Explorando tecnologias de IoT no contexto de Health Smart Home: uma abordagem para detecção de quedas em pessoas idosas

Mano, Leandro Yukio; Funes, Marcio Maestralo; Volpato, Tiago; Valladares‐Neto, José

doi:10.26729/jadi.v2i1.1667

Cited by 14 publications

(16 citation statements)

References 23 publications

(37 reference statements)

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“…Therefore, the identification of the individual and the detection of physical behavior, for example, a fall, can help systems monitor the health of people with disabilities, those with reduced mobility, and the elderly. They can also meet the needs and assist in the daily life of each user [23,32].…”

Section: Smart Individualized Monitoringmentioning

confidence: 99%

“…KNN is a supervised algorithm, which learns to perform a task (in this research, recognition and detection) according to a specialized dataset. We chose KNN because it presented useful results in the detection and classification of falls, especially with the use of wearable sensor-based fall detection [32,33]. In the context of face recognition, there are more robust recognition approaches presenting good results in face classification [8,23,34].…”

Section: Smart Individualized Monitoringmentioning

confidence: 99%

“…A fall detection application monitors the movements of users in smart environments as a means of monitoring their routine at a distance and detecting unusual activity, such as falls. MOOSE uses fall detection based on [32,33], but we used a smartwatch to collect accelerometer and gyroscope data. The use of a smartwatch presents an additional challenge in collecting data, namely, false positives.…”

Section: Fall Detection Applicationmentioning

confidence: 99%

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Exploiting Offloading in IoT-Based Microfog: Experiments with Face Recognition and Fall Detection

Valladares‐Neto

Filho

Mano

et al. 2019

Wireless Communications and Mobile Computing

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The growth in many countries of the population in need of healthcare and with reduced mobility in many countries shows the demand for the development of assistive technologies to cater for this public, especially when they require home treatment after being discharged from the hospital. To this end, interactive applications on mobile devices are often integrated into intelligent environments. Such environments usually have limited resources, which are not capable of processing great volumes of data and can expend much energy due to devices being in communication to a cloud. Some approaches have tried to minimize these problems by using fog microdatacenter networks to provide high computational capabilities. However, full outsourcing of the data analysis to a microfog can generate a reduced level of accuracy and adaptability. In this work, we propose a healthcare system that uses data offloading to increase performance in an IoT-based microfog, providing resources and improving health monitoring. The main challenge of the proposed system is to provide high data processing with low latency in an environment with limited resources. Therefore, the main contribution of this work is to design an offloading algorithm to ensure resource provision in a microfog and synchronize the complexity of data processing through a healthcare environment architecture. We validated and evaluated the system using two interactive applications of individualized monitoring: (1) recognition of people using images and (2) fall detection using the combination of sensors (accelerometer and gyroscope) on a smartwatch and smartphone. Our system improves by 54% and 15% on the processing time of the user recognition and Fall Decision applications, respectively. In addition, it showed promising results, notably (a) high accuracy in identifying individuals, as well as detecting their mobility; and (b) efficiency when implemented in devices with scarce resources.

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Section: Smart Individualized Monitoringmentioning

confidence: 99%

Section: Smart Individualized Monitoringmentioning

confidence: 99%

Section: Fall Detection Applicationmentioning

confidence: 99%

See 1 more Smart Citation

Exploiting Offloading in IoT-Based Microfog: Experiments with Face Recognition and Fall Detection

Valladares‐Neto

Filho

Mano

et al. 2019

Wireless Communications and Mobile Computing

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“…In addition, the database allows making an advanced design of systems to convert texts to voice (e.g., transcribe foreign speech) or systems of language education in LIBRAS Frota et al (2015); Durlak (2015); Weiss et al (2017). In Ambient Assisted Living, our database can help in assisting people with disabilities to communicate and to provide interactivity with computer systems or smartphones (e.g., to recognize Gonçalves et al (2016); Mano et al (2016a;2016c) and conduct decision-making process by speech Filho et al (2018); Khunt and Prabu (2018)). Also, the VERBO may provide the support needed for serious game applications in the treatment of children with autism, and in engaging of teenagers with Down Syndrome, improving their communication and speech skills Bernardini et al (2014); Gonzàlez-Ferreras et al (2017).…”

Section: Applicabilitymentioning

confidence: 99%

VERBO: Voice Emotion Recognition dataBase in Portuguese Language

Valladares‐Neto

Filho

Mano

et al. 2018

Journal of Computer Science

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The recognition of human emotional traits based on Affective Computing is being carried out by computational systems that are able to interpret and react intelligently to the context of the user. Speech Emotion Recognition systems are capable of transforming speech signal data into information related to the feelings of individuals in specific situations. However, the emotional expression of a human being depends mainly on his origins. For this reason, emotional voice databases are peculiar to each language. In this paper, we propose a new emotional database with speech in the Portuguese language of Brazil, called Voice Emotion Recognition dataBase in Portuguese language (VERBO). The database was validated by a panel of expert judges and we achieved an agreement rate of 76% using the content validity index and substantial agreement rate of 65% using Fleiss' Kappa. In addition, an accuracy of 0.76 was achieved and it was possible to observe that the emotions anger and happiness were more easy to recognize showing 0.85 and 0.83 of f1-score, respectively, whereas the disgust and surprise emotions were the most difficult showing 0.67 and 0.68, respectively. In view of this, the main contributions to research made by this study are: (1) The establishment of a new actuated voice database; (2) support provided by voice recognition systems for the analysis of feelings and emotions; and (3) statistical validation of the database using CVI and Fleiss kappa.

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“…In [13], text messages are automatically sent to a patient 24 hours before the scheduled appointment, as a reminder for an appointment with a doctor.…”

Section: Related Workmentioning

confidence: 99%