Reconfigurable Cyber-Physical System for Lifestyle Video-Monitoring via Deep Learning

Deniz, Daniel; Barranco, Francisco; Isern, Juan; Ros, Eduardo

doi:10.1109/etfa46521.2020.9211910

Cited by 6 publications

(2 citation statements)

References 34 publications

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“…The authors in [96] proposed a skeleton-based approach utilizing Spatiotemporal information and CNNs for the classification of human activities. The authors in [97] presented an indoor monitoring reconfigurable CPS that uses embedded local nodes (Nvidia Jetson TX2), proposing learning architectures to address Human Action Recognition.…”

Section: Human Centric Perceptionmentioning

confidence: 99%

Empowering cyberphysical systems of systems with intelligence

Nousias¹,

Piperigkos²,

Arvanitis³

et al. 2021

Preprint

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Cyber Physical Systems have been going into a transition phase from individual systems to a collecttives of systems that collaborate in order to achieve a highly complex cause, realizing a system of systems approach. The automotive domain has been making a transition to the system of system approach aiming to provide a series of emergent functionality like traffic management, collaborative car fleet management or large-scale automotive adaptation to physical environment thus providing significant environmental benefits (e.g air pollution reduction) and achieving significant societal impact. Similarly, large infrastructure domains, are evolving into global, highly integrated cyber-physical systems of systems covering all parts of the value chain. In practice, there are significant challenges in CPSoS applicability and usability to be addressed, i.e. even a small CPSoS such as a car consists several subsystems Decentralization of CPSoS appoints tasks to individual CPSs within the System of Systems. CPSoSs are heterogenous systems. They comprise of various, autonomous, CPSs, each one of them having unique performance capabilities, criticality level, priorities and pursued goals. all CPSs must also harmonically pursue systembased achievements and collaborate in order to make system-ofsystem based decisions and implement the CPSoS functionality. This survey will provide a comprehensive review on current best practices in connected cyberphysical systems. The basis of our investigation is a dual layer architecture encompassing a perception layer and a behavioral layer. Perception algorithms with respect to scene understanding (object detection and tracking, pose estimation), localization mapping and path planning are thoroughly investigated. Behavioural part focuses on decision making and human in the loop control.

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Section: Human Centric Perceptionmentioning

confidence: 99%

Empowering cyberphysical systems of systems with intelligence

Nousias¹,

Piperigkos²,

Arvanitis³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…While intelligent video analytics is the most widely used technology in safety and security [8], the use of this technology is not widely deployed in electric substations in general, or in final distribution substations in particular [9]. Nonscheduled service interruptions come at a significant cost, both economic and of reputation, positioning supply, quality, reliability and cost penalties at the forefront of interests for utilities [10].…”

Section: Introductionmentioning

confidence: 99%

A Cyber-Physical System for Integrated Remote Control and Protection of Smart grid Critical Infrastructures

Isern

Jimenez-Perera

Medina-Valdés

et al. 2023

J Sign Process Syst

Self Cite

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This work proposes a Cyber-Physical System (CPS) for protecting smart electric grid critical infrastructures using video surveillance while remotely monitoring them. Due to the critical nature of the smart grid, it is necessary to guarantee an adequate level of safety, security and reliability. Thus, this CPS is back-boned by a Time-Sensitive Network solution providing concurrent support for smart video surveillance and smart grid control over a single communication infrastructure. To this end, TSN delivers high-bandwidth communication for video surveillance and deterministic quality of service, latency and bandwidth guarantees, required by the time-critical revision smart grid control. On the one hand, the CPS utilizes High-availability Seamless Redundancy in the control subsystem via Remote Terminal Units (RTUs) guaranteeing seamless failover against failures in smart grid. On the other hand, the smart video surveillance subsystem applies machine learning to monitor secured perimeters and detect people around the smart grid critical infrastructure. Moreover, it is also able to directly interoperate with RTUs to send alarms in case of for example, an intrusion. The work evaluates the accuracy and performance of the detection using common metrics in surveillance field. An integrated monitoring dashboard has also been developed in which all CPS information is available in real time.

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Deep Multimodal Habit Tracking System: A User-adaptive Approach for Low-power Embedded Systems

Deniz

Jimenez-Perera²,

Nolasco³

et al. 2023

J Sign Process Syst

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The pace of population ageing is increasing and is currently becoming one of the challenges our society faces. The introduction of Cyber-Physical Systems (CPS) has fostered the development of e-Health solutions that ease the associated economic and social burden. In this work, a CPS-based solution is presented to partially tackle the problem: a Deep Multimodal Habit Tracking system. The aim is to monitor daily life activities to alert in case of life-threatening situations improving their autonomy and supporting healthy lifestyles while living alone at home. Our approach combines video and heart rate cues to accurately identify indoor actions, running the processing locally in embedded edge nodes. Local processing provides inherent protection of data privacy since no image or vital signs are transmitted to the network, and reduces data bandwidth usage. Our solution achieves an accuracy of more than 80% in average, reaching up to a 95% for specific subjects after adapting the system. Adding heart-rate information improves F1-score by 2.4%. Additionally, the precision and recall for critical actions such as falls reaches up to 93.75%. Critical action detection is crucial due to their dramatic consequences, it helps to reduce false alarms, leading to building trust in the system and reducing economic cost. Also, the model is optimized and integrated in a Nvidia Jetson Nano embedded device, reaching real-time performance below 3.75 Watts. Finally, a dataset specifically designed for indoor action recognition using synchronized video and heart rate pulses has been collected.

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Reconfigurable Cyber-Physical System for Lifestyle Video-Monitoring via Deep Learning

Cited by 6 publications

References 34 publications

Empowering cyberphysical systems of systems with intelligence

Empowering cyberphysical systems of systems with intelligence

A Cyber-Physical System for Integrated Remote Control and Protection of Smart grid Critical Infrastructures

Deep Multimodal Habit Tracking System: A User-adaptive Approach for Low-power Embedded Systems

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