DTPAAL: Digital Twinning Pepper and Ambient Assisted Living

Cascone, Lucia; Nappi, Michele; Narducci, Fabio; Passero, Ignazio

doi:10.1109/tii.2021.3090363

Cited by 25 publications

(9 citation statements)

References 26 publications

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“…A comprehensive range of sensors and actuators, as well as revealing abilities with high-level interface for realistic human contact, enable Pepper, a humanoid robot, to exhibit body language, perceive, and interact with its surrounding environment. In this paper, Cascone et al [13] described experiences centered on the connection of the digital-twin with both the copies of the smart products in a smart home in order to present the creation of V-Pepper, the Pepper digital replica. Although Pepper robot has hands and arms, its motors and actuator are not strong enough to sustain lengthy testing sessions and training program to learn how to securely handle objects.…”

Section: Literature Surveymentioning

confidence: 99%

“…Building a smart home is often an essence for deploying all the sensors, software, network, and physical assets. The data collected and analysis results are shared to the digital twin and can be monitored by an individual [13]. These digital proxies are expected to be built from the domain knowledge of subject matter experts as well as the real-time data collected from the devices [14].…”

Section: Introductionmentioning

confidence: 99%

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Digital Twins for Smart Home Gadget Threat Prediction using Deep Convolution Neural Network

Padmapriya¹,

Srivenkatesh²

2023

IJACSA

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Digital twin is one of the most important innovations in the Internet of Things (IoT) era and business disruption. Digital twins are a growing technology that bridges the gap between the real and the digital. Home automation in the IoT refers to the practice of automatically managing and monitoring smart home electronics by use of a variety of control system methods. The geysers, refrigerators, fans, lighting, fire alarms, kitchen timers, and other electrical and electronic items in the home can all be managed and monitored with the help of a variety of control methods. Digital twins replicate the physical machine in real time and produce data, such as asset degradation, product performance level that may be used by the predictive maintenance algorithm to identify the product functionality levels. The purpose of this research is to design the framework of Digital Twin using machine learning and state estimation algorithms model to assess and predict home appliances based on the probability rate of smart home system gadgets functionality. The main goal of this research is to create a digital twin for smart home gadgets that are used to monitor the health status of these devices for increasing the life time and to reduce maintenance costs. This research presents a Deep Convolution Neural Network based Logistic Regression Model with Digital Twins (DCNN-LR-DT) for accurate prediction of smart home gadget functionality levels and to predict the threats in advance. The proposed model is compared with the traditional models and the results represent that the proposed model performance is better than traditional models.

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Section: Literature Surveymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Digital Twins for Smart Home Gadget Threat Prediction using Deep Convolution Neural Network

Padmapriya¹,

Srivenkatesh²

2023

IJACSA

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“…The substances from which to draw diagnostic information for continuous monitoring of health status can be various, urine, feces, body fat, but also heart-rate frequency and number of steps done (Cascone et al. 2021 ).…”

Section: Health Monitoringmentioning

confidence: 99%

Biometrics for Industry 4.0: a survey of recent applications

Cascone

Nappi

2023

J Ambient Intell Human Comput

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The Fourth Industrial Revolution, also known as Industry 4.0, represents the rise of digital industrial technology that is propagating at an exponential rate compared to the previous three revolutions. Interoperability is a basis of production, where there is a continuous exchange of information between machines and production units that act autonomously and intelligently. Workers play a central role in making autonomous decisions and using advanced technological tools. It may involve using measures that distinguish individuals, and their behaviours and reactions. Increasing the level of security, allowing only authorized personnel access to designated areas, and promoting worker welfare can have a positive impact on the entire assembly line. Thus, capturing biometric information, with or without individuals’ knowledge, could allow identity verification and monitoring of of their emotional and cognitive states during the daily actions of work life. From the study of the literature, we outline three macro categories in which the principles of Industry 4.0 are merged and the functionalities of biometric systems are exploited: security, health monitoring, and quality work life analysis. In this review, we present an overview of all biometric features used in the context of Industry 4.0 with a focus on their advantages, limitations, and practical use. Attention is also paid to future research directions for which new answers are being explored.

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“…During the usage of mechanical components, damages and even failures can occur due to external and internal factors, making it difficult to maintain a stable operating state over a long period of time [1]. As components are used over an extended period, the operating environment becomes increasingly complex, posing new challenges for equipment diagnosis and maintenance [2][3][4]. Currently, the maintenance of mechanical components is mainly based on after-the-fact maintenance and limited experience-based predictive maintenance, which limits the accuracy and effectiveness of component maintenance and can affect the operation of the entire equipment [5].…”

Section: Iintroductionmentioning

confidence: 99%

State Perception and Prediction of Digital Twin Based on Proxy Model

Wang

et al. 2023

IEEE Access

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The maintenance of critical components plays a crucial role in ensuring the overall stable operation of equipment and minimizing damages caused by functional errors. However, Traditional operation and maintenance (O&M) modes suffer from problems such as reliance on empirical judgment, lack of data support, insufficient preventive maintenance, and inadequate collaborative management. To address these issues, a viable approach is to adopt more intelligent O&M modes. Based on the characteristics of digital twin technology, such as virtual interaction and real-time feedback, a digital twin framework for critical component maintenance of equipment is proposed, providing a new approach for the practical application of digital twin in intelligent maintenance processes. This framework consists of two key components: the digital twin maintenance model and the proxy model. The process of establishing the digital twin model is elaborated in detail, and a mechanism that integrates digital twin technology and the proxy model is proposed, along with a prediction process based on the fusion of simulation and monitoring data. Finally, based on the summary of the modeling process and the proxy model, a visualization interface for intelligent maintenance of components is built using relevant engineering software.

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DTPAAL: Digital Twinning Pepper and Ambient Assisted Living

Cited by 25 publications

References 26 publications

Digital Twins for Smart Home Gadget Threat Prediction using Deep Convolution Neural Network

Digital Twins for Smart Home Gadget Threat Prediction using Deep Convolution Neural Network

Biometrics for Industry 4.0: a survey of recent applications

State Perception and Prediction of Digital Twin Based on Proxy Model

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