Personalized models for human activity recognition with wearable sensors: deep neural networks and signal processing

Gholamiangonabadi, Davoud; Grolinger, Katarina

doi:10.1007/s10489-022-03832-6

Cited by 11 publications

(9 citation statements)

References 52 publications

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“…Muaaz et al 28 used CNN to extract information from Wi-Fi signals, which provided an intelligent sports behavior monitoring method for health information systems without wearing equipment. Gholamiangonabadi and Grolinger 29 established a personalized wearable sensor human activity recognition model, which highlighted the effectiveness of convolutional neural network in customized sports behavior analysis. Gangrade and Bharti 30 realized a vision-based Indian sign language gesture recognition by using CNN.…”

Section: Literature Reviewmentioning

confidence: 99%

Boxing behavior recognition based on artificial intelligence convolutional neural network with sports psychology assistant

Kong,

Duan

2024

Sci Rep

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The purpose of this study is to deeply understand the psychological state of boxers before the competition, and explore an efficient boxing action classification and recognition model supported by artificial intelligence (AI) technology through these psychological characteristics. Firstly, this study systematically measures the key psychological dimensions of boxers, such as anxiety level, self-confidence, team identity, and opponent attitude, through psychological scale survey to obtain detailed psychological data. Then, based on these data, this study innovatively constructs a boxing action classification and recognition model based on BERT fusion 3D-ResNet, which not only comprehensively considers psychological information, but also carefully considers action characteristics to improve the classification accuracy of boxing actions. The performance evaluation shows that the model proposed in this study is significantly superior to the traditional model in terms of loss value, accuracy and F1 value, and the accuracy reaches 96.86%. Therefore, through the comprehensive application of psychology and deep learning, this study successfully constructs a boxing action classification and recognition model that can fully understand the psychological state of boxers, which provides strong support for the psychological training and action classification of boxers.

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Section: Literature Reviewmentioning

confidence: 99%

Boxing behavior recognition based on artificial intelligence convolutional neural network with sports psychology assistant

Kong,

Duan

2024

Sci Rep

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“…Personalization is crucial in Smart Living technologies, particularly in the realm of HAR. Recognizing individual uniqueness when performing specific actions can lead to improved recognition accuracy and personalized experiences, overcoming the challenges of a "one-size-fits-all" approach [83]. Researchers have found that by identifying similarities between a target subject and individuals in a training set, emphasizing data from subjects with similar attributes can enhance the overall performance of HAR models [84].…”

Section: Personalizationmentioning

confidence: 99%

“…However, generic models often face performance deterioration when applied to new subjects. Studies have proposed personalized HAR models based on CNN and signal decomposition to address this challenge, achieving better accuracy than state-of-the-art CNN approaches with time-domain features [83]. In healthcare applications, personalization has been explored for classifying normal control individuals and early-stage dementia patients based on Activities of Daily Living (ADLs).…”

Section: Personalizationmentioning

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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“…Personalized healthcare monitoring has revolutionized medical services by allowing individuals to track their health in real‐time with wearable devices and mobile applications 11 . By doing so, individuals can monitor vital signs, activity levels, 12 and other health parameters, giving them a better understanding of their health status and empowering them to take proactive measures to prevent diseases and manage chronic conditions. An overall framework for DL‐based personalized health monitoring is depicted in Figure 1.…”

Section: Introductionmentioning

confidence: 99%

Deep learning for personalized health monitoring and prediction: A review

Damaševičius,

Jagatheesaperumal,

Kandala

et al. 2024

Computational Intelligence

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Personalized health monitoring and prediction are indispensable in advancing healthcare delivery, particularly amidst the escalating prevalence of chronic illnesses and the aging population. Deep learning (DL) stands out as a promising avenue for crafting personalized health monitoring systems adept at forecasting health outcomes with precision and efficiency. As personal health data becomes increasingly accessible, DL‐based methodologies offer a compelling strategy for enhancing healthcare provision through accurate and timely prognostications of health conditions. This article offers a comprehensive examination of recent advancements in employing DL for personalized health monitoring and prediction. It summarizes a diverse range of DL architectures and their practical implementations across various realms, such as wearable technologies, electronic health records (EHRs), and data accumulated from social media platforms. Moreover, it elucidates the obstacles encountered and outlines future directions in leveraging DL for personalized health monitoring, thereby furnishing invaluable insights into the immense potential of DL in this domain.

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Personalized models for human activity recognition with wearable sensors: deep neural networks and signal processing

Cited by 11 publications

References 52 publications

Boxing behavior recognition based on artificial intelligence convolutional neural network with sports psychology assistant

Boxing behavior recognition based on artificial intelligence convolutional neural network with sports psychology assistant

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

Deep learning for personalized health monitoring and prediction: A review

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