Wearable Devices and IoT

Poongodi, T.; Krishnamurthi, Rajalakshmi; Indrakumari, R.; Suresh, P.; Balamurugan, B.

doi:10.1007/978-3-030-23983-1_10

Cited by 65 publications

(35 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Beside the accuracy obtained with just peripheral signals, these works show that the fusion of multiple modalities can outperform single modality approaches, therefore they can be useful sources of information for improving multimodal system performance. Moreover, the rapid development and mass production of consumer grade devices, such as smartband and smartwatch (Poongodi et al, 2020 ), will facilitate the integration of these signals in most of HRI systems. For example, in Lazzeri et al ( 2014 ) a multimodal acquisition platform, including a humanoid robot capable of expressing emotions, was tested in a social robot-based therapy scenario for children with autism.…”

Section: State Of the Artmentioning

confidence: 99%

Emotion Recognition for Human-Robot Interaction: Recent Advances and Future Perspectives

2020

View full text Add to dashboard Cite

A fascinating challenge in the field of human–robot interaction is the possibility to endow robots with emotional intelligence in order to make the interaction more intuitive, genuine, and natural. To achieve this, a critical point is the capability of the robot to infer and interpret human emotions. Emotion recognition has been widely explored in the broader fields of human–machine interaction and affective computing. Here, we report recent advances in emotion recognition, with particular regard to the human–robot interaction context. Our aim is to review the state of the art of currently adopted emotional models, interaction modalities, and classification strategies and offer our point of view on future developments and critical issues. We focus on facial expressions, body poses and kinematics, voice, brain activity, and peripheral physiological responses, also providing a list of available datasets containing data from these modalities.

show abstract

Section: State Of the Artmentioning

confidence: 99%

Emotion Recognition for Human-Robot Interaction: Recent Advances and Future Perspectives

2020

View full text Add to dashboard Cite

show abstract

“…Alcoholic beverages have become a routine part of today's society [9]. The people who are employed in enterprises where alcohol frequently accompanies socialising fall prey to ill effects of alcohol consumption [11].…”

Section: Related Workmentioning

confidence: 99%

IoTPulse: machine learning-based enterprise health information system to predict alcohol addiction in Punjab (India) using IoT and fog computing

Dhillon

Singh

Vohra

et al. 2020

Enterprise Information Systems

View full text Add to dashboard Cite

This paper proposes IoT-based an enterprise health information system called IoTPulse to predict alcohol addiction providing real-time data using machine-learning in fog computing environment. We used data from 300 alcohol addicts from Punjab (India) as a case study to train machine-learning models. The performance of IoTPulse is compared against existing work using various parameters including accuracy, sensitivity, specificity and precision which shows improvement of 7%, 4%, 12% and 12% respectively. Finally, IoTPulse is validated in FogBus-based real fog environment using QoS parameters including latency, network bandwidth, energy and response time which improves performance by 19.56%, 18.36%, 19.53% and 21.56% respectively.

show abstract

“…The prior studies have focused on how to offload computation tasks to the cloud system in order to improve application performance on the user's devices and measure the delay optimal results of healthcare data without any risk. The delay and cost-optimal task scheduling of heartbeat healthcare applications into cloud networks were investigated in [34][35][36][37]. The studies accepted the input of data from real-time sensors and provided the application tasks for the actions.…”

Section: Related Workmentioning

confidence: 99%

A Novel Cost-Efficient Framework for Critical Heartbeat Task Scheduling Using the Internet of Medical Things in a Fog Cloud System

Mastoi

Wah

Raj

et al. 2020

Sensors

View full text Add to dashboard Cite

Recently, there has been a cloud-based Internet of Medical Things (IoMT) solution offering different healthcare services to wearable sensor devices for patients. These services are global, and can be invoked anywhere at any place. Especially, electrocardiogram (ECG) sensors, such as Lead I and Lead II, demands continuous cloud services for real-time execution. However, these services are paid and need a lower cost-efficient process for the users. In this paper, this study considered critical heartbeat cost-efficient task scheduling problems for healthcare applications in the fog cloud system. The objective was to offer omnipresent cloud services to the generated data with minimum cost. This study proposed a novel health care based fog cloud system (HCBFS) to collect, analyze, and determine the process of critical tasks of the heartbeat medical application for the purpose of minimizing the total cost. This study devised a health care awareness cost-efficient task scheduling (HCCETS) algorithm framework, which not only schedule all tasks with minimum cost, but also executes them on their deadlines. Performance evaluation shows that the proposed task scheduling algorithm framework outperformed the existing algorithm methods in terms of cost.

show abstract

Wearable Devices and IoT

Cited by 65 publications

References 35 publications

Emotion Recognition for Human-Robot Interaction: Recent Advances and Future Perspectives

Emotion Recognition for Human-Robot Interaction: Recent Advances and Future Perspectives

IoTPulse: machine learning-based enterprise health information system to predict alcohol addiction in Punjab (India) using IoT and fog computing

A Novel Cost-Efficient Framework for Critical Heartbeat Task Scheduling Using the Internet of Medical Things in a Fog Cloud System

Contact Info

Product

Resources

About