LARES: An AI-based teleassistance system for emergency home monitoring

Ropero, Fernando; Vaquerizo-Hdez, Daniel; Muñoz, Pablo; Barrero, David F.

doi:10.1016/j.cogsys.2019.03.019

Cited by 14 publications

(8 citation statements)

References 17 publications

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“…Assuming that the training set samples are x and y [22] and the number of hidden layer units of artificial intelligence machine learning is k, the output model is…”

Section: Music Automatic Notation Algorithmmentioning

confidence: 99%

Piano Performance and Music Automatic Notation Algorithm Teaching System Based on Artificial Intelligence

Yang

2021

Mobile Information Systems

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Artificial intelligence is a subject that studies all kinds of human intelligent activities and their laws. It is developed on the basis of the cohesion of many disciplines such as computer science, politics, information system, neurophysiology, psychology, philosophy, and language. This paper aims to study how to build a computer or intelligent machine, including hardware and software, imitate and expand the human brain to perform thinking functions such as thinking, programming, arithmetic, and learning, solve complex problems that need to be handled by professionals, and better apply the artificial intelligence assistance system to the teaching of piano performance. In this paper, Prolog language and music-assisted learning system based on the ARM and SA algorithm are proposed, the principle and operation process of music automatic recording technology are deeply studied, and the system data of artificial intelligence are summarized and analyzed by using internal database, so as to find out the implementation principle and law of piano automatic recording system. So that the artificial intelligence assistant system can be better applied to music teaching. The experimental results show that, in the teaching system of piano performance and music automatic notation algorithm, the utilization rate of artificial intelligence auxiliary technology has reached 56.81 and is growing rapidly. Therefore, we can find that the artificial intelligence assistant system plays an important role in the teaching system of piano performance and automatic music notation.

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“…Assuming that the training set samples are x and y [22] and the number of hidden layer units of artificial intelligence machine learning is k, the output model is…”

Section: Music Automatic Notation Algorithmmentioning

confidence: 99%

Piano Performance and Music Automatic Notation Algorithm Teaching System Based on Artificial Intelligence

Yang

2021

Mobile Information Systems

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“…HAPTIC CREATURE [43,44], EDU' [45], MAYA [46] Telepresence robots TURTLEBOT 2 [47][48][49]; CHICARO [50]; DALI [51]; INTOUCH VITA [52,53]; GIRAFFPLUS [54][55][56]; SIRA [57].…”

Section: Type Of Robot Modelsmentioning

confidence: 99%

Application of Social Robots in Healthcare: Review on Characteristics, Requirements, Technical Solutions

Ragno,

Borboni,

Vannetti

et al. 2023

Sensors

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Cyber-physical or virtual systems or devices that are capable of autonomously interacting with human or non-human agents in real environments are referred to as social robots. The primary areas of application for biomedical technology are nursing homes, hospitals, and private homes for the purpose of providing assistance to the elderly, people with disabilities, children, and medical personnel. This review examines the current state-of-the-art of social robots used in healthcare applications, with a particular emphasis on the technical characteristics and requirements of these different types of systems. Humanoids robots, companion robots, and telepresence robots are the three primary categories of devices that are identified and discussed in this article. The research looks at commercial applications, as well as scientific literature (according to the Scopus Elsevier database), patent analysis (using the Espacenet search engine), and more (searched with Google search engine). A variety of devices are enumerated and categorized, and then our discussion and organization of their respective specifications takes place.

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“…Focusing on fall detection, the problem is not only related to the data gathering, but also with the technology and methodology used to retrieve the fall information. In our previous works related to the monitoring of elderly people falling, we faced the problem of collecting related data [7,8] in order to satisfy our needs at the moment. After consulting with experts in geriatrics, the use of a dummy was suggested.…”

Section: Related Workmentioning

confidence: 99%

“…For instance, we can highlight the usage of Machine Learning (ML) applied both to image and video recognition [6], as well as audio processing [7] for fall detection. In particular, we want to underline our LARES project [8], whose main objective is the passive monitorization of dependent people in opposition to common (and less effective) active systems based on teleasistance, in which the dependent has to activate a necklace to ask for assistance. This objective is achieved through the deployment of a Wireless Sensor Network along with a low-cost robot, supervised by a dependent's family member or a caregiving service via a Web-based system.…”

Section: Introductionmentioning

confidence: 99%

A Simulator to Support Machine Learning-Based Wearable Fall Detection Systems

et al. 2020

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People’s life expectancy is increasing, resulting in a growing elderly population. That population is subject to dependency issues, falls being a problematic one due to the associated health complications. Some projects are trying to enhance the independence of elderly people by monitoring their status, typically by means of wearable devices. These devices often feature Machine Learning (ML) algorithms for fall detection using accelerometers. However, the software deployed often lacks reliable data for the models’ training. To overcome such an issue, we have developed a publicly available fall simulator capable of recreating accelerometer fall samples of two of the most common types of falls: syncope and forward. Those simulated samples are like real falls recorded using real accelerometers in order to use them later as input for ML applications. To validate our approach, we have used different classifiers over both simulated falls and data from two public datasets based on real data. Our tests show that the fall simulator achieves a high accuracy for generating accelerometer data from a fall, allowing to create larger datasets for training fall detection software in wearable devices.

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LARES: An AI-based teleassistance system for emergency home monitoring

Cited by 14 publications

References 17 publications

Piano Performance and Music Automatic Notation Algorithm Teaching System Based on Artificial Intelligence

Piano Performance and Music Automatic Notation Algorithm Teaching System Based on Artificial Intelligence

Application of Social Robots in Healthcare: Review on Characteristics, Requirements, Technical Solutions

A Simulator to Support Machine Learning-Based Wearable Fall Detection Systems

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