Voice-Controlled IoT Devices Framework for Smart Home

Ranjan, Ravi; Sharma, Aditi

doi:10.1007/978-981-15-3369-3_5

Cited by 12 publications

(5 citation statements)

References 18 publications

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“…Regarding home assistants, while many authors focused on a specific group of people, such as the elderly and/or disabled [ 37 , 38 , 39 , 40 , 43 , 63 ], our assistant is meant to be accessible to all home inhabitants, from younger to older, with or without temporary/permanent limitations. Furthermore, most works mainly aim at device control and/or home automation [ 38 , 43 , 63 , 64 , 65 ]. In contrast, our system goes beyond simple control or automation, by allowing obtaining rich relevant information on the home and its devices.…”

Section: Discussionmentioning

confidence: 99%

“…An outlier is the conversational assistant proposed by Salvi and coworkers [ 36 ], which relied on Google’s Dialogflow platform. Other authors used commercially available assistants, such as Google Assistant [ 38 , 65 ] and Alexa [ 64 ]. In regard to the language(s) covered by the assistants proposed in the literature, the most common is English [ 34 , 35 , 36 , 37 , 38 , 39 , 64 ].…”

Section: Discussionmentioning

confidence: 99%

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An Accessible Smart Home Based on Integrated Multimodal Interaction

Rocha

Ketsmur

Almeida

et al. 2021

Sensors

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Our homes are becoming increasingly sensorized and smarter. However, they are also becoming increasingly complex, making accessing them and their advantages difficult. Assistants have the potential for improving the accessibility of smart homes, by providing everyone with an integrated, natural, and multimodal way of interacting with the home’s ecosystem. To demonstrate this potential and contribute to more environmentally friendly homes, in the scope of the project Smart Green Homes, a home assistant highly integrated with an ICT (Information and communications technology) home infrastructure was developed, deployed in a demonstrator, and evaluated by seventy users. The users’ global impression of our home assistant is in general positive, with 61% of the participants rating it as good or excellent overall and 51% being likely or very likely to recommend it to others. Moreover, most think that the assistant enhances interaction with the smart home’s multiple devices and is easy to use by everyone. These results show that a home assistant providing an integrated view of a smart home, through natural, multimodal, and adaptive interaction, is a suitable solution for enhancing the accessibility of smart homes and thus contributing to a better living ambient for all of their inhabitants.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

An Accessible Smart Home Based on Integrated Multimodal Interaction

Rocha

Ketsmur

Almeida

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…For instance, smart thermostats optimize temperature settings based on user preferences and occupancy patterns, saving energy and ensuring comfort. Privacy concerns in this category often revolve around the potential for unauthorized access to home systems, compromising security and individual privacy [11].…”

Section: Smart Homementioning

confidence: 99%

IoT Complexity: Security, Vulnerabilities and Risks

Rashid,

Haque,

Wang

2024

EJECE

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IoT privacy highlights the critical significance of tailored threat models to address the evolving challenges in the IoT landscape. This research paper presents an in-depth examination of privacy threat modeling in the context of the Internet of Things (IoT), and aims to develop threat models tailored to different IoT device categories, identifying vulnerabilities and potential privacy risks associated with each. This analysis seeks to provide insights into the diverse privacy challenges posed by IoT devices, ranging from wearables to healthcare IoT and smart home, which exhibit unique vulnerabilities and privacy risks. By developing threat models specific to each device category, this research elucidates the diversity of privacy concerns, such as data breaches, unauthorized access, and data tracking the applicability of privacy regulations varies across these categories, emphasizing the need for tailored regulatory frameworks. The research underscores the importance of user education and responsible device design, advocating for privacy literacy and transparency, as it ensures that privacy is an integral part of the development process, fostering a safer, more secure, and privacy-conscious IoT ecosystem where innovation and privacy coexist for the benefit of all.

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“…But quite clearly, the data management systems must cater to the phenomenal volume, variety and velocity of data which may need huge storage and make analytical, process and retrieval operations cumbersome and time-consuming. Though the recent advancements in 5G wireless technology and edge computing enable connected devices to process data closer to where it is created, it may partially improve the response time [12]. Still the overwhelming amount of data can create havoc and the healthcare practitioners might miss on some important information.…”

Section: Fig1 Challenges In Iomt Adoptionmentioning

confidence: 99%

Resolving Data Overload and Latency Issues in Multivariate Time-Series IoMT Data for Mental Health Monitoring

2021

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Pervasive healthcare services have evolved substantially in the recent years with IoMT rapidly changing the pace and scale of healthcare delivery. A promising application of IoMT is to fetch patterns of mental behaviour symptomatology based on bio-signals and transfer it to the corresponding hospital or psychologist for remote monitoring. But the data volume & performance, device diversity & interoperability, hacking & unauthorized use and acceptance & adoption barriers still restrain the practical and competent use of these devices. This research presents a plausible solution to surmount the data overload and processing latency in real-time sensory data collected through wearable devices for mental health monitoring. We propose a modified k-medoid data clustering technique based on time-frame restricted intra-cluster similarity calculations to obtain a summarized version of the original benchmark WESAD dataset for which the degree of information lost is minimum. A CNN is then trained on this summarized dataset for classification of mental state into the baseline, stress and amusement categories. The results show a significant reduction in the average execution time by 34% with a comparable accuracy to the original dataset, thus offering prompt real-time healthcare analytics.

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Voice-Controlled IoT Devices Framework for Smart Home

Cited by 12 publications

References 18 publications

An Accessible Smart Home Based on Integrated Multimodal Interaction

An Accessible Smart Home Based on Integrated Multimodal Interaction

IoT Complexity: Security, Vulnerabilities and Risks

Resolving Data Overload and Latency Issues in Multivariate Time-Series IoMT Data for Mental Health Monitoring

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