Design of active ankle foot orthotics for gait assistance and fall prevention

IF 0.8 4区 计算机科学 Q4 AUTOMATION & CONTROL SYSTEMS International Journal of Robotics & Automation Pub Date : 2020-08-27 DOI:10.15406/IRATJ.2020.06.00209
S. Redkar, Jason Olson, Sambarta Ray, T. Sugar, Claire F. Honeycutt
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引用次数: 2

Abstract

Certain groups of people are more susceptible to experience fall than others, one of which being stroke survivors. Although the risk of stroke increases with age, a stroke can occur at any age, and the most common type of stroke inhibits blood flow to the brain.2 The brain is responsible for sending motor signals to muscles through the nervous system required for muscle movement. After a stroke, these signals get affected and can delay muscle, both kinetic and kinematic responses to perturbation.1 Partial or complete loss of muscle activity (paresis) in the lower limb or limbs can result in some cases. This loss of muscle activity leads to motor-issues such as Drop foot (i.e. inability to lift the impaired foot during swing phase of gait) or Spasticity (i.e. stiffness and tightening of muscles) of lower limb muscles. These issues lead to the loss of postural control3 increasing fall risk among those affected. Stroke is not uncommon either, with around 795,000 people in the United States suffering from stroke annually according to the Centers for Disease Control and Prevention (CDC). Falls are the common complaints that individuals with such impairment have during or after their rehabilitation phase. Statistics on after stroke falls4 reveal that 14%-65% patients fall during hospitalization and between 37%-73% fall during the first 6 months of discharge from hospital. The most commonly prescribed clinical remedy provided to tackle drop foot issue and improper gait is passive thermoplastic Ankle Foot Orthosis (AFO) that is designed to lock the paretic ankle joint at a certain angle, facilitate foot clearance during swing phase, ankle stability during stance phase and heel strike. While there are reported improvements of gait velocity, stride length and cadence(steps/min) shown after the use of such AFOs, studies5 show that continual constraints in the ankle joint adversely affects the compensatory stepping response, forward propulsion and proprioceptive sensory information. The primary contributor to the kinetic energy and the speed of the stepping leg is forward propulsion force which is generated by the plantar flexor muscles. Locking the ankle joint using rigid AFOs lead to impeded forward propulsion due to restricted plantar flexion and that can cause inhibited compensatory stepping response, inadequate foot clearance and improper gait. Therefore, there is a need for an Active Ankle Foot Orthotics (AAFO) which can deliver powered push off for a stronger plantar flexion, locking of the ankle joint during swing phase to prevent dragging of the paretic foot and a stable heel strike.
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主动踝关节足矫形器的步态辅助和跌倒预防设计
某些人群比其他人更容易跌倒,其中之一就是中风幸存者。尽管中风的风险随着年龄的增长而增加,但中风可以发生在任何年龄,最常见的中风类型是抑制血液流向大脑大脑负责通过肌肉运动所需的神经系统向肌肉发送运动信号。中风后,这些信号受到影响,可以延迟肌肉对扰动的动力学和运动学反应在某些情况下,下肢或四肢部分或完全丧失肌肉活动(轻瘫)可导致。这种肌肉活动的丧失导致运动问题,如落脚(即在步态摇摆阶段无法抬起受损的脚)或下肢肌肉痉挛(即肌肉僵硬和收紧)。这些问题会导致失去对姿势的控制,从而增加患者跌倒的风险。中风也并不罕见,根据美国疾病控制与预防中心(CDC)的数据,美国每年约有79.5万人患中风。跌倒是有这种损伤的人在康复期间或之后的常见抱怨。有关中风后跌倒的统计数据4显示,14%-65%的患者在住院期间跌倒,37%-73%的患者在出院后的前6个月内跌倒。最常用的临床治疗方法是被动式热塑性踝关节矫形器(AFO),用于将麻痹的踝关节锁定在一定角度,促进摇摆阶段的足部间隙,站立阶段和脚跟撞击阶段的踝关节稳定性。虽然有报道称,使用这种afo后,步态速度、步幅和节奏(步数/分钟)有所改善,但研究5表明,踝关节持续受限会对代偿性步进反应、向前推进和本体感觉信息产生不利影响。主要贡献者的动能和步伐腿的速度是向前推进力,这是由足底屈肌产生的。使用刚性afo锁定踝关节,由于足底屈曲受限,导致向前推进受阻,并可能导致代偿性步进反应抑制,足部间隙不足和步态不当。因此,需要一种主动踝关节足矫形器(AAFO),它可以为更强的足底屈曲提供动力,在摆动阶段锁定踝关节,以防止拖拽麻痹的脚和稳定的脚跟撞击。
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来源期刊
CiteScore
1.20
自引率
44.40%
发文量
71
审稿时长
8 months
期刊介绍: First published in 1986, the International Journal of Robotics and Automation was one of the inaugural publications in the field of robotics. This journal covers contemporary developments in theory, design, and applications focused on all areas of robotics and automation systems, including new methods of machine learning, pattern recognition, biologically inspired evolutionary algorithms, fuzzy and neural networks in robotics and automation systems, computer vision, autonomous robots, human-robot interaction, microrobotics, medical robotics, mobile robots, biomechantronic systems, autonomous design of robotic systems, sensors, communication, and signal processing.
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