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 对于从中风中恢复过来的人，经常是由于跌倒或“本质上产生的”而发生的伤害跌倒 行走时旅行（即摇摆的脚接触地面）。发展有效秋季的主要障碍 预防策略无法事先确定将发生与步行有关的旅行。摇摆 然而，肢体运动是由晚期运动运动学决定的。因此，我们建议开发一种新颖的推论 基于立场阶段运动学的系统，可以实时准确，可靠地预测旅行是 即将发生。如果预计脚会撞到地面，则该算法将告知哪些步骤需要 挥杆肢体轨迹中的干预。当前预防预防教学的方法反应反应 罢工或训练个体后，以最大程度地减少与跌倒相关的障碍（例如，力量，平衡， ROM）。尽管预防培训可以减少其他健康老年人的本质上产生的旅行，但 自愿性肌肉激活的缺陷限制了势后这种训练的效率。而不是 使用常规的反应方法，我们打算开发开发一个必要的初步工具 积极的，集成的，前馈控制者，以告知未来的工程方法（例如，多渠道 电刺激器，外骨骼/外质），以适当干预秋千肢的轨迹 必要的。这里提出的工作是确定我们可以成功预测旅行的必要第一步 准确且有足够的时间进行适当的干预。为了实现这一目标，我们将追求两个特定的 目标。在特定目标1中，我们将使用非环境干扰因素来增加参与者的可能性 在跑步机和地面上行走时，经历了本质上产生的旅行。然后我们会 使用记录的肢体运动学选择用于开发新推理预测系统的功能集。 该算法将离线评估，以确定其在将步骤分类为旅行或 非旅行。在AIM 2中，我们将在TRIP和非旅行的实时分析中评估开发的推理系统 步行过程中的步骤。同样，将在步行试验期间评估在线系统的准确性和速度。 该项目的结论是，我们将采用一种强大的方法来检测即将到来的“选择性”旅行 挥杆四肢轨迹的干预。这项工作有望对步行领域产生巨大影响 中风后和其他有与旅行相关的跌倒风险的人群。特别是成功完成 该项目将建立从预防反应性秋天转变为主动旅行和预防范围的范式转变。