Evaluation of Inertial Sensor Configurations for Wearable Gait Analysis

Zhao, Hongyu; Wang, Zhelong; Qiu, Sen; Li, Jie; Gao, Fang; Wang, Jianjun

doi:10.1007/978-3-030-24405-7_13

Cited by 4 publications

(5 citation statements)

References 22 publications

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“…4) Contribution of machine learning algorithms in wearable sensor gait analysis and current challenges. : Machine learning algorithms have been employed in several studies, where they have been mostly used for classification problems such as gait events or gait phase classification [60], [80], [96], [108] and gait activity/terrain classification [111]. We also identified a few other applications such as verification of frailty discrimination [61], joint angle correction [104], false swing phase detection [107], and scoring algorithms in gait ataxia [97].…”

Section: Discussionmentioning

confidence: 99%

“…For example, Butterworth low-pass filters with various orders and cut-off frequencies were commonly applied in these studies. The sliding window technique was implemented in studies proposing a real-time processing approach [37] or in data preparation for thresholdbased algorithms [76], [108].…”

Section: F Algorithmsmentioning

confidence: 99%

“…Several studies have implemented ML-based algorithms such as SVM [60], [80], [104], RF [96], [97], [111], NN [96], [104], [108], kNN [80], [107], k-means clustering [61], decision tree [80], [104], logistic regression [96], naïve Bayes, and LDA [80] in their proposed framework for quantitative gait analysis using wearable sensors. Most of the studies used ML-based algorithms for classification problems such as gait event/gait phase classification [60], [80], [96], [108], and classification of gait on different terrains [111]. Further, we identified four studies that employed an ML-based algorithm as a supporting algorithm for performing operations such as crosschecking [61], correction [104], and false phase detection [107].…”

Section: F Algorithmsmentioning

confidence: 99%

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Recent Advances in Quantitative Gait Analysis Using Wearable Sensors: A Review

2021

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The current gold standard for gait analysis involves performing the gait experiments in a laboratory environment with a constrained space. However, there is growing interest in using flexible, efficient, and inexpensive wearable sensors as tools to perform gait analysis. This review aimed to identify and summarize the current advances in wearable sensors for various aspects of gait analysis, such as the application of wearable gait analysis systems, sensor systems and their attachment locations, and the algorithms used for the analysis. The PRISMA guideline was adopted to find relevant studies from the period 2011 to 2020 from several scientific databases. A total of 76 articles were selected based on the inclusion and exclusion criteria. A wearable inertial measurement unit (IMU) attached to the lower limb region was found to be the most common approach for gait analysis. Temporal, spatial, and spatiotemporal features were the most common quantitative gait features extracted from the wearable sensors. The proposed frameworks showed varying performances, and an increased number of sensors did not necessarily improve the estimation performance metrics. A few studies have integrated various machine learning techniques for classification problems, correction algorithms, crosschecking functions, and scoring functions. Finally, this review paper discusses the challenges and future direction of the research on quantitative gait analysis.

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Section: Discussionmentioning

confidence: 99%

Section: F Algorithmsmentioning

confidence: 99%

Section: F Algorithmsmentioning

confidence: 99%

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Recent Advances in Quantitative Gait Analysis Using Wearable Sensors: A Review

2021

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“…Although the NN-/HMM-based hybrid method is computationally complex for training, it is computationally efficient at runtime. It requires no careful sensor alignment or parameter adjustment and generalizes well to new subjects, new gaits, new sensors, and new sensor locations [51].…”

Section: Nn-/hmm-based Hybrid Gait Modelmentioning

confidence: 99%

Applications of MEMS Gyroscope for Human Gait Analysis

Zhao¹,

Qiu²,

Wang³

et al. 2020

Gyroscopes - Principles and Applications

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After decades of development, quantitative instruments for human gait analysis have become an important tool for revealing underlying pathologies manifested by gait abnormalities. However, the gold standard instruments (e.g., optical motion capture systems) are commonly expensive and complex while needing expert operation and maintenance and thereby be limited to a small number of specialized gait laboratories. Therefore, in current clinical settings, gait analysis still mainly relies on visual observation and assessment. Due to recent developments in microelectromechanical systems (MEMS) technology, the cost and size of gyroscopes are decreasing, while the accuracy is being improved, which provides an effective way for qualifying gait features. This chapter aims to give a close examination of human gait patterns (normal and abnormal) using gyroscope-based wearable technology. Both healthy subjects and hemiparesis patients participated in the experiment, and experimental results show that foot-mounted gyroscopes could assess gait abnormalities in both temporal and spatial domains. Gait analysis systems constructed of wearable gyroscopes can be more easily used in both clinical and home environments than their gold standard counterparts, which have few requirements for operation, maintenance, and working environment, thereby suggesting a promising future for gait analysis.

