A Benchmark Dataset for Human Activity Recognition and Ambient Assisted Living

Amato, Giuseppe; Bacciu, Davide; Chessa, Stefano; Dragone, Mauro; Gallicchio, Claudio; Gennaro, Claudio; Lozano, Héctor; Micheli, Alessio; O’Hare, Gregory M. P.; Rentería, Arantxa; Vairo, Claudio

doi:10.1007/978-3-319-40114-0_1

Cited by 11 publications

(7 citation statements)

References 18 publications

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“…Smart homes [77], equipped with sensors, can be equipped for monitoring different environmental and resident conditions [78] and drives for efficient help in their daily activities [79], for technology to be available for AAL. The design may include service or social robots, which may introduce additional functionalities and tools or provide more natural human-robot-interactions to enhance these environments and improve their acceptance towards end-users [80].…”

Section: Ambient Sensormentioning

confidence: 99%

Human Activity Recognition with Deep Learning: Methods, Progress & Possibilities

Kumar¹

2021

Preprint

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Over the past decade, recognition of human activities (HAR) has become a vibrant field of research, in particular, the spread in our everyday lives of electronics such as mobile phones, smart cell phones, and video cameras. Furthermore, the advancement in the field of deep methodologies and other paradigms have enabled scientists to enable HAR in many areas, consisting of activities in fitness and wellness. For instance, HAR is one of many resorts to support older people through day-to-day activities to support their cognition and physicality. This study is centered on the key aspects deep learning plays in the development of HAR applications. Although numerous HAR examination studies were carried out previously, there have been no overall studies on this subject, in all the earlier studies there were only specific HAR-related subjects. A detailed review covering all the main subjects in this area is therefore essential. This study discusses the latest developments and works in HAR. It separates the methods and the advantages and disadvantages of each method group. This paper finally addresses many problems in the current HAR subject and provides recommendations for potential study.

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Section: Ambient Sensormentioning

confidence: 99%

Human Activity Recognition with Deep Learning: Methods, Progress & Possibilities

Kumar¹

2021

Preprint

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“…In this study, we focus on HAR conducted from wearable sensors used, for instance, during sport activities. Other works focus on daily human activities such as cooking or cleaning [ 10 , 11 , 12 ], mostly using home sensors. These studies describe how they have collected datasets from heterogeneous sensor networks located in apartments.…”

Section: Related Workmentioning

confidence: 99%

A Benchmark of Data Stream Classification for Human Activity Recognition on Connected Objects

Khannouz

Glatard

2020

Sensors

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This paper evaluates data stream classifiers from the perspective of connected devices, focusing on the use case of Human Activity Recognition. We measure both the classification performance and resource consumption (runtime, memory, and power) of five usual stream classification algorithms, implemented in a consistent library, and applied to two real human activity datasets and three synthetic datasets. Regarding classification performance, the results show the overall superiority of the Hoeffding Tree, the Mondrian forest, and the Naïve Bayes classifiers over the Feedforward Neural Network and the Micro Cluster Nearest Neighbor classifiers on four datasets out of six, including the real ones. In addition, the Hoeffding Tree and—to some extent—the Micro Cluster Nearest Neighbor, are the only classifiers that can recover from a concept drift. Overall, the three leading classifiers still perform substantially worse than an offline classifier on the real datasets. Regarding resource consumption, the Hoeffding Tree and the Mondrian forest are the most memory intensive and have the longest runtime; however, no difference in power consumption is found between classifiers. We conclude that stream learning for Human Activity Recognition on connected objects is challenged by two factors which could lead to interesting future work: a high memory consumption and low F1 scores overall.

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“…The learning system realizes a distributed learning infrastructure, based on the RUBICON learning layer by Bacciu et al, capable of addressing system information to provide predictions regarding the ecology state (eg, event recognition), allowing to tackle computational learning tasks concerning the online processing of streams (ie, time series) of sensor data. This embraces a large variety of ambient assisted living application scenarios, including, eg, recognition of human daily life activities at home, identification of human indoor mobility patterns, human activity recognition, and robot localization . Specifically, the used learning system proposes a neural‐motivated architecture, where state‐of‐the‐art recurrent neural networks (RNNs) are exploited as a computationally efficient means for implementing learning functions onboard the units composing the ecology.…”

Section: Approachmentioning

confidence: 99%

An ambient intelligence approach for learning in smart robotic environments

Bacciu

Rocco

Dragone

et al. 2019

Computational Intelligence

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Smart robotic environments combine traditional (ambient) sensing devices and mobile robots. This combination extends the type of applications that can be considered, reduces their complexity, and enhances the individual values of the devices involved by enabling new services that cannot be performed by a single device. To reduce the amount of preparation and preprogramming required for their deployment in real‐world applications, it is important to make these systems self‐adapting. The solution presented in this paper is based upon a type of compositional adaptation where (possibly multiple) plans of actions are created through planning and involve the activation of pre‐existing capabilities. All the devices in the smart environment participate in a pervasive learning infrastructure, which is exploited to recognize which plans of actions are most suited to the current situation. The system is evaluated in experiments run in a real domestic environment, showing its ability to proactively and smoothly adapt to subtle changes in the environment and in the habits and preferences of their user(s), in presence of appropriately defined performance measuring functions.

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A Benchmark Dataset for Human Activity Recognition and Ambient Assisted Living

Cited by 11 publications

References 18 publications

Human Activity Recognition with Deep Learning: Methods, Progress & Possibilities

Human Activity Recognition with Deep Learning: Methods, Progress & Possibilities

A Benchmark of Data Stream Classification for Human Activity Recognition on Connected Objects

An ambient intelligence approach for learning in smart robotic environments

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