Activity Monitoring by Multiple Distributed Sensing

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information and Communications Technology".

Deadline for manuscript submissions: closed (30 June 2019) | Viewed by 4112

Special Issue Editors

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Special Issue Information

Dear Colleagues,

Visual sensor networks are now progressively becoming a fundamental piece of our lives, with a wide application in smart cities, traffic monitoring, and crime event detection.

The increasing computational power, together with the decreasing costs of dedicated hardware and the availability of communications infrastructures are greatly modifying the traditional approaches to visual monitoring. What was previously done at the level of individual sensors, today is done via sensor networks. The focus of the developed approaches is lightly changed, pointing not only on the robustness of algorithms and the relative results, but also on the computational time and the communications between sensors.

Activity monitoring, that crosses different aspects of intelligent systems development, was typically threated in a single-view context; the multi-view approach has been also investigated, but in scenarios where the interaction between nodes was very limited and typically demanded to a central processing unit, devoted to merging and decision making tasks.

Now the attention of researchers is on the sharing and distribution of the information, as well as in the communication and processing. In such a context, there are now many challenges to face in order to perform activity recognition in a distributed manner, spacing from the design of the network, to the automatic integration of new devices.

Aim of this Special Issue is to bring together researchers from different communities (such as Computer Vision, networked embedded sensing, artificial intelligence and so on) which address the problem of interpretation of the information coming from multiple distributed sensing systems.

Topics of interest include, but are not limited to, the following:

  • Single and multiple object tracking;
  • Multi-agent/multi sensing activity detection and recognition;
  • Re-identification;
  • Analysis of communication issues in sensor networks;
  • Fast real-time detection, segmentation and classification of objects;
  • Deep learning solutions for sensor networks applications;
  • Human behavior analysis;
  • Sensor networks architectures and calibration;
  • Scene understanding;
  • Event detection;
  • Fusion Processing for heterogeneous sensor networks;
  • Applications (Video Surveillance, Sport scene analysis, Human Computer Interaction …).

Each manuscript will be blind reviewed by journal academic editors.

Dr. Paolo Spagnolo
Dr. Pier Luigi Mazzeo
Guest Editors

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Information is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 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.


  • Sensor Networks
  • Activity Monitoring
  • Artificial Intelligence
  • Computer Vision
  • Deep Learning

Published Papers (1 paper)

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15 pages, 3020 KiB  
Multi-Sensor Activity Monitoring: Combination of Models with Class-Specific Voting
by Lingfei Mo, Lujie Zeng, Shaopeng Liu and Robert X. Gao
Information 2019, 10(6), 197; - 4 Jun 2019
Cited by 2 | Viewed by 3692
This paper presents a multi-sensor model combination system with class-specific voting for physical activity monitoring, which combines multiple classifiers obtained by splicing sensor data from different nodes into new data frames to improve the diversity of model inputs. Data obtained from a wearable [...] Read more.
This paper presents a multi-sensor model combination system with class-specific voting for physical activity monitoring, which combines multiple classifiers obtained by splicing sensor data from different nodes into new data frames to improve the diversity of model inputs. Data obtained from a wearable multi-sensor wireless integrated measurement system (WIMS) consisting of two accelerometers and one ventilation sensor have been analysed to identify 10 different activity types of varying intensities performed by 110 voluntary participants. It is noted that each classifier shows better performance on some specific activity classes. Through class-specific weighted majority voting, the recognition accuracy of 10 PA types has been improved from 86% to 92% compared with the non-combination approach. Furthermore, the combination method has shown to be effective in reducing the subject-to-subject variability (standard deviation of recognition accuracies across subjects) in activity recognition and has better performance in monitoring physical activities of varying intensities than traditional homogeneous classifiers. Full article
(This article belongs to the Special Issue Activity Monitoring by Multiple Distributed Sensing)
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