The MARBLE Dataset: Multi-inhabitant Activities of Daily Living Combining Wearable and Environmental Sensors Data

Arrotta, Luca; Bettini, Cláudio; Civitarese, Gabriele

doi:10.1007/978-3-030-94822-1_25

Cited by 10 publications

(3 citation statements)

References 23 publications

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“…In this review, we have carefully selected several relevant datasets widely adopted by the research community for Human Activity Recognition (HAR) studies. These datasets include Opportunity [42], PAMAP2 [43], UniMiB [44], CASAS: Aruba [45], CASAS: Cairo [46], CASAS: Kyoto [47], CASAS: Kyoto Multiresident [48], CASAS: Milan [47], CASAS: Tokyo [49], CASAS: Tulum [47], WISDM [50], ExtraSensory [51], USC-HAD [52], Skoda [53], UP-Fall [54], UK-DALE [55], MARBLE [56], KTH [57], Weizmann [58], UCF Sports Action [59], SisFall [60], LARa [61], UCI-HAR [62], UT_complex [63], UTD-MHAD [64], and UCI-SBHAR [65].…”

Section: Common Publicly Available Datasetsmentioning

confidence: 99%

Review on Human Action Recognition in Smart Living: Sensing Technology, Multimodality, Real-Time Processing, Interoperability, and Resource-Constrained Processing

Diraco

Rescio

Siciliano

et al. 2023

Sensors

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Smart living, a concept that has gained increasing attention in recent years, revolves around integrating advanced technologies in homes and cities to enhance the quality of life for citizens. Sensing and human action recognition are crucial aspects of this concept. Smart living applications span various domains, such as energy consumption, healthcare, transportation, and education, which greatly benefit from effective human action recognition. This field, originating from computer vision, seeks to recognize human actions and activities using not only visual data but also many other sensor modalities. This paper comprehensively reviews the literature on human action recognition in smart living environments, synthesizing the main contributions, challenges, and future research directions. This review selects five key domains, i.e., Sensing Technology, Multimodality, Real-time Processing, Interoperability, and Resource-Constrained Processing, as they encompass the critical aspects required for successfully deploying human action recognition in smart living. These domains highlight the essential role that sensing and human action recognition play in successfully developing and implementing smart living solutions. This paper serves as a valuable resource for researchers and practitioners seeking to further explore and advance the field of human action recognition in smart living.

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Section: Common Publicly Available Datasetsmentioning

confidence: 99%

Review on Human Action Recognition in Smart Living: Sensing Technology, Multimodality, Real-Time Processing, Interoperability, and Resource-Constrained Processing

Diraco

Rescio

Siciliano

et al. 2023

Sensors

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“…A cyber-only dataset was collected by Miettinen et al (2017) which provides network traffic and smart devices data for public use, IoT-Sentinel focused on cyber threats and attack detection by fingerprinting IoT devices. Lastly, Luca Arrotta et al (2022) recently published a 16-hour multi-occupant dataset collected using environmental sensors and wearable and smart devices from a single house. Table 1 provides a holistic comparison between other publicly accessible smart-home datasets with our dataset, based on the activities of daily living, user behaviour, energy consumption, network traffic, environmental sensors, and smart devices.…”

Section: Related Workmentioning

confidence: 99%

Dataset for cyber–physical anomaly detection in smart homes

Majib,

Alosaimi,

Asaturyan

et al. 2023

Front. Internet Things

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“…Participants tag five generic activities (eating, sleeping, working, leisure or personal). Another interesting database could be MARBLE [ 20 ]. In this database, the authors collect information from 8 environmental sensors and the information published by a smart watch.…”

Section: Related Workmentioning

confidence: 99%

SDHAR-HOME: A Sensor Dataset for Human Activity Recognition at Home

Ramos

Domingo

Zalama

et al. 2022

Sensors

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Nowadays, one of the most important objectives in health research is the improvement of the living conditions and well-being of the elderly, especially those who live alone. These people may experience undesired or dangerous situations in their daily life at home due to physical, sensorial or cognitive limitations, such as forgetting their medication or wrong eating habits. This work focuses on the development of a database in a home, through non-intrusive technology, where several users are residing by combining: a set of non-intrusive sensors which captures events that occur in the house, a positioning system through triangulation using beacons and a system for monitoring the user’s state through activity wristbands. Two months of uninterrupted measurements were obtained on the daily habits of 2 people who live with a pet and receive sporadic visits, in which 18 different types of activities were labelled. In order to validate the data, a system for the real-time recognition of the activities carried out by these residents was developed using different current Deep Learning (DL) techniques based on neural networks, such as Recurrent Neural Networks (RNN), Long Short-Term Memory networks (LSTM) or Gated Recurrent Unit networks (GRU). A personalised prediction model was developed for each user, resulting in hit rates ranging from 88.29% to 90.91%. Finally, a data sharing algorithm has been developed to improve the generalisability of the model and to avoid overtraining the neural network.

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The MARBLE Dataset: Multi-inhabitant Activities of Daily Living Combining Wearable and Environmental Sensors Data

Cited by 10 publications

References 23 publications

Review on Human Action Recognition in Smart Living: Sensing Technology, Multimodality, Real-Time Processing, Interoperability, and Resource-Constrained Processing

Review on Human Action Recognition in Smart Living: Sensing Technology, Multimodality, Real-Time Processing, Interoperability, and Resource-Constrained Processing

Dataset for cyber–physical anomaly detection in smart homes

SDHAR-HOME: A Sensor Dataset for Human Activity Recognition at Home

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