Emotional condition in the Health Smart Homes environment: emotion recognition using ensemble of classifiers

Mano, Leandro Yukio

doi:10.1109/inista.2018.8466318

Cited by 16 publications

(20 citation statements)

References 27 publications

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“…IoT-based HSH has been extensively studied in the literature. In particular, advances in in-home healthcare in recent years are due to smart environment applications, as well as artificial intelligence [21][22][23]. Therefore, the combination of modern detection equipment, advanced data-processing techniques, and wireless networks results in the creation of digital environments that improve residents' daily lives [24,25].…”

Section: Related Workmentioning

confidence: 99%

“…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%

“…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]. However, the choice to use KNN in our model is justified by self-learning based on historical examples only and by adaptability due to not requiring, at each update, changes in network topology.…”

Section: Smart Individualized Monitoringmentioning

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: Related Workmentioning

confidence: 99%

Section: Smart Individualized Monitoringmentioning

confidence: 99%

Section: Smart Individualized Monitoringmentioning

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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“…Here the movement assessment is based on a dynamic time warping (DTW) approach, which has several advantages over the other methods (e.g., hidden Markov models) in terms of the size of the referential dataset and the possibility of performing an independent analysis on different portions of the body [5], [6]. The automatic identification of a situation in which patients are faced with difficulties or pain is based on a detection technique of subtle features of facial expressions [7] or body movements [8]. Then, machine learning classifiers are used to distinguish whether the patients are reaching their limits.…”

Section: Introductionmentioning

confidence: 99%

Design of a Kinematic and Emotional Assessment Module for the Tele-rehabilitation Platform

Rybarczyk¹,

Leconte²,

Pérez-Medina³

et al. 2019

IJMO

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Tele-rehabilitation is becoming extremely popular in the health scenario. Although this new medical approach has several advantages over traditional therapy, it is important to identify its limitations and risks in the recovery process. This study focuses on the modeling and implementation of a module to assess in real time the quality of movement and the emotional state of patients when they execute the rehabilitation exercises provided by a web-based platform. An algorithm based on dynamic time warping is proposed and tested to assess the movement kinematic. Furthermore, pain is discriminated through a support vector machine classifier. Both methods allow a high identification of gestures and emotions, respectively. These results are discussed in terms of the most suitable solutions to develop an efficient telerehabilitation system.

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“…us, a residential monitoring system is needed which allows alerts and warnings to be issued so that medical staff and/or family members are aware of the health conditions of the disabled and can intervene whenever necessary [1,2]. e concept of Health Smart Home (HSH) emerged from a combination of telemedicine, Internet of ings (IoT), and information systems and can be defined as an intelligent home equipped with specialized devices for distance healthcare aid, such as smartphones, and embedded and wearable technologies (e.g., smartwatch) [1,3].…”

Section: Introductionmentioning

confidence: 99%

ENLACE: A Combination of Layer-Based Architecture and Wireless Communication for Emotion Monitoring in Healthcare

Mano

Barros

Nunes

et al. 2019

Mobile Information Systems

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Owing to the increase in the number of people with disabilities, as a result of either accidents or old age, there has been an increase in research studies in the area of ubiquitous computing and the Internet of Things. They are aimed at monitoring health, in an efficient and easily accessible way, as a means of managing and improving the quality of life of this section of the public. It also involves adopting a Health Homes policy based on the Internet of Things and applied in smart home environments. This is aimed at providing connectivity between the patients and their surroundings and includes mechanisms for helping the diagnosis and prevention of accidents and/or diseases. Monitoring gives rise to an opportunity to exploit the way computational systems can help to determine the real-time emotional state of patients. This is necessary because there are some limitations to traditional methods of health monitoring, for example, establishing the behavior of the user’s routine and issuing alerts and warnings to family members and/or medical staff about any abnormal event or signs of the onset of depression. This article discusses how a layer-based architecture can be used to detect emotional factors to assist in healthcare and the prevention of accidents within the context of Smart Home Health. The results show that this process-based architecture allows a load distribution with a better service that takes into account the complexity of each algorithm and the processing power of each layer of the architecture to provide a prompt response when there is a need for some intervention in the emotional state of the user.

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Emotional condition in the Health Smart Homes environment: emotion recognition using ensemble of classifiers

Cited by 16 publications

References 27 publications

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

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

Design of a Kinematic and Emotional Assessment Module for the Tele-rehabilitation Platform

ENLACE: A Combination of Layer-Based Architecture and Wireless Communication for Emotion Monitoring in Healthcare

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