Hip Joint Angles and Moments during Stair Ascent Using Neural Networks and Wearable Sensors

McCabe, Megan V.; Citters, Douglas W. Van; Chapman, David M. F.

doi:10.3390/bioengineering10070784

Cited by 3 publications

(1 citation statement)

References 59 publications

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“…In terms of sensor system design, commonly used sensors include inertial sensors (IMUs) [ 3 , 4 , 5 ], plantar pressure sensors [ 6 ], surface electromyography electrodes [ 7 , 8 ], and electroencephalogram electrodes [ 9 ]. IMUs are widely used in gait motion recognition for their high portability, low cost, and environmental friendliness [ 10 , 11 , 12 , 13 , 14 ].…”

Section: Introductionmentioning

confidence: 99%

Gait Recognition and Assistance Parameter Prediction Determination Based on Kinematic Information Measured by Inertial Measurement Units

Xiang,

Wang,

Liu

et al. 2024

Bioengineering

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The gait recognition of exoskeletons includes motion recognition and gait phase recognition under various road conditions. The recognition of gait phase is a prerequisite for predicting exoskeleton assistance time. The estimation of real-time assistance time is crucial for the safety and accurate control of lower-limb exoskeletons. To solve the problem of predicting exoskeleton assistance time, this paper proposes a gait recognition model based on inertial measurement units that combines the real-time motion state recognition of support vector machines and phase recognition of long short-term memory networks. A recognition validation experiment was conducted on 30 subjects to determine the reliability of the gait recognition model. The results showed that the accuracy of motion state and gait phase were 99.98% and 98.26%, respectively. Based on the proposed SVM-LSTM gait model, exoskeleton assistance time was predicted. A test was conducted on 10 subjects, and the results showed that using assistive therapy based on exercise status and gait stage can significantly improve gait movement and reduce metabolic costs by an average of more than 10%.

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Section: Introductionmentioning

confidence: 99%

Gait Recognition and Assistance Parameter Prediction Determination Based on Kinematic Information Measured by Inertial Measurement Units

Xiang,

Wang,

Liu

et al. 2024

Bioengineering

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Machine Learning in Biomaterials, Biomechanics/Mechanobiology, and Biofabrication: State of the Art and Perspective

Wu,

Xu,

Fang

et al. 2024

Arch Computat Methods Eng

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In the past three decades, biomedical engineering has emerged as a significant and rapidly growing field across various disciplines. From an engineering perspective, biomaterials, biomechanics, and biofabrication play pivotal roles in interacting with targeted living biological systems for diverse therapeutic purposes. In this context, in silico modelling stands out as an effective and efficient alternative for investigating complex interactive responses in vivo. This paper offers a comprehensive review of the swiftly expanding field of machine learning (ML) techniques, empowering biomedical engineering to develop cutting-edge treatments for addressing healthcare challenges. The review categorically outlines different types of ML algorithms. It proceeds by first assessing their applications in biomaterials, covering such aspects as data mining/processing, digital twins, and data-driven design. Subsequently, ML approaches are scrutinised for the studies on mono-/multi-scale biomechanics and mechanobiology. Finally, the review extends to ML techniques in bioprinting and biomanufacturing, encompassing design optimisation and in situ monitoring. Furthermore, the paper presents typical ML-based applications in implantable devices, including tissue scaffolds, orthopaedic implants, and arterial stents. Finally, the challenges and perspectives are illuminated, providing insights for academia, industry, and biomedical professionals to further develop and apply ML strategies in future studies.

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Perioperative nursing care for hip arthroplasty patients with concomitant hypertension: A minireview

Ji,

Yang

2023

World J Clin Cases

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Hip replacement (HA) is mainly indicated for the elderly, who generally suffer from various underlying diseases such as hypertension. This article provides a review of the key points of perioperative nursing care for patients with hypertension undergoing HA. It analyzes the key points of care during the perioperative period (preoperative, intraoperative, and postoperative) and proposes directions for the development of perioperative nursing care for HA. The prognosis for patients can be improved through the modification of traditional medical approaches and the application of new technologies and concepts.

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Hip Joint Angles and Moments during Stair Ascent Using Neural Networks and Wearable Sensors

Cited by 3 publications

References 59 publications

Gait Recognition and Assistance Parameter Prediction Determination Based on Kinematic Information Measured by Inertial Measurement Units

Gait Recognition and Assistance Parameter Prediction Determination Based on Kinematic Information Measured by Inertial Measurement Units

Machine Learning in Biomaterials, Biomechanics/Mechanobiology, and Biofabrication: State of the Art and Perspective

Perioperative nursing care for hip arthroplasty patients with concomitant hypertension: A minireview

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