Non-Invasive Ambient Intelligence in Real Life: Dealing with Noisy Patterns to Help Older People

Antón, Miguel; Ordieres-Meré, Joaquín; Saralegui, Unai; Sun, Shengjing

doi:10.3390/s19143113

Cited by 21 publications

(5 citation statements)

References 39 publications

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“…A cloud based IoMT platform as a multi-layered architecture was proposed to sense and collect patients' information of their vitals and surrounding environment for AAL [26]. With the objective of "aging well at home," an IoT-based smart home automation-enabled health monitoring and the assistive system was proposed to assist in caring for sick and elderly persons around the clock [3]. A non-invasive ambient intelligence for older…”

Section: Ambient Assisted Living For Elderly Activities Monitoringmentioning

confidence: 99%

“…In this section, the proposed technique for elderly activity recognition for AAL is evaluated in experiments over the recorded AAL dataset and MHealth benchmark dataset [5]. Leveraging the benefits of smart sensors and deep learning techniques, experiments were performed with GRU and BiGRU deep learning techniques and compared with SVM, Naive Bayes,Decision Trees and with machine learning techniques proposed by other researchers [1,3,6,23]. This experimentation aims to produce an efficient model capable of effectively detecting the daily activities of elderly people to continuously monitor their wellbeing.…”

Section: Experimentationmentioning

confidence: 99%

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Ambient assisted living framework for elderly care using Internet of medical things, smart sensors, and GRU deep learning techniques

Syed

Alsaeeedi

Jabeen³

et al. 2022

AIS

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Due to the increase in the global aging population and its associated age-related challenges, various cognitive, physical, and social problems can arise in older adults, such as reduced walking speed, mobility, falls, fatigue, difficulties in performing daily activities, memory-related and social isolation issues. In turn, there is a need for continuous supervision, intervention, assistance, and care for elderly people for active and healthy aging. This research proposes an ambient assisted living system with the Internet of Medical Things that leverages deep learning techniques to monitor and evaluate the elderly activities and vital signs for clinical decision support. The novelty of the proposed approach is that bidirectional Gated Recurrent Unit, and Gated Recurrent Unit deep learning techniques with mutual information-based feature selection technique is applied to select robust features to identify the target activities and abnormalities. Experiments were conducted on two datasets (the recorded Ambient Assisted Living data and MHealth benchmark data) with bidirectional Gated Recurrent Unit, and Gated Recurrent Unit deep learning techniques and compared with other state of art techniques. Different evaluation metrics were used to assess the performance, findings reveal that bidirectional Gated Recurrent Unit deep learning techniques outperform other state of art approaches with an accuracy of 98.14% for Ambient Assisted Living data, and 99.26% for MHealth data using the proposed approach.

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Section: Ambient Assisted Living For Elderly Activities Monitoringmentioning

confidence: 99%

Section: Experimentationmentioning

confidence: 99%

Ambient assisted living framework for elderly care using Internet of medical things, smart sensors, and GRU deep learning techniques

Syed

Alsaeeedi

Jabeen³

et al. 2022

AIS

View full text Add to dashboard Cite

show abstract

“…When data are related to people, such as devices which are linked to apps by bluetooth, the apps are mostly designed to present the data to users and upload summaries to the cloud manufacturer [35]. However, data integration (especially when related to people) has limitations related to privacy and misuse [36,37]. Cyber security is vulnerable, as wearable device manufacturers have reduced their safety protocols and safety stack layers to enable cheap products, as they have been understood as only serving the end user in a local context.…”

Section: Literature Reviewmentioning

confidence: 99%

Data Handling in Industry 4.0: Interoperability Based on Distributed Ledger Technology

Sun

Zheng

Villalba-Díez

et al. 2020

Sensors

Self Cite

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Information-intensive transformation is vital to realize the Industry 4.0 paradigm, where processes, systems, and people are in a connected environment. Current factories must combine different sources of knowledge with different technological layers. Taking into account data interconnection and information transparency, it is necessary to enhance the existing frameworks. This paper proposes an extension to an existing framework, which enables access to knowledge about the different data sources available, including data from operators. To develop the interoperability principle, a specific proposal to provide a (public and encrypted) data management solution to ensure information transparency is presented, which enables semantic data treatment and provides an appropriate context to allow data fusion. This proposal is designed also considering the Privacy by Design option. As a proof of application case, an implementation was carried out regarding the logistics of the delivery of industrial components in the construction sector, where different stakeholders may benefit from shared knowledge under the proposed architecture.

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“…One of the main objectives of the scientific community is to monitor and accurately identify the activity patterns of the elderly. Different studies have shown that the activity patterns of the elderly are a valid parameter to predict their quality of life [6]. There are approaches in this area to propose new algorithms, techniques, or systems to improve activity monitoring such as [7], focused on collecting different types of home surveillance technologies for monitoring behaviour of older people, or [8], where an architecture is developed that exploits the benefits of the Internet of Things (IoT) to capture location and other data to detect patterns of behaviour in older people in a nonintrusive way, or [7], where an IoT detection infrastructure for the city is presented that through REST and Linked Open Data application programming interfaces (APIs) collect and present data related to older people.…”

Section: Introductionmentioning

confidence: 99%

Smart Nursing Homes: Self‐Management Architecture Based on IoT and Machine Learning for Rural Areas

Flores-Martín

Rojo

Moguel

et al. 2021

Wireless Communications and Mobile Computing

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The rate of world population aging is increasing. This situation directly affects all countries socially and economically, increasing their compromise and effort to improve the living conditions of this sector of society. In environments with large influxes of elderly people, such as nursing homes, the use of technology has shown promise in improving their quality of life. The use of smart devices allows people to automate everyday tasks and learn from them to predict future actions. Additionally, smartphones capture a wealth of information that allows to adapt to nearby actuators according to people’s preferences and even detects anomalies in their behaviour. Current works are proposing new frameworks to detect these behaviours and act accordingly. However, these works are not focused on managing multidevice environments where sensor and smartphone data are considered to automate environments with elderly people or to learn from them. Also, most of these works require a permanent Internet connection, so the full benefit of smart devices is not completely achieved. In this work, we present an architecture that takes the data from sensors and smartphones in order to adapt the behaviour of the actuators of the environment. In addition, it uses this data to learn from the environment to predict actions or to extrapolate the actions that should be executed according to similar behaviours. The architecture is implemented through a use case based on a nursing home located in a rural area. Thanks to this work, the quality of life of the elderly is improved in a simple, affordable, and transparent way for them.

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Non-Invasive Ambient Intelligence in Real Life: Dealing with Noisy Patterns to Help Older People

Cited by 21 publications

References 39 publications

Ambient assisted living framework for elderly care using Internet of medical things, smart sensors, and GRU deep learning techniques

Ambient assisted living framework for elderly care using Internet of medical things, smart sensors, and GRU deep learning techniques

Data Handling in Industry 4.0: Interoperability Based on Distributed Ledger Technology

Smart Nursing Homes: Self‐Management Architecture Based on IoT and Machine Learning for Rural Areas

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