Delivering home healthcare through a Cloud-based Smart Home Environment (CoSHE)

Pham, Minh; Mengistu, Yehenew; Manh, Ha; Sheng, Weihua

doi:10.1016/j.future.2017.10.040

Cited by 122 publications

(48 citation statements)

References 13 publications

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“…Rabindra and Rojalina [22] proposed a fogbased machine learning model for smart system big data analytics called FogLearn for application of K-means clustering in Ganga River Basin Management and real-world feature data for detecting diabetes patients suffering from diabetes mellitus. Alvin et al [ [11] ✓ ✓ FogCepCare [12] ✓ ✓ IoT e-health service [13] ✓ ✓ ECGH [14] ✓ ✓ ✓ AMS [16] ✓ ✓ GRAM [17] ✓ [39] proposed a Cloud-based Smart Home Environment (CoSHE) to deliver home healthcare to provide humans contextual information and monitors the vital signs using robot assistant. Initially, CoSHE uses non-invasive wearable sensors to gather the audio, motion and physiological signals and delivers the contextual information in terms of the residents daily activity.…”

Section: Related Workmentioning

confidence: 99%

“…The performance of CBFA is evaluated using iFogSim simulator [44] in terms of only latency. Research work [39,41,42,43] developed general healthcare applications at small scale and none of the work focused on heart patientbased healthcare application to diagnose the health status of heart patients.…”

Section: Related Workmentioning

confidence: 99%

“…Other works that propose computing models for healthcare applications in Fog Computing do not consider various aspects which HealthFog does. Many prior works [13,16,17,22,23,39,42] do not leverage resources close to the edge of the network. As per Figure 13, such models provide a much higher latency as all computation is done on the cloud and hence has higher data transfer times.…”

Section: Analysis With Related Workmentioning

confidence: 99%

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HealthFog: An ensemble deep learning based Smart Healthcare System for Automatic Diagnosis of Heart Diseases in integrated IoT and fog computing environments

Tuli

Basumatary

Gill

et al. 2020

Future Generation Computer Systems

495

248

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Cloud computing provides resources over the Internet and allows a plethora of applications to be deployed to provide services for different industries. The major bottleneck being faced currently in these cloud frameworks is their limited scalability and hence inability to cater to the requirements of centralized Internet of Things (IoT) based compute environments. The main reason for this is that latency-sensitive applications like health monitoring and surveillance systems now require computation over large amounts of data (Big Data) transferred to centralized database and from database to cloud data centers which leads to drop in performance of such systems. The new paradigms of fog and edge computing provide innovative solutions by bringing resources closer to the user and provide low latency and energy efficient solutions for data processing compared to cloud domains. Still, the current fog models have many limitations and focus from a limited perspective on either accuracy of results or reduced response time but not both. We proposed a novel framework called HealthFog for integrating ensemble deep learning in Edge computing devices and deployed it for a real-life application of automatic Heart Disease analysis. HealthFog delivers healthcare as a fog service using IoT devices and efficiently manages the data of heart patients, which comes as user requests. Fog-enabled cloud framework, FogBus is used to deploy and test the performance of the proposed model in terms of power consumption, network bandwidth, latency, jitter, accuracy and execution time. HealthFog is configurable to various operation modes which provide the best Quality of Service or prediction accuracy, as required, in diverse fog computation scenarios and for different user requirements.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Analysis With Related Workmentioning

confidence: 99%

See 1 more Smart Citation

HealthFog: An ensemble deep learning based Smart Healthcare System for Automatic Diagnosis of Heart Diseases in integrated IoT and fog computing environments

Tuli

Basumatary

Gill

et al. 2020

Future Generation Computer Systems

495

248

View full text Add to dashboard Cite

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“…From the literature review, it is possible to observe a significant move from using technologies such as Smart Home solutions, systems, technology and Elderly with health-related keywords through the extensive use of sensors, assistive technologies, monitoring of daily activities until now, where a clear phenomenon of IoT [137][138][139][140][141] and activity recognition with ambient assisted living (AAL) [2,137,142,143] and quality of life (QoL) [3,144] can be seen (Figure 7). There are also several journal publications in prominent impact factor journals [137][138][139]141,[145][146][147][148][149][150]] that confirm these findings; the most important one is "Internet-of-things-based smart environments: state of the art, taxonomy, and open research challenges" by Ahmed et al 2016 [145] published in the IEEE Wireless Communications journal with IF = 11, which is indexed second or fourth in all four web of science categories. This article has already received 70 citations in three years.…”

