MyoSense: Automated Muscle Hypertonicity Classification System

描述（由申请人提供）：目的是设计、开发和临床验证MyoSense，一种临床医生佩戴的高分辨率感觉增强假体，用于定量表征和区分不同类型的肌肉张力过高。发展将集中在日益增长的临床需要，以区分肌张力障碍痉挛的儿童影响脑性瘫痪（CP）和其他混合或继发性肌张力障碍。患有CP的儿童通常患有混合性运动障碍，包括痉挛、肌无力、共济失调、手足徐动症和肌张力障碍，导致严重的功能障碍和日常生活活动受限。此外，这些共存的运动表现可以发生在基于脑损伤拓扑的身体的不同部位。痉挛和肌张力障碍目前在临床上都是使用主观的、有序的和非间隔的评定量表来测量的，从而限制了统计技术在任何分析中的应用。目前，虽然最近的研究表明生物力学特征可以区分不同类型的高渗性，但定量测量并没有得到广泛应用。不同的药理学和手术干预存在不同的神经系统的结果，运动体征，并观察到的CP儿童的运动;因此，定量评估可以更好地指导临床判断的治疗。为错误诊断选择侵入性治疗的后果可能具有重大的长期后果。此外，一个通用的，定量的评估系统，检查肌肉张力应该有重要的应用在其他几个运动障碍，包括帕金森氏病，中风，和一般康复。MyoSense系统将提供一种紧凑的用户佩戴式假肢，配有动力和力传感器，并集成实时软件反馈，以指导标准化和定量运动检查。具体创新点在于四个方面。首先，代替对患者进行仪器化，感觉增强假体由临床医生佩戴，使得该装置高度适用于在各种患者和状况下测量来自广泛关节组的张力亢进。其次，无线手套中的多模态传感器的集成测量实时关节位置和速度，同时测量独立于重力移动身体部位所需的力，以帮助区分痉挛和肌张力障碍。第三，实时软件显示反馈将指导临床医生运动评估以标准化和量化特征。最后，智能算法将处理数据并提取特征来分类痉挛和肌张力障碍。第一阶段将利用现有的运动传感硬件平台 结合附加的传感器模态以证明捕获和量化痉挛和肌张力障碍的特征的可行性。将修改硬件，通过优化传感器类型和数量并将系统嵌入临床医生佩戴的手套中，最大限度地减少患者和临床医生的负担。将开发一个软件界面，以收集临床期间的数据 研究并为评估提供实时反馈。最后，对原型系统进行临床可行性研究。 公共卫生相关性：我们将设计、开发和临床验证“MyoSense”，这是一种临床医生佩戴的高分辨率感觉增强假肢，可定量表征和区分不同类型的肌肉张力过高，特别是区分脑瘫（CP）和其他混合性或继发性肌张力障碍儿童的肌张力障碍和痉挛状态。痉挛和肌张力障碍的治疗方法不同，目前临床上都是使用主观、顺序和非间隔评定量表进行测量，因此选择不正确的治疗方法可能对CP儿童产生显著、持久的影响。MyoSense系统将提供一种紧凑的、临床医生佩戴的手套，配有动力和力传感器，并集成实时软件反馈，以指导标准化和定量的运动检查，从而使临床医生能够适当地开出有助于改善患者生活质量的治疗处方。

DESCRIPTION (provided by applicant): The objective is to design, develop, and clinically validate MyoSense", a clinician worn, high-resolution sensory enhancing prosthetic to quantitatively characterize and distinguish different types of muscle hypertonicity. Development will focus on the growing clinical need to differentiate dystonia from spasticity in children affected by cerebral palsy (CP) and other mixed or secondary dystonias. Children with CP often suffer from mixed motor disorders including spasticity, muscle weakness, ataxia, athetosis and dystonia causing severe functional impairment and limiting activities of daily living. Additionally these coexisting motor manifestations can occur in different parts of the body based on brain injury topology. Spasticity and dystonia are both currently measured clinically using subjective, ordinal and non-interval rating scales, thereby limiting applications of statistical techniques in any analysis. Currently, quantitative measures are not widely used although recent studies suggest biomechanical features can distinguish different types of hypertonicity. Distinct pharmacologic and surgical interventions exist for different neurological findings, motor signs, and movements observed for children with CP; therefore, quantitative assessment could better guide clinical judgments for treatments. Consequences for selecting invasive treatments for the incorrect diagnosis can have significant, long term consequences. Additionally, a general, quantitative assessment system for examining muscle tone should have important applications in several other movement disorders including Parkinson's disease, stroke, and general rehabilitation. The MyoSense system will provide a compact, user worn prosthetic instrumented with kinetic and force sensors and integrate real-time software feedback to guide a standardized and quantitative motor examination. The specific innovation lies in four areas. First, instead of instrumenting a patient, the sensory enhancing prosthetic is worn by the clinician making the device is highly adaptable to measure hypertonia from a wide range of joint sets in a variety of patient and conditions. Second, the integration of multi-modal sensors in a wireless glove measures real-time joint position and velocity while simultaneously measuring forces required to move the body part independent of gravity to help distinguish spasticity from dystonia. Third, real-time software display feedback will guide the clinician motion assessment to standardize and quantify features. Finally, intelligent algorithms will process the data and extract features t classify spasticity and dystonia. Phase I will utilize an existing motion sensing hardware platform with the integration of additional sensor modalities to demonstrate feasibility of capturing and quantifying features of spasticity and dystonia. The hardware will be modified to minimize patient and clinician burden by optimizing sensor type and count and embedding the system into a clinician worn glove. A software interface will be developed to collect data during clinical studies and provide real-time feedback for an evaluation. Finally, the prototype system will be evaluated in clinical feasibility study. PUBLIC HEALTH RELEVANCE: We will design, develop, and clinically validate MyoSense", a clinician worn, high-resolution sensory enhancing prosthetic to quantitatively characterize and distinguish different types of muscle hypertonicity, specifically differentiating dystonia from spasticity in children affected by cerebral palsy (CP) and other mixed or secondary dystonias. Spasticity and dystonia are treated differently and are both currently measured clinically using subjective, ordinal and non-interval rating scales and as a result selecting the incorrect treatment can have significant, long lasting implications for children with CP. The MyoSense system will provide a compact, clinician worn glove instrumented with kinetic and force sensors and integrate real-time software feedback to guide a standardized and quantitative motor examination so that clinicians can appropriately prescribe treatments that will help improve patient quality of life.