Human Activity Recognition Based on Multiple Kinects

Haller, Emanuela; Scarlat, Georgiana; Mocanu, Irina; Trăşcău, Mihai

doi:10.1007/978-3-642-41043-7_5

Cited by 8 publications

(10 citation statements)

References 8 publications

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“…Patients coexisted with older adults 9 of 26 times or 35% of found cases ( Figure 6 ). This shows that other ages benefited from AI models [ 44 , 72 ]. Frail persons were less present and coappeared with older adults in 43% (9/12) of instances.…”

Section: Discussionmentioning

confidence: 92%

“…They have served as alternatives to data-driven approaches (ie, clustering and regression) by promoting desirable behaviors and eliminating undesirable user behaviors. Hidden Markov models are the most common algorithms in applications, including user activity recognition from appliance consumption data [ 43 ] or with multiple Kinect devices [ 44 ].…”

Section: Resultsmentioning

confidence: 99%

“…Patients (n=26) were persons with health declines who underwent different medical treatments. Examples include activity prediction for fall prevention of patients at risk [ 44 ] and diagnosing clinical abnormalities of patients using multiple vital signs (eg, heart rate, blood pressure, and respiratory rate) [ 72 ]. The overlap with older adults (9 times; Figure 6 ) indicates that for most older people, the purpose of AAL systems was more assistive and aimed toward health promotion rather than therapy.…”

Section: Resultsmentioning

confidence: 99%

Section: Beneficiaries Of Ai Modelsmentioning

confidence: 99%

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Ambient Assisted Living: Scoping Review of Artificial Intelligence Models, Domains, Technology, and Concerns

Jovanovic¹,

Mitrov²,

Zdravevski³

et al. 2022

J Med Internet Res

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Background Ambient assisted living (AAL) is a common name for various artificial intelligence (AI)—infused applications and platforms that support their users in need in multiple activities, from health to daily living. These systems use different approaches to learn about their users and make automated decisions, known as AI models, for personalizing their services and increasing outcomes. Given the numerous systems developed and deployed for people with different needs, health conditions, and dispositions toward the technology, it is critical to obtain clear and comprehensive insights concerning AI models used, along with their domains, technology, and concerns, to identify promising directions for future work. Objective This study aimed to provide a scoping review of the literature on AI models in AAL. In particular, we analyzed specific AI models used in AАL systems, the target domains of the models, the technology using the models, and the major concerns from the end-user perspective. Our goal was to consolidate research on this topic and inform end users, health care professionals and providers, researchers, and practitioners in developing, deploying, and evaluating future intelligent AAL systems. Methods This study was conducted as a scoping review to identify, analyze, and extract the relevant literature. It used a natural language processing toolkit to retrieve the article corpus for an efficient and comprehensive automated literature search. Relevant articles were then extracted from the corpus and analyzed manually. This review included 5 digital libraries: IEEE, PubMed, Springer, Elsevier, and MDPI. Results We included a total of 108 articles. The annual distribution of relevant articles showed a growing trend for all categories from January 2010 to July 2022. The AI models mainly used unsupervised and semisupervised approaches. The leading models are deep learning, natural language processing, instance-based learning, and clustering. Activity assistance and recognition were the most common target domains of the models. Ambient sensing, mobile technology, and robotic devices mainly implemented the models. Older adults were the primary beneficiaries, followed by patients and frail persons of various ages. Availability was a top beneficiary concern. Conclusions This study presents the analytical evidence of AI models in AAL and their domains, technologies, beneficiaries, and concerns. Future research on intelligent AAL should involve health care professionals and caregivers as designers and users, comply with health-related regulations, improve transparency and privacy, integrate with health care technological infrastructure, explain their decisions to the users, and establish evaluation metrics and design guidelines. Trial Registration PROSPERO (International Prospective Register of Systematic Reviews) CRD42022347590; https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022347590

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Section: Discussionmentioning

confidence: 92%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Beneficiaries Of Ai Modelsmentioning

confidence: 99%

See 2 more Smart Citations

Ambient Assisted Living: Scoping Review of Artificial Intelligence Models, Domains, Technology, and Concerns

Jovanovic¹,

Mitrov²,

Zdravevski³

et al. 2022

J Med Internet Res

View full text Add to dashboard Cite

show abstract

“…Furthermore, they also feature an automatic, skeleton-based extrinsic registration mechanism and a small evaluation of the fusion results, however only with respect to the raw camera data without further ground truth. Multiple Kinects can also be used for action or activity recognition, as shown by Azis et al [2], Haller et al [9] or Hachaj et al [8]. Williamson et al [21] use a multi-Kinect setup for dismounted soldier training.…”

Section: Multi-depth-camera Skeleton Tracking System Use Casesmentioning

confidence: 98%

FusionKit

Rietzler

Geiselhart

Thomas³

et al. 2016

Proceedings of the 8th ACM SIGCHI Symposium on Engineering Interactive Computing Systems

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We present a toolkit for markerless skeleton tracking and marker-based object tracking utilizing data fusion with an arbitrary number of depth cameras. As depth-camera based skeletal tracking is always inaccurate due to technology limitations, our goal was to be able to preestimate systematic errors for given tracking situations to improve fusion. Previous work analyzed various aspects of depth camera accuracy, however to our best knowledge, there has been neither systematic error modelling nor an application of such a model for skeletal fusion. Our paper presents such a model for the Kinect v2 camera, by using statistical modelling on capture datasets using such cameras and a marker-based ground truth capture system. By applying this model, we are able to improve the overall accuracy of the fusion output by 68% by predicting data quality with an error of around 3.2 cm. Our toolkit is available for use by other researchers to easily create larger capture spaces with higher tracking accuracy based on the error model when compared to single depth cameras.

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Implementation of Human Action Recognition System Using Multiple Kinect Sensors

Kwon

Kim

et al. 2015

Lecture Notes in Computer Science

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Human Activity Recognition Based on Multiple Kinects

Cited by 8 publications

References 8 publications

Ambient Assisted Living: Scoping Review of Artificial Intelligence Models, Domains, Technology, and Concerns

Ambient Assisted Living: Scoping Review of Artificial Intelligence Models, Domains, Technology, and Concerns

FusionKit

Implementation of Human Action Recognition System Using Multiple Kinect Sensors

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