Section: Future Challengesmentioning

confidence: 80%

Commercial ICT Smart Solutions for the Elderly: State of the Art and Future Challenges in the Smart Furniture Sector

et al. 2020

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Within a ubiquitous environment, home and office furniture can be maximally utilized to provide ease, especially if the items are designed based on smart technology. For this reason, the acceptance of smart furniture has soared over the years. Given the vast influence of the Internet of Things (IoT) and Industry 4.0 on technological advancement in furniture design, it is imperative to examine information and communication technology (ICT) solutions for the elderly in the context of smart furniture design and implementation. This article presents a review of the state-of-the-art literature in smart solutions for the elderly based on publications under ICT smart solutions for these elderly, along with smart furniture options and manufacturer activities in terms of fixing market prices for these furniture materials. Furthermore, patenting rights on some existing smart furniture designs for the elderly, given the current trends in worldwide acceptance, are examined. Moreover, this article also highlights opportunities introduced by IoT-based solutions for the elderly as current trends in research and their effects on human life. Some smart product examples from different enterprises are also presented. New, innovative and active designs must be developed, focusing upon human healthcare, and in turn providing greater comfort and convenience for elderly people. To fulfil these requirements, the also selected technical aspects of new Smart Furniture solutions in connection to the cost of these solutions are discussed. Simultaneously, Smart Furniture solutions need to be flexible, low-cost, easy to buy and install without expert knowledge, and widely available on the market.

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“…Using cloud services to recognize human activities at home is key for implementing light-weight data processing. Pham et al [46] presented a Cloud-Based Smart Home Environment (CoSHE) for home healthcare. While the effect of the system is good, various basic sensors and devices must be installed, which is not ideal for implementation and long-term maintenance.…”

Section: Introductionmentioning

confidence: 99%

Integrating Multiple Models Using Image-as-Documents Approach for Recognizing Fine-Grained Home Contexts

Chen

Saiki

Nakamura

2020

Sensors

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To implement fine-grained context recognition that is accurate and affordable for general households, we present a novel technique that integrates multiple image-based cognitive APIs and light-weight machine learning. Our key idea is to regard every image as a document by exploiting "tags" derived by multiple APIs. The aim of this paper is to compare API-based models' performance and improve the recognition accuracy by preserving the affordability for general households. We present a novel method for further improving the recognition accuracy based on multiple cognitive APIs and four modules, fork integration, majority voting, score voting, and range voting.Our interest is to use little image data, applying cognitive services to implement affordable context sensing that can adapt to custom contexts in every single house. The cognitive service is a cloud service with cognitive computing functions that provide the capability to understand multimedia data (i.e., vision (object recognition) [19], speech recognition [20], natural language processing [21], etc.), based on sophisticated machine-learning algorithms powered by the offered big data and large-scale computing. They are offered by cloud companies, but the data processing algorithms behind them are not public. They are also widely used in various fields of research, such as modern knowledge management solutions [22] and criminal detection and recognition [23]. The cognitive API is an application program interface via the HTTP/REST protocol [24], with which developers can easily integrate powerful recognition features in their own applications. An image-based cognitive API receives an image from an external application, i.e., extracts specific information from the image, and returns the information in JavaScript Object Notation (JSON) [25] format from the cloud server rather than the local. The information usually contains a set of words called "tags", representing objects and concepts that the API has recognized in the given image. Examples of tags from the API are: [Living, room, indoors, classroom, basement, supporting structure]. The information of interest and the way of recognizing the image vary among individual cognitive services. Related work uses image tagging technology with deep learning, as in [26][27][28], but the implementation is more complex. In our future realistic implementation, for security and privacy of the users the images are not saved after sending the APIs over.The main contribution of this paper is to present a novel method which is not only affordable but also has higher accuracy in recognizing fine-grained home contexts. For this purpose, we are currently investigating techniques that integrate inexpensive camera devices, multiple image-based cognitive APIs, and light-weight machine learning. We previously encoded the tags of a single API to document vectors, then applied them into machine learning for the model construction [29]. However, we found that the accuracy significantly decreased for contexts with multiple people (e.g., "...

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Delivering home healthcare through a Cloud-based Smart Home Environment (CoSHE)

Cited by 122 publications

References 13 publications

HealthFog: An ensemble deep learning based Smart Healthcare System for Automatic Diagnosis of Heart Diseases in integrated IoT and fog computing environments

HealthFog: An ensemble deep learning based Smart Healthcare System for Automatic Diagnosis of Heart Diseases in integrated IoT and fog computing environments

Commercial ICT Smart Solutions for the Elderly: State of the Art and Future Challenges in the Smart Furniture Sector

Integrating Multiple Models Using Image-as-Documents Approach for Recognizing Fine-Grained Home Contexts

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