Online Prediction of Gait Related Trips Post-Stroke

中风后步态相关行程的在线预测

• 批准号: 10022146
• 负责人: MICHAEL D LEWEK
• 金额: $ 18.21万
• 依托单位: UNIV OF NORTH CAROLINA CHAPEL HILL
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-23 至 2023-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10022146
• 关键词: Affect; Algorithms; Attention; Behavior; Controlled Environment; Custom; Data; Development; Educational process of instructing; Elderly; Engineering; Ensure; Environment; Equilibrium; Event; Exhibits; Fall prevention; Future; Gait; Goals; Impairment; Individual; Intervention; Joints; Limb structure; Methods; Monitor; Motion; Online Systems; Participant; Pelvis; Phase; Populations at Risk; Prevention strategy; Preventive; Reaction; Reaction Time; Recovery; Research; Research Personnel; Self-Help Devices; Skeletal Muscle; Speed; Stroke; System; Techniques; Testing; Thigh structure; Time; Toes; Training; Visual; Walking; Work; base; biomechanical model; design; exoskeleton; exosuit; experience; fall injury; falls; feature extraction; foot; gait rehabilitation; kinematics; neuromuscular; novel; post stroke; prediction algorithm; prevent; response; sensor; tool; treadmill; walking speed; wearable sensor technology

Abstract For individuals recovering from a stroke, injurious falls often occur due to a stumble, or “intrinsically generated” trip (i.e., the swinging foot contacts the ground), while walking. A major barrier toward developing effective fall prevention strategies is an inability to determine reliably, in advance, that a walking-related trip will occur. Swing limb motion, however, is dictated by late stance kinematics. Therefore, we propose to develop a novel inference system, based on stance phase kinematics, that can accurately and reliably predict, in real-time, that a trip is about to occur. Thus, if the foot is predicted to strike the ground, the algorithm will inform which steps require intervention in the swing limb's trajectory. Current approaches to fall prevention teach reactive responses to a trip or train individuals post-stroke to minimize the impairments associated with falls (e.g., strength, balance, ROM). Although preventive training can reduce intrinsically generated trips for otherwise healthy older adults, deficits in voluntary muscle activation limits the efficacy of such training in individuals post-stroke. Rather than using a conventional reactive approach, we intend to develop the preliminary tools needed to develop a proactive, integrated, feed-forward controller to inform future engineering approaches (e.g., a multi-channel electrical stimulator, exoskeleton/exosuit) to appropriately intervene in the swing limb's trajectory, only when necessary. The work proposed here is a necessary first step to determine that we can successfully predict trips accurately and with sufficient time to intervene appropriately. To accomplish this goal, we will pursue two Specific Aims. In Specific Aim 1, we will use non-environmental distractors to increase the likelihood of participants experiencing an intrinsically generated trip while walking on both a treadmill, as well as overground. We will then use the recorded limb kinematics to select a feature set for development of a novel inference prediction system. This algorithm will be evaluated offline to determine its accuracy and speed in classifying steps as either trips or non-trips. In Aim 2, we will evaluate the developed inference system in a real-time analysis of trip and non-trip steps during walking. Again, the online system will be evaluated for accuracy and speed during walking trials. At the conclusion of this project, we will have a robust method of detecting an upcoming trip for “selective” intervention in swing limb trajectory. This work promises to have a tremendous impact on the field of walking recovery post-stroke and for other populations at risk for trip related falls. In particular, successful completion of this project will establish a paradigm shift from reactive fall prevention to proactive trip and fall prevention.

抽象的 对于中风康复者来说，受伤性跌倒通常是由于绊倒或“内在原因”造成的 行走时的绊倒（即摆动的脚接触地面）。发展有效跌倒的主要障碍 预防策略是无法提前可靠地确定是否会发生与步行相关的旅行。摇摆 然而，肢体运动是由后期姿势运动学决定的。因此，我们建议开发一种新颖的推论 系统基于站立阶段运动学，可以准确可靠地实时预测行程 即将发生。因此，如果预测脚会着地，算法将告知需要采取哪些步骤 干预摆动肢的轨迹。当前预防跌倒的方法教导对跌倒的反应反应 对中风后的患者进行旅行或训练，以尽量减少与跌倒相关的损伤（例如力量、平衡、 只读存储器）。尽管预防性培训可以减少原本健康的老年人的内在出行， 随意肌肉激活的缺陷限制了中风后个体的此类训练的效果。而不是 使用传统的反应方法，我们打算开发开发所需的初步工具 主动、集成、前馈控制器，为未来的工程方法提供信息（例如，多通道 仅当 必要的。这里提出的工作是确定我们可以成功预测出行的必要的第一步 准确并有足够的时间进行适当的干预。为实现这一目标，我们将采取两个具体措施 目标。在具体目标 1 中，我们将使用非环境干扰因素来增加参与者的可能性 在跑步机上和地面上行走时体验内在产生的旅行。我们随后将 使用记录的肢体运动学来选择用于开发新颖的推理预测系统的特征集。 该算法将进行离线评估，以确定其将步骤分类为行程或步骤的准确性和速度。 非行程。在目标 2 中，我们将在旅行和非旅行的实时分析中评估所开发的推理系统 行走时的步数。同样，在线系统将在步行试验期间评估准确性和速度。在 该项目结束后，我们将拥有一种强大的方法来检测“选择性”即将到来的旅行 干预摆动肢体轨迹。这项工作有望对步行领域产生巨大影响 中风后以及其他有旅行相关跌倒风险的人群的康复。特别是顺利完成 该项目将实现从被动预防跌倒到主动预防绊倒和跌倒的范式转变。