scholarly journals Proprioceptive Feedback and Preferred Patterns of Human Movement

2013 ◽  
Vol 41 (1) ◽  
pp. 36-43 ◽  
Author(s):  
Jesse C. Dean
1999 ◽  
Vol 13 (4) ◽  
pp. 234-244
Author(s):  
Uwe Niederberger ◽  
Wolf-Dieter Gerber

Abstract In two experiments with four and two groups of healthy subjects, a novel motor task, the voluntary abduction of the right big toe, was trained. This task cannot usually be performed without training and is therefore ideal for the study of elementary motor learning. A systematic variation of proprioceptive, tactile, visual, and EMG feedback was used. In addition to peripheral measurements such as the voluntary range of motion and EMG output during training, a three-channel EEG was recorded over Cz, C3, and C4. The movement-related brain potential during distinct periods of the training was analyzed as a central nervous parameter of the ongoing learning process. In experiment I, we randomized four groups of 12 subjects each (group P: proprioceptive feedback; group PT: proprioceptive and tactile feedback; group PTV: proprioceptive, tactile, and visual feedback; group PTEMG: proprioceptive, tactile, and EMG feedback). Best training results were reported from the PTEMG and PTV groups. The movement-preceding cortical activity, in the form of the amplitude of the readiness potential at the time of EMG onset, was greatest in these two groups. Results of experiment II revealed a similar effect, with a greater training success and a higher electrocortical activation under additional EMG feedback compared to proprioceptive feedback alone. Sensory EMG feedback as evaluated by peripheral and central nervous measurements appears to be useful in motor training and neuromuscular re-education.


Author(s):  
Guanis de Barros Vilela Junior ◽  
Carlos Henrique Prevital Fileni ◽  
Ricardo Pablo Passos

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.


2018 ◽  
Vol 30 (6) ◽  
pp. 1073
Author(s):  
Guodao Sun ◽  
Fen Liu ◽  
Li Jiang ◽  
Ronghua Liang

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2383 ◽  
Author(s):  
Chi Cuong Vu ◽  
Jooyong Kim

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.


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