Survey of Cognitive Assisted Living Ambient System Using Ambient intelligence as a Companion

Ramkumar, Mo.; Catharin, S. Sarah; Nivetha, D.

doi:10.1109/icscan.2019.8878707

Cited by 8 publications

(3 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Ambient intelligence (AmI) in healthcare as a computing paradigm where sensors are integrated into conventional objects aimed at enabling people's capabilities through digital environments that are adaptive, sensitive, responsive and adaptive to human activities and emotions has been receiving intense attention in the subject of smart homes [10][11][12][13]. Readers interested in the subject of AmI for AAL and smart homes are referred to the following articles [14][15][16][17][18][19].…”

Section: Related Workmentioning

confidence: 99%

Passive RFID Module With LSTM Recurrent Neural Network Activity Classification Algorithm for Ambient-Assisted Living

Oguntala

Alabdullah

et al. 2021

IEEE Internet Things J.

View full text Add to dashboard Cite

Human activity recognition from sensor data is a fundamental research topic to achieve remote health monitoring and ambient assisted living (AAL). In AAL, sensors are integrated into conventional objects aimed at enabling people's capabilities through digital environments that are sensitive, responsive and adaptive to human activities. Moreover, new technological approaches to support AAL within the home or community setting offers people the prospect of more individually focused care and improved quality of living. In the present work, an ambient human activity classification framework that augments information from the received signal strength indicator (RSSI) of passive RFID tags to obtain detailed activity profiling is proposed. Key indices of position, orientation, mobility, and degree of activities which are critical to guide reliable clinical management decisions using 4 volunteers are employed to simulate the research objective. A twolayer, fully connected sequence long short-term memory recurrent neural network model (LSTM RNN) is implemented. The LSTM RNN model extracts the feature of RSS from the sensor data and classifies the sampled activities using SoftMax. The performance of the LSTM model is evaluated for different data size and the hyper-parameters of the RNN are adjusted to optimal states, which results in an accuracy of 98.18%. The proposed framework suits well for smart homes and smart health and offers a pervasive sensing environment for the elderly, persons with disability and chronic illness.

show abstract

Section: Related Workmentioning

confidence: 99%

Passive RFID Module With LSTM Recurrent Neural Network Activity Classification Algorithm for Ambient-Assisted Living

Oguntala

Alabdullah

et al. 2021

IEEE Internet Things J.

View full text Add to dashboard Cite

show abstract

“…In the last decade, smart environments have found wide application in several contexts, such as domotic, education, rehabilitation, assistance, and so on. Structuring classroom environments with new technologies makes learning more attractive, as well as providing support for teaching people with disabilities [1]. Similarly, new technological devices provide more specific tools and support for the rehabilitation of people with impairment [2,3].…”

Section: Introductionmentioning

confidence: 99%

Ambient Assisted Living: A Review of Technologies, Methodologies and Future Perspectives for Healthy Aging of Population

Cicirelli

Marani

Petitti

et al. 2021

Sensors

125

View full text Add to dashboard Cite

Over the last decade, there has been considerable and increasing interest in the development of Active and Assisted Living (AAL) systems to support independent living. The demographic change towards an aging population has introduced new challenges to today’s society from both an economic and societal standpoint. AAL can provide an arrary of solutions for improving the quality of life of individuals, for allowing people to live healthier and independently for longer, for helping people with disabilities, and for supporting caregivers and medical staff. A vast amount of literature exists on this topic, so this paper aims to provide a survey of the research and skills related to AAL systems. A comprehensive analysis is presented that addresses the main trends towards the development of AAL systems both from technological and methodological points of view and highlights the main issues that are worthy of further investigation.

show abstract

“…Both of them, however, have yielded a vast scientific production, as evidenced by the important number of surveys currently present in the related literature. Specifically concerning AmI it is possible to find a wide range of studies: (i) papers of a general nature that overview the field, introducing key concepts, common use cases, and state-of-the-art techniques [6][7][8][9][10][11]; (ii) works circumscribed only to a subset of relevant related matters, such as security and privacy [12][13][14][15], sensing, computing, and networking technologies [16,17], as well as ethical implications and social issues [18], and (iii) research focused on particular application domains (e.g., architecture [19], workplaces [20], educational environments [21,22], and smart cities [18]), with healthcare being the one that has shown greater treatment [12,[23][24][25][26][27][28][29]. For its part, the on-device object detection corpus is limited to literature published over the last five years (2017)(2018)(2019)(2020)(2021).…”

Section: Introductionmentioning

confidence: 99%

On-Device Object Detection for More Efficient and Privacy-Compliant Visual Perception in Context-Aware Systems

2021

View full text Add to dashboard Cite

Ambient Intelligence (AmI) encompasses technological infrastructures capable of sensing data from environments and extracting high-level knowledge to detect or recognize users’ features and actions, as well as entities or events in their surroundings. Visual perception, particularly object detection, has become one of the most relevant enabling factors for this context-aware user-centered intelligence, being the cornerstone of relevant but complex tasks, such as object tracking or human action recognition. In this context, convolutional neural networks have proven to achieve state-of-the-art accuracy levels. However, they typically result in large and highly complex models that typically demand computation offloading onto remote cloud platforms. Such an approach has security- and latency-related limitations and may not be appropriate for some AmI use cases where the system response time must be as short as possible, and data privacy must be guaranteed. In the last few years, the on-device paradigm has emerged in response to those limitations, yielding more compact and efficient neural networks able to address inference directly on client machines, thus providing users with a smoother and better-tailored experience, with no need of sharing their data with an outsourced service. Framed in that novel paradigm, this work presents a review of the recent advances made along those lines in object detection, providing a comprehensive study of the most relevant lightweight CNN-based detection frameworks, discussing the most paradigmatic AmI domains where such an approach has been successfully applied, the different challenges arisen, the key strategies and techniques adopted to create visual solutions for image-based object classification and localization, as well as the most relevant factors to bear in mind when assessing or comparing those techniques, such as the evaluation metrics or the hardware setups used.

show abstract

Survey of Cognitive Assisted Living Ambient System Using Ambient intelligence as a Companion

Cited by 8 publications

References 4 publications

Passive RFID Module With LSTM Recurrent Neural Network Activity Classification Algorithm for Ambient-Assisted Living

Passive RFID Module With LSTM Recurrent Neural Network Activity Classification Algorithm for Ambient-Assisted Living

Ambient Assisted Living: A Review of Technologies, Methodologies and Future Perspectives for Healthy Aging of Population

On-Device Object Detection for More Efficient and Privacy-Compliant Visual Perception in Context-Aware Systems

Contact Info

Product

Resources

About