Detecting Irregular Patterns in IoT Streaming Data for Fall Detection

Mahfuz, Sazia; Isah, Haruna; Zulkernine, Farhana; Nicholls, P. H.

doi:10.1109/iemcon.2018.8614822

Cited by 13 publications

(3 citation statements)

References 25 publications

(45 reference statements)

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“…The purpose of the article was to provide voice input with a secure connection to a smart house with IoT Network. In article [19] the author mentions that training is a difficult part of a detection system. This is because the training dataset can become obsolete when the individual trained on it changes their usual pattern.…”

Section: Types and Placement Of Sensorsmentioning

confidence: 99%

Autonomous Update of a Dataset for Anomaly Detection Services in Elderly Care Smart House

Nchena¹,

Tomášek²

2023

Internet of Things - New Trends, Challenges and Hurdles

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This work proposes a smart system that could be useful in the delivery of elderly care services. Elderly care is a set of services that are provided to senior citizens to help them have a more comfortable and independent life which would not be possible without these services. This proposed system is unique in that it combines the detection algorithm with the automatic update of the dataset. It also uses a heuristic mechanism to reduce false detections. This is on the premise that the AI effort is good, but it could be made better with the inclusion of heuristics. Fall detection accuracy is initially solved by the first classifier, then another classifier evaluates the result with inferences before evoking an alarm. It checks the location of the subject to use in its inferences. Hence the smart house design consists of two machine learning systems. One system performs human activity classification while the other performs fall occurrence detection. Of the eight different classification methods utilized, XGBoost was most accurate with an average of 97.65% during training. A customized dataset is then generated with newly labeled data hence improving system performance.

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Section: Types and Placement Of Sensorsmentioning

confidence: 99%

Autonomous Update of a Dataset for Anomaly Detection Services in Elderly Care Smart House

Nchena¹,

Tomášek²

2023

Internet of Things - New Trends, Challenges and Hurdles

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“…Monitoring devices have been proven to ensure the safety of the nursing home residents in fall prevention [ [96,106]. Sensors were installed in the mattresses and rooms to monitor the older adults' behaviours and sleeping quality, especially used for residents with limited mobility [51,90].…”

Section: Monitoring and Notification Of Abnormal Eventsmentioning

confidence: 99%

Defining the concepts of a smart nursing home and its potential technology utilities that integrate medical services and are acceptable to stakeholders: a scoping review

et al. 2022

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Background and objectives Smart technology in nursing home settings has the potential to elevate an operation that manages more significant number of older residents. However, the concepts, definitions, and types of smart technology, integrated medical services, and stakeholders’ acceptability of smart nursing homes are less clear. This scoping review aims to define a smart nursing home and examine the qualitative evidence on technological feasibility, integration of medical services, and acceptability of the stakeholders. Methods Comprehensive searches were conducted on stakeholders’ websites (Phase 1) and 11 electronic databases (Phase 2), for existing concepts of smart nursing home, on what and how technologies and medical services were implemented in nursing home settings, and acceptability assessment by the stakeholders. The publication year was inclusive from January 1999 to September 2021. The language was limited to English and Chinese. Included articles must report nursing home settings related to older adults ≥ 60 years old with or without medical demands but not bed-bound. Technology Readiness Levels were used to measure the readiness of new technologies and system designs. The analysis was guided by the Framework Method and the smart technology adoption behaviours of elder consumers theoretical model. The results were reported according to the PRISMA-ScR. Results A total of 177 literature (13 website documents and 164 journal articles) were selected. Smart nursing homes are technology-assisted nursing homes that allow the life enjoyment of their residents. They used IoT, computing technologies, cloud computing, big data and AI, information management systems, and digital health to integrate medical services in monitoring abnormal events, assisting daily living, conducting teleconsultation, managing health information, and improving the interaction between providers and residents. Fifty-five percent of the new technologies were ready for use in nursing homes (levels 6–7), and the remaining were proven the technical feasibility (levels 1–5). Healthcare professionals with higher education, better tech-savviness, fewer years at work, and older adults with more severe illnesses were more acceptable to smart technologies. Conclusions Smart nursing homes with integrated medical services have great potential to improve the quality of care and ensure older residents’ quality of life.

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“…Thus, the task is compared with the factory task datasets. Thereafter, the task is ignored if found noisy; otherwise, it will be computed [36].…”

Section: Stage 1: Check Taskmentioning

confidence: 99%

Efficient energy and completion time for dependent task computation offloading algorithm in industry 4.0

2021

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Rapid technological development has revolutionized the industrial sector. Internet of Things (IoT) started to appear in many fields, such as health care and smart cities. A few years later, IoT was supported by industry, leading to what is called Industry 4.0. In this paper, a cloud-assisted fog-networking architecture is implemented in an IoT environment with a three-layer network. An efficient energy and completion time for dependent task computation offloading (ET-DTCO) algorithm is proposed, and it considers two quality-of-service (QoS) parameters: efficient energy and completion time offloading for dependent tasks in Industry 4.0. The proposed solution employs the Firefly algorithm to optimize the process of the selection-offloading computing mode and determine the optimal solution for performing tasks locally or offloaded to a fog or cloud considering the task dependency. Moreover, the proposed algorithm is compared with existing techniques. Simulation results proved that the proposed ET-DTCO algorithm outperforms other offloading algorithms in minimizing energy consumption and completion time while enhancing the overall efficiency of the system.

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Detecting Irregular Patterns in IoT Streaming Data for Fall Detection

Cited by 13 publications

References 25 publications

Autonomous Update of a Dataset for Anomaly Detection Services in Elderly Care Smart House

Autonomous Update of a Dataset for Anomaly Detection Services in Elderly Care Smart House

Defining the concepts of a smart nursing home and its potential technology utilities that integrate medical services and are acceptable to stakeholders: a scoping review

Efficient energy and completion time for dependent task computation offloading algorithm in industry 4.0

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