Sensorial System for Obtaining the Angles of the Human Movement in the Coronal and Sagittal Anatomical Planes

2017 ◽  
pp. 535-547
Author(s):  
David Alvarado ◽  
Leonel Corona ◽  
Saúl Muñoz ◽  
José Aquino
Keyword(s):  
Human Movement

REDES NEURAIS RECORRENTES: APLICABILIDADE NA ANÁLISE DO MOVIMENTO HUMANO E NA SAÚDE

2020 ◽  
pp. 1-7
Author(s):  
Guanis de Barros Vilela Junior ◽  
Carlos Henrique Prevital Fileni ◽  
Ricardo Pablo Passos
Keyword(s):  
Neural Network ◽  
Human Movement ◽  
Google Scholar ◽  
Recursive Neural Network

Um dos tipos de redes neurais artificiais (RNA) mais utilizados para análise de imagens são as Redes Neurais Recorrentes (RNR). Este artigo de revisão teve como objetivo mostrar como uma rede neural recorrente pode ser aplicada na área da saúde. Métodos: a busca pelos artigos foi realizada nas bases Scopus, ScienceDirect, PubMed, IEEE Xplore e google scholar, durante o mês fevereiro de 2020 com a seguinte sintaxe para os unitermos: Recursive Neural Network AND Human Movement. Foram encontrados 16 artigos que contemplavam os critérios de inclusão e exclusão, publicados entre 2011 e 2020. Resultados e discussão: As RNR são amplamente utilizadas no reconhecimento de caracteres e produção de textos de elevada qualidade; na identificação e estadiamento de doenças neurológicas como Parkinson e Alzheimer; na análise do movimento humano em situações esportivas ou não; no monitoramento de ecossistemas como florestas e plantações, vitais para a sobrevivência humana, dentre outros. Conclusão: concluímos que são enormes as possibilidades de aplicação das mesmas nos mais diferentes contextos. Isto acontece especialmente em relação à análise do movimento humano. O desafio está posto à ortopedia, educação física, fonoaudiologia, fisioterapia e áreas afins.

Visual Analysis of Human Movement: A Functional Region Perspective

2018 ◽  
Vol 30 (6) ◽  
pp. 1073
Author(s):  
Guodao Sun ◽  
Fen Liu ◽  
Li Jiang ◽  
Ronghua Liang
Keyword(s):  
Visual Analysis ◽  
Human Movement ◽  
Functional Region

scholarly journals Highly Sensitive E-Textile Strain Sensors Enhanced by Geometrical Treatment for Human Monitoring

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2383 ◽  
Author(s):  
Chi Cuong Vu ◽  
Jooyong Kim
Keyword(s):  
Form Factors ◽  
Human Movement ◽  
Fast Response ◽  
Strain Sensors ◽  
Electronic Textiles ◽  
Smart Textiles ◽  
Electronic Manufacturing ◽  
Starting Point ◽  
Textile Sensors ◽  
Manufacturing Techniques

Electronic textiles, also known as smart textiles or smart fabrics, are one of the best form factors that enable electronics to be embedded in them, presenting physical flexibility and sizes that cannot be achieved with other existing electronic manufacturing techniques. As part of smart textiles, e-sensors for human movement monitoring have attracted tremendous interest from researchers in recent years. Although there have been outstanding developments, smart e-textile sensors still present significant challenges in sensitivity, accuracy, durability, and manufacturing efficiency. This study proposes a two-step approach (from structure layers and shape) to actively enhance the performance of e-textile strain sensors and improve manufacturing ability for the industry. Indeed, the fabricated strain sensors based on the silver paste/single-walled carbon nanotube (SWCNT) layers and buffer cutting lines have fast response time, low hysteresis, and are six times more sensitive than SWCNT sensors alone. The e-textile sensors are integrated on a glove for monitoring the angle of finger motions. Interestingly, by attaching the sensor to the skin of the neck, the pharynx motions when speaking, coughing, and swallowing exhibited obvious and consistent signals. This research highlights the effect of the shapes and structures of e-textile strain sensors in the operation of a wearable e-textile system. This work also is intended as a starting point that will shape the standardization of strain fabric sensors in different applications.

Matching shape sequences in video with applications in human movement analysis

2005 ◽  
Vol 27 (12) ◽  
pp. 1896-1909 ◽  
Author(s):  
Veeraraghavan A ◽  
A.K. Roy-Chowdhury ◽  
R. Chellappa
Keyword(s):  
Movement Analysis ◽  
Human Movement ◽  
Human Movement Analysis

scholarly journals Filtering Biomechanical Signals in Movement Analysis

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4580
Author(s):  
Francesco Crenna ◽  
Giovanni Battista Rossi ◽  
Marta Berardengo
Keyword(s):  
Measurement Uncertainty ◽  
Dimensional Space ◽  
Three Dimensional ◽  
Movement Analysis ◽  
Analytical Signal ◽  
Human Movement ◽  
Reference Points ◽  
Biomechanical Analysis ◽  
Method Performance ◽  
Acceleration Signal

Biomechanical analysis of human movement is based on dynamic measurements of reference points on the subject’s body and orientation measurements of body segments. Collected data include positions’ measurement, in a three-dimensional space. Signal enhancement by proper filtering is often recommended. Velocity and acceleration signal must be obtained from position/angular measurement records, needing numerical processing effort. In this paper, we propose a comparative filtering method study procedure, based on measurement uncertainty related parameters’ set, based upon simulated and experimental signals. The final aim is to propose guidelines to optimize dynamic biomechanical measurement, considering the measurement uncertainty contribution due to the processing method. Performance of the considered methods are examined and compared with an analytical signal, considering both stationary and transient conditions. Finally, four experimental test cases are evaluated at best filtering conditions for measurement uncertainty contributions.

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