Multi-modal gait: A wearable, algorithm and data fusion approach for clinical and free-living assessment

Çelik, Yunus; Stuart, Samuel; Woo, Wai Lok; Sejdić, Ervin; Godfrey, Alan

doi:10.1016/j.inffus.2021.09.016

Cited by 46 publications

(24 citation statements)

References 86 publications

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“…Our feet have a complex structure and support all the weight of our body during locomotion. Many factors, such as aging, injuries, and pathological diseases, can affect one’s walking style temporarily or permanently. , Gait analysis is an important locomotion study to recognize normal or pathological patterns of standing, walking, and running, , which has attracted increasing attention in a variety of application scenarios, including fall risk estimation, disease diagnosis, rehabilitation assessment, physical therapy, exercise training, and so on. − Normally, gait analysis is performed at a standard Gait Lab equipped with professional equipment, including high-speed cameras, accelerometers, gyroscopes, etc . However, these burdensome equipment and unnatural environment usually affect the walking style of the subject, skewing the gait features .…”

Section: Introductionmentioning

confidence: 99%

Breathable, Antibacterial, and Biocompatible Collagen Fiber Network Decorated with Zwitterionic Silver Nanoparticles for Plantar Pressure Monitoring

Xiang

Yang

et al. 2022

ACS Appl. Mater. Interfaces

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Foot plantar pressure monitoring and gait analysis are of great significant in footwear design, sport biomechanics, injury prevention, and early warning of disease. Flexible and wearable smart insoles pave a feasible way for these application scenarios. However, the majority of the currently developed smart insoles are composed of synthetic polymers (e.g., plastics, rubbers, etc.), leading to inevitable problems associated with air permeability, hygiene condition, biocompatibility, and wearing comfort. Here, a new paradigm of a natural collagen fiber network (CFN) with soft and breathable features, which can be obtained by facilely treating animal hides via conventional leather pretreatment process, is selected as substrate material for constructing smart insoles due to its high permeability and porosity. Further, biocompatible zwitterionic silver nanoparticles (AgNPs) with both carboxybetaine and catechol groups on the interface were designed for firmly and uniformly immobilization onto the hierarchical micro-/nanoscale fibers of CFN through mussel-inspired catechol/amino chemistry, giving rise to both good antibacterial property and pressure sensing capability of the resultant material. The finally developed smart insole by using the AgNPs decorated CFN exhibits good capability for plantar pressure mapping and gait feature analysis. Especially, the smart insole will be very suitable for pressure monitoring and gait analysis of a diabetic foot with sensitive skin that requires a high biocompatible and antibacterial environment.

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Section: Introductionmentioning

confidence: 99%

Breathable, Antibacterial, and Biocompatible Collagen Fiber Network Decorated with Zwitterionic Silver Nanoparticles for Plantar Pressure Monitoring

Xiang

Yang

et al. 2022

ACS Appl. Mater. Interfaces

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“…The proposed system can also help in detecting fall in elderly individuals and potential disease analysis by following a system such as [67,68].…”

Section: Discussionmentioning

confidence: 99%

Deep Human Motion Detection and Multi-Features Analysis for Smart Healthcare Learning Tools

et al. 2022

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Unhealthy lifestyle causes several chronic diseases in humans. Many products are introduced to avoid such illnesses and provide e-learning-based healthcare services. However, the main focus is still on providing comfortable and reliable solutions. Inertial measurement units (IMU) are considered as the most independent and non-intrusive way to monitor and analyze human health via motion patterns detection. Deep learning is also taken as an excellent tool to detect motion patterns from IMU data. In this paper, a deep-learning-based human motion detection approach for smart healthcare learning tool has been proposed. A novel hybrid descriptors-based pre-classification and multi-features analysis algorithm is proposed to classify the human motion for healthcare e-learning. For pre-processing, a quaternion-based filter is utilized to filter the IMU signals. An experiment is performed over the acceleration signals by using minimum and average gravity removal techniques. Next, signal segmentation of multiple time intervals has been applied to segment data and ultimately compare the results to decide which type provides better performance. Then, pre-classification is done using motion pattern identification in the form of active and passive patterns. During the features analysis phase, features have been extracted based on both active and passive motion patterns. Further, an orthogonal fuzzy neighborhood discriminant analysis technique has been used to reduce the dimensionality of the extracted feature vector. Finally, a deep learner known as long-short term memory has been applied to classify the actions of both active and passive motion features for healthcare e-learning systems. For this purpose, we utilized two datasets: REALDISP and wearable computing. The experimental results show that our proposed system for smart healthcare learning outperformed other state-of-the-art systems. The proposed implemented system provided 87.35% accuracy for REALDISP and 85.18% accuracy for wearable computing datasets. Furthermore, the classified motion patterns are provided to a smart healthcare advisor in order to provide live feedback about human health for immediate action.

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“…Negi et al ( 2021 ) implemented the fusion of pressure insole, IMU and EMG signals for analyzing different terrain walk. Using IMU and EMG, Celik et al ( 2022 ) introduced a novel data fusion algorithm for enabling gait analysis for both clinical and free-living assessments.…”

Section: Gait Analysis Methodologymentioning

confidence: 99%

Detection and assessment of Parkinson's disease based on gait analysis: A survey

Guo

Yang

Liu

et al. 2022

Front. Aging Neurosci.

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Neurological disorders represent one of the leading causes of disability and mortality in the world. Parkinson's Disease (PD), for example, affecting millions of people worldwide is often manifested as impaired posture and gait. These impairments have been used as a clinical sign for the early detection of PD, as well as an objective index for pervasive monitoring of the PD patients in daily life. This review presents the evidence that demonstrates the relationship between human gait and PD, and illustrates the role of different gait analysis systems based on vision or wearable sensors. It also provides a comprehensive overview of the available automatic recognition systems for the detection and management of PD. The intervening measures for improving gait performance are summarized, in which the smart devices for gait intervention are emphasized. Finally, this review highlights some of the new opportunities in detecting, monitoring, and treating of PD based on gait, which could facilitate the development of objective gait-based biomarkers for personalized support and treatment of PD.

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Multi-modal gait: A wearable, algorithm and data fusion approach for clinical and free-living assessment

Cited by 46 publications

References 86 publications

Breathable, Antibacterial, and Biocompatible Collagen Fiber Network Decorated with Zwitterionic Silver Nanoparticles for Plantar Pressure Monitoring

Breathable, Antibacterial, and Biocompatible Collagen Fiber Network Decorated with Zwitterionic Silver Nanoparticles for Plantar Pressure Monitoring

Deep Human Motion Detection and Multi-Features Analysis for Smart Healthcare Learning Tools

Detection and assessment of Parkinson's disease based on gait analysis: A survey

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