LSTM-Based Lower Limbs Motion Reconstruction Using Low-Dimensional Input of Inertial Motion Capture System

Tong, Lina; Liu, Rongkai; Liang, Peng

doi:10.1109/jsen.2019.2959639

Cited by 15 publications

(4 citation statements)

References 29 publications

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“…Learning meaningful medical ontology representations within the EHR database can alleviate the data insufficiency problem, and the learned embeddings can cluster nicely into particular groups of diseases [ 151 ]. With simulation and motion data synthesis, digital twin models can be established for patient performance estimation and rehabilitation program planning to individual needs [ 152 , 153 ]. The twin model can serve as a counterpart of the patients to improve the wearability of sensor systems and transfer detailed human movement characteristics to real-world applications to reduce errors.…”

Section: Discussion and Future Directionsmentioning

confidence: 99%

IMU-Based Monitoring for Assistive Diagnosis and Management of IoHT: A Review

Yerebakan

Dai

et al. 2022

Healthcare

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With the rapid development of Internet of Things (IoT) technologies, traditional disease diagnoses carried out in medical institutions can now be performed remotely at home or even ambient environments, yielding the concept of the Internet of Health Things (IoHT). Among the diverse IoHT applications, inertial measurement unit (IMU)-based systems play a significant role in the detection of diseases in many fields, such as neurological, musculoskeletal, and mental. However, traditional numerical interpretation methods have proven to be challenging to provide satisfying detection accuracies owing to the low quality of raw data, especially under strong electromagnetic interference (EMI). To address this issue, in recent years, machine learning (ML)-based techniques have been proposed to smartly map IMU-captured data on disease detection and progress. After a decade of development, the combination of IMUs and ML algorithms for assistive disease diagnosis has become a hot topic, with an increasing number of studies reported yearly. A systematic search was conducted in four databases covering the aforementioned topic for articles published in the past six years. Eighty-one articles were included and discussed concerning two aspects: different ML techniques and application scenarios. This review yielded the conclusion that, with the help of ML technology, IMUs can serve as a crucial element in disease diagnosis, severity assessment, characteristic estimation, and monitoring during the rehabilitation process. Furthermore, it summarizes the state-of-the-art, analyzes challenges, and provides foreseeable future trends for developing IMU-ML systems for IoHT.

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Section: Discussion and Future Directionsmentioning

confidence: 99%

IMU-Based Monitoring for Assistive Diagnosis and Management of IoHT: A Review

Yerebakan

Dai

et al. 2022

Healthcare

View full text Add to dashboard Cite

show abstract

“…Learning meaningful medical ontology representations within the EHR database can alleviate the data insufficiency problem and the learned embeddings can cluster nicely into particular groups of diseases [150]. With simulation and motion data synthesis, digital twin models can be established for patient performance estimation and rehabilitation program planning to individual needs [151], [152]. The twin model can serve as a counterpart of the patients to improve the wearability of sensor systems and transfer detailed human movement characteristics to real-world applications to reduce errors.…”

Section: B Data Processing Methodsmentioning

confidence: 99%

IMU-Based Monitoring for Assistive Disease Diagnosis and Management of IoHT: A Review

Fan¹,

Yerebakan²,

Dai³

et al. 2022

Preprint

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With the rapid development of Internet of Things (IoT) technologies, traditional disease diagnosis carried out in medical institutions can now be performed remotely at home or even ambient environments, yielding the concept of Internet of Health Things (IoHT). Among the diverse IoHT applications, inertial measurement units (IMUs) based systems play a significant role in the detection of diseases in many fields, such as neurological, musculoskeletal, and mental. However, traditional numerical interpretation methods have proven to be challenging to provide satisfying detection accuracies owing to the low quality of raw data especially under strong electromagnetic interference (EMI). To address this issue, in recent years, machine learning (ML) based techniques have been proposed to smartly map IMU captured data on disease detection and progress. After a decade of development, the combination of IMUs and ML algorithms for assistive disease diagnosis has become a hot topic, with an increasing number of studies reported yearly. To help readers comprehensively understand the fundamentals and state-of-the-art techniques, this article systematically reviews this field, by introducing and explaining relevant application scenarios, discussing challenges, and predicting foreseeable future trends. <br>

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“…Therefore, the study applies OpenCV software with high robustness to the construction of human pose recognition system, and uses VGGNet-19, which can better adapt to different resolution image data, as the feature extractor. openPose extracts multiple key points from a single image frame simultaneously when performing human body recognition, and applies the affinity field method with realtime and targeting to the two-dimensional pose image of human body to The VGGNet-19 network is a deeper structured convolutional network, and its expansion of the network depth enables better network performance [21]. The VGGNet-19 network can be used to extract the image features and explore their depth of meaning.…”

Section: Design Of Improved Human Posture Recognition Algorithm and R...mentioning

confidence: 99%

Application of Fashion Element Trend Prediction Model Integrating AM and EfficientNet-b7 Models in Art Design

Yan,

Cao,

2024

SCPE

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The poor application effect of traditional sports training methods and the difficulty of recording data due to the time and space constraints of sports make it difficult for trainers to improve their learning outcomes. Based on this, the study proposes to apply human posture recognition in sports teaching design, and use VGGNet-19 as a feature extractor and OpenCV open source software to capture posture movements, and introduce the concepts of joint angle and movement similarity to design a sports assessment system for physical education based on the geometric spatial feature variability analysis of posture based on limb angle information. The testing outcomes demonstrate that the study’s improved gesture recognition algorithm has a recognition rate of more than 90% on gesture movements, and the maximum recognition error value (0.010) is smaller than that of the dynamic time-regularised gesture algorithm (0.014) and the convolutional neural network algorithm (0.017). The assessment system is also better able to improve students’ professional performance and satisfaction, with its average professional score and satisfaction reaching 86 and 92%, which is significantly better than other comparative algorithms. The method is effective in providing trainers with data-based training scenarios and helping them to improve their learning in sport.

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LSTM-Based Lower Limbs Motion Reconstruction Using Low-Dimensional Input of Inertial Motion Capture System

Cited by 15 publications

References 29 publications

IMU-Based Monitoring for Assistive Diagnosis and Management of IoHT: A Review

IMU-Based Monitoring for Assistive Diagnosis and Management of IoHT: A Review

IMU-Based Monitoring for Assistive Disease Diagnosis and Management of IoHT: A Review

Application of Fashion Element Trend Prediction Model Integrating AM and EfficientNet-b7 Models in Art Design

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