Special Issue "Multi-Sensor Fusion in Body Sensor Networks"
Deadline for manuscript submissions: closed (30 September 2019).
Interests: high-level programming methodologies and frameworks for body sensor networks; collaborative and cloud-assisted body sensor networks; pattern recognition and knowledge discovery algorithms on physiological signals; human activity recognition; ECG analysis; emotion recognition; interoperability on the Internet-of-Things
Special Issues and Collections in MDPI journals
Interests: body sensor networks, wearable sensing, data fusion, medical big data
Interests: pervasive computing, machine learning, algorithm design, Internet-of-Things, mobile health
Interests: wearable sensors; machine learning; activity recognition; inertial sensors; movement analysis; gait parameters estimation; automatic early detection of gait alterations; sports bioengineering; mobile health
Special Issues and Collections in MDPI journals
Multi-sensor data fusion comprises methodologies, algorithms and techniques to capture, from multiple sources, a unified picture of the observed phenomenon. In the context of body sensor networks (BSNs), the objective of sensor data fusion is the integration of multiple, heterogeneous, noisy and error-affected signals to obtain more accurate and comprehensive information on a subject’s health and psycho-physiological status.
Since their appearance, BSNs were considered a potentially disruptive shift of our health and social lifestyle. The key aspect of BSNs are wireless unobstructive wearable sensing units attached to the human body that allow, in mobility, continuous and real-time physiological monitoring to enable diverse applications such as (i) prevention, early detection, and monitoring of diseases and other medical conditions; (ii) elderly assistance at home; (iii) sport and training; (iv) physical activity and gesture detection; and (v) emotion recognition.
However, although the increasing diffusion of smart wearable sensing devices, the design and implementation of effective (and power-efficient) BSN applications remains challenging. Physiological signals are acquired, processed, and streamed by resource-constrained devices with limited processing capabilities, energy availability, and storage capacity that altogether hinder signal processing, pattern recognition, and machine learning performance. Multi-sensor data fusion applied to redundant or complementary signals is seen as an effective solution to infer accurate information from such corrupted, noisy, or error-affected signals. Nevertheless, the current evolution trend of BSNs to multi-device, multi-modal sensing systems makes data fusion a complex task that has only recently started to be approached with systematic and reusable methods and technical solutions.
This Special Issue aims to provide a report of recent research results related to methodologies, algorithms and techniques of “Multi-Sensor Fusion in Body Sensor Networks”.
Dr. Raffaele Gravina
Prof. Ye Li
Prof. Hassan Ghasemzadeh
Dr. Andrea Mannini
Manuscript Submission Information
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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
- distributed multi-sensor fusion algorithms
- collaborative multi-sensor fusion
- multi-level algorithms for multi-sensor fusion in BSNs
- multi-modal fusion for cognitive services
- power-efficient sensor fusion in BSNs
- multi-sensor fusion for early diseases detection in BSNs
- multi-sensor fusion for human activity recognition applications
- multi-sensor fusion for emotion recognition applications