Sensor-based activity recognition in the context of ambient assisted living systems: A review

Patel, Ashish; Shah, Jigarkumar

doi:10.3233/ais-190529

Cited by 47 publications

(21 citation statements)

References 104 publications

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“…In this line, there are different reviews in the literature in which the different alternatives regarding the integration of Ambient Assisted Living (AAL) technology in residences are studied [9,10]. Reviews in which network projects, middleware, sensors, communications, or actuators are proposed, although most of them with a very particular focus and for specific scenarios, mainly focused on ubiquitous sensor systems and telehealth devices.…”

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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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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show abstract

“…Beyond the works referenced, the intersection of AmI and AI has already resulted in a substantial number of review articles [17,[42][43][44][45]. They summarize the progress both fields have experienced jointly, detailing the several approaches explored for creating intelligent and context-aware environments (e.g., human activity and behavior recognition techniques [44], and classification methods for more accurate disease prediction and diagnosis [45]).…”

Section: Introductionmentioning

confidence: 99%

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

2021

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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.

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“…Recognition of daily activities executed by a smart home resident supports the remote monitoring of elderly and enable them to live independently in their own homes (Patel and Shah 2019;Cook 2012;Chen et al 2012). Activity recognition improves the quality of life and maintains the well being of a smart home occupant through the analysis of the performed activities and identification of changes in the daily routine (Fahad et al 2013;Stikic et al 2011).…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Activity recognition in a smart home using local feature weighting and variants of nearest-neighbors classifiers

Fahad

Tahir

2020

J Ambient Intell Human Comput

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Recognition of activities, such as preparing meal or watching TV, performed by a smart home resident, can promote the independent living of elderly in a safe and comfortable environment of their own homes, for an extended period of time. Different activities performed at the same location have commonalities resulting in less inter-class variations; while the same activity performed multiple times, or by multiple residents, varies in its execution resulting in high intra-class variations. We propose a Local Feature Weighting approach (LFW) that assigns weights based on both inter-class and intra-class importance of a feature in an activity. Multiple sensors are deployed at different locations in a smart home to gather information. We exploit the obtained information, such as frequency and duration of activation of sensors, and the total sensors in an activity for feature weighting. The weights for the same features vary among activities, since a feature may have more importance for one activity but less for the other. For the classification, we exploit the two variants of K-Nearest Neighbors (KNN): Evidence Theoretic KNN (ETKNN) and Fuzzy KNN (FKNN). The evaluation of the proposed approach on three datasets, from CASAS smart home project, demonstrates its ability in the correct recognition of activities compared to the existing approaches.

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Sensor-based activity recognition in the context of ambient assisted living systems: A review

Cited by 47 publications

References 104 publications

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

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

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

Activity recognition in a smart home using local feature weighting and variants of nearest-neighbors classifiers

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