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“…As aplicações da Ciência de Dados, a Análise de Dados e a IA na área da saúde são muito amplas, e podemos citar o exemplo, da detecção de células mamárias anormais (ZHU et al, 2017), no desenvolvimento de sistemas de reabilitação do antebraço (ZHU et al, 2018), na análise da marcha humana com sensores inerciais (ZHAO et al, 2020)…”

Section: Aplicações Da Indústria 40unclassified

Proposta metodológica de otimização baseada em IA, ciência de dados e indústria 4.0. estudo de casos

Flórez¹

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Este trabalho não poderia ser terminado sem a ajuda de diversas pessoas às quais presto minha homenagem: Ao Deus, por tudo o que Ele fez na minha vida.Aos meus pais, Luis Fernando Cáceres e Martha Cecilia Flórez; irmãos, Julian e María Alejandra Cáceres pelo incentivo, paciência e voz de alento para continuar em todos os momentos de minha vida. A Olga Szczygielska por sua paciência, dedicação e apoio incondicional no decorrer deste caminho.Ao meu orientador, Prof. Dr. João Mauricio Rosário pela confiança, orientação e o apoio ao longo do trabalho desenvolvido, principalmente por seus conselhos e amizade durante minha formação acadêmica. Sua dedicação e ajuda permitiram transpor os desafios deste trabalho e promover meu crescimento pessoal, acadêmico e profissional. Aos Prof. Dario Amaya e Olga Ramos, e a meu colega e amigo Jefry Mora, pela ajuda, amizade e apoio ao longo de minhas pesquisas e trabalhos.Aos meus colegas e amigos, principalmente a meus colegas de laboratório por seu companheirismo e apoio.Aos membros da banca examinadora pela disposição e interesse no meu trabalho.Aos professores e funcionários do Departamento de Sistemas Integrados da Faculdade de Engenharia Mecânica da UNICAMP, com os que tive a oportunidade de trabalhar.Ao CNPq (Conselho Nacional de Desenvolvimento Científico e Tecnológico) pela concessão da bolsa de estudos de Doutorado, processo 140743/2017-0, a qual me permitiu uma dedicação integral ao desenvolvimento deste trabalho. ResumoEste trabalho de pesquisa apresenta uma proposta metodológica de otimização aplicada à engenharia de sistemas utilizando conceitos de inteligência artificial, ciência de dados e indústria 4.0. Esta proposta foi validada através de dois estudos de casos correspondentes a implementação de um sistema inteligente para o serviço de emergência em e-hospital, e ao gerenciamento do abastecimento energético de uma cidade utilizando fontes híbridas. A abordagem proposta utiliza conceitos da Industria 4.0, pesquisa operacional, ciência de dados e inteligência artificial. A metodologia apresentada segue uma abordagem sistêmica, facilitando a melhoria de sistemas complexos em engenharia, tais como os sistemas estocásticos. O estudo de caso relacionado com o gerenciamento inteligente de um e-Hospital, aborda o sistema de emergência hospitalar, controlando de forma inteligente a gestão de pessoal, particularmente nas áreas chave e gargalos existentes nos sistemas em estudo. O uso de conceitos de telemedicina, controle automático e algoritmos genéticos permitiram encontrar uma solução flexível e robusta para situações de baixo e alto fluxo de pacientes. O segundo estudo de caso permitiu projetar um sistema híbrido de geração de energia, utilizando fontes renováveis e um sistema de armazenamento com baterias para o abastecimento de uma população selecionada.As decisões tomadas para projetar o sistema de geração de energia foram baseadas na análise de bancos de dados relacionados. A melhoria proposta para o sistema inteligente de distribuição de energia utilizou técnicas de...

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Evaluation of Inertial Sensor Configurations for Wearable Gait Analysis

Cited by 4 publications

References 22 publications

Recent Advances in Quantitative Gait Analysis Using Wearable Sensors: A Review

Recent Advances in Quantitative Gait Analysis Using Wearable Sensors: A Review

Applications of MEMS Gyroscope for Human Gait Analysis

Proposta metodológica de otimização baseada em IA, ciência de dados e indústria 4.0. estudo de casos

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