors available in mobile devices aiming to identify activities of daily living (ADLs).

Subfields: Biomedical Engineering, Computer Vision and Pattern Recognition, Transportation

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Citations in the corpus

A survey on data fusion in internet of things: Towards secure and privacy-preserving fusion (2019, methodology): “Objectives and Topics Limitation [15] Focus on data fusion algorithms and theories Not highly related to IoT data fusion [16] Focus on data fusion methods in specific IoT environments Ignore privacy and security in IoT data fusion [17] Classification of data fusion methods: stage-based methods, feat”
Deep Learning Sensor Fusion for Autonomous Vehicle Perception and Localization: A Review (2020, unknown): “…model to model the sensory information [64,67] Can handle ; Tolerant[68,69] Limited to linear estimators; Computation complexity is high [65] Estimation Low [70] Based on probability representation for the sensory information [64] Uncertainty in the provided information is handled.…”
Vision-based human activity recognition: a survey (2020, background): “Methods of human activities recognition are categorized, in [127], with regard to the nature of the detector into two main categories: Uni-modal methods that consider an activity as a set of visual characteristics and allow the recognition from single modality data [109, 147]; Multi-modal methods, w”