Dataset of acceleration signals recorded while performing activities of daily living

Climent-Pérez, Pau; Muñoz-Antón, Ángela M.; Poli, Angelica; Spinsante, Susanna; Flórez-Revuelta, Francisco

doi:10.1016/j.dib.2022.107896

Cited by 14 publications

(7 citation statements)

References 15 publications

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“…In this review, we have meticulously chosen several pertinent datasets extensively used by the research community for HAR studies. These datasets comprise: Opportunity [44], PAMAP2 [45], CASAS: Aruba [46], CASAS: Cairo [47], CASAS: Kyoto Daily life [48], CASAS: Kyoto Multiresident [49], CASAS: Milan [48], CASAS: Tokyo [50], CASAS: Tulum [48], WISDM [51], ExtraSensory [52], MHEALTH [53], UCF101 [54], HMDB51 [55], NTU RGB+D [56], SmartFABER [57], PAAL ADL Accelerometry [58], Houses: HA, HB, and HC. [59], UCI-HAR [60], Ordonez [61], Utwente [62], IITR-IAR [63].…”

Section: Common Publicly Available Datasetsmentioning

confidence: 99%

Human Action Recognition in Smart Living Services and Applications: Context Awareness, Data Availability, Personalization, and Privacy

Diraco

Rescio

Caroppo

et al. 2023

Sensors

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Smart living, an increasingly prominent concept, entails incorporating sophisticated technologies in homes and urban environments to elevate the quality of life for citizens. A critical success factor for smart living services and applications, from energy management to healthcare and transportation, is the efficacy of human action recognition (HAR). HAR, rooted in computer vision, seeks to identify human actions and activities using visual data and various sensor modalities. This paper extensively reviews the literature on HAR in smart living services and applications, amalgamating key contributions and challenges while providing insights into future research directions. The review delves into the essential aspects of smart living, the state of the art in HAR, and the potential societal implications of this technology. Moreover, the paper meticulously examines the primary application sectors in smart living that stand to gain from HAR, such as smart homes, smart healthcare, and smart cities. By underscoring the significance of the four dimensions of context awareness, data availability, personalization, and privacy in HAR, this paper offers a comprehensive resource for researchers and practitioners striving to advance smart living services and applications. The methodology for this literature review involved conducting targeted Scopus queries to ensure a comprehensive coverage of relevant publications in the field. Efforts have been made to thoroughly evaluate the existing literature, identify research gaps, and propose future research directions. The comparative advantages of this review lie in its comprehensive coverage of the dimensions essential for smart living services and applications, addressing the limitations of previous reviews and offering valuable insights for researchers and practitioners in the field.

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Section: Common Publicly Available Datasetsmentioning

confidence: 99%

Human Action Recognition in Smart Living Services and Applications: Context Awareness, Data Availability, Personalization, and Privacy

Diraco

Rescio

Caroppo

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…In this review, we have meticulously chosen several pertinent datasets extensively used by the research community for HAR studies. These datasets comprise: Opportunity [45], PAMAP2 [46], CASAS: Aruba [47], CASAS: Cairo [48], CASAS: Kyoto Daily life [49], CASAS: Kyoto Multiresident [50], CASAS: Milan [49], CASAS: Tokyo [51], CASAS: Tulum [49], WISDM [52], ExtraSensory [53], MHEALTH [54], UCF101 [55], HMDB51 [56], NTU RGB+D [57], SmartFABER [58], PAAL ADL Accelerometry [59], Houses: HA, HB, and HC. [60], UCI-HAR [61], Ordonez [62], Utwente [63], IITR-IAR [64].…”

Section: Common Publicly Available Datasetsmentioning

confidence: 99%

Human Action Recognition in Smart Living Services and Applications: Context Awareness, Data Availability, Personalization, and Privacy

Diraco¹,

Rescio²,

Caroppo³

et al. 2023

Preprint

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Smart Living, an increasingly prominent concept, entails incorporating sophisticated technologies in homes and urban environments to elevate the quality of life for citizens. A critical success factor for Smart Living services and applications, from energy management to healthcare and transportation, is the efficacy of human action recognition (HAR). HAR, rooted in computer vision, seeks to identify human actions and activities using visual data and various sensor modalities. This paper extensively reviews the literature on HAR in Smart Living services and applications, amalgamating key contributions and challenges while providing insights into future research directions. The review delves into the essential aspects of Smart Living, the state of the art in HAR, and the potential societal implications of this technology. Moreover, the paper meticulously examines the primary application sectors in Smart Living that stand to gain from HAR, such as smart homes, smart healthcare, and smart cities. By underscoring the significance of the four dimensions of Context Awareness, Data Availability, Personalization, and Privacy in HAR, this paper serves as a valuable resource for researchers and practitioners striving to advance Smart Living services and applications.

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“…For this study, a publicly accessible human activity recognition dataset called "PAAL ADL Accelerometry dataset" is used [22] [23]. The dataset consists of accelerometer measurements of 52 healthy participants.…”

Section: B Paal Adl Accelerometry Datasetmentioning

confidence: 99%

“…The measurements are recorded at 15Hz. In order to capture true nature of activities, participants are asked to wear this wrist worn accelerometer at home or office setting instead of a laboratory environment [22]. Participants did repetitions for every activity at average 5 times.…”

Section: B Paal Adl Accelerometry Datasetmentioning

confidence: 99%

Complex Human Activity Recognition Using a Local Weighted Approach

Asuroglu

2022

IEEE Access

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Due to the life expectancy increase, there will be a workforce shortage in elderly care sector in forthcoming years. Ambient Assisted Living (AAL) systems can cope with this issue. A subset of AAL, Human activity recognition (HAR) provides an efficient way to tackle this issue. It can help with evaluating general health and welfare of elderly by automatically tracking their activities. Lifelogging and home diary applications will reduce the load on physicians and caregivers. On the other hand, complex activities play a vital role as they have high level semantic characteristics that truly represent daily life of the user. The main objective is to track these high-level semantic motions with low-cost single sensor systems with efficient machine learning frameworks. To achieve this objective, a framework is proposed to predict complex human activities from a single sensor using a machine learning approach. Time and frequency features are extracted from PAAL ADL Accelerometry Dataset and fed to Locally Weighted Random Forest (LWRF) machine learning algorithm. This algorithm is a hybrid structure that utilizes local weighting by introducing neighboring samples on Random Forest tree building phases. Proposed approach achieved 91% accuracy for HAR and 91.3% for gender recognition, outperforming other machine learning algorithms and previous study on the same dataset. This is the first study that utilize a local weighted approach for accelerometer signal domain. For prospective application, proposed framework can be embedded in lifelogging and home diary applications in home environments to track mental status of elderlies.

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Dataset of acceleration signals recorded while performing activities of daily living

Cited by 14 publications

References 15 publications

Human Action Recognition in Smart Living Services and Applications: Context Awareness, Data Availability, Personalization, and Privacy

Human Action Recognition in Smart Living Services and Applications: Context Awareness, Data Availability, Personalization, and Privacy

Human Action Recognition in Smart Living Services and Applications: Context Awareness, Data Availability, Personalization, and Privacy

Complex Human Activity Recognition Using a Local Weighted Approach

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