SBIR Phase I: A Patient-Centered Wearable System to Enable Data-Driven Decisions in Neuromuscular Disorders

这个小型企业创新研究(SBIR)第一阶段项目的广泛影响/商业潜力将使ALS社区的成员受益，因为这项可穿戴技术和数据收集使ALS患者能够更独立，允许他们与照顾者和临床医生远程沟通，控制他们生活空间内的环境因素，以及能够监控他们的自我护理进展。扩大肌萎缩侧索硬化症患者、照顾者和临床护理团队成员之间的沟通联系，创造了一个动态的、多学科的护理环境，可以彻底满足肌萎缩侧索硬化症患者不断变化的需求。这项拟议的技术可以为肌萎缩侧索硬化症患者带来即时和持续的帮助，并将在由于肌萎缩侧索硬化症进展而发生的身体变化时保持功能，即使患者失去了所有的运动能力。这种技术的直接目标市场是行动和说话能力严重受损的个人。包括多发性硬化症、脑瘫、脊髓损伤和严重中风在内的神经肌肉疾病影响着美国的150万人和全球的3000万人。这些人群在有效沟通和控制方面的选择非常有限，而且在很大程度上没有得到现有技术的服务。拟议的项目旨在为肌萎缩侧索硬化症(ALS)患者创建一个技术平台，以解决护理环境中受肌萎缩侧索硬化症影响的沟通和可访问性问题。肌萎缩侧索硬化症患者与鼠标和键盘等接口技术的交流和互动能力逐渐下降。由于疾病进展速度的不同，每个患者都有动态的需求。这为提供具有数据收集能力的个性化人机界面创造了机会。该提案的目标包括测试来自呼吸机和电动轮椅等医疗设备的皮肤电极阻抗和电磁干扰(EMI)对生物电势信号质量的影响。此外，它还涉及基于个人身体损伤的个性化信号分类模型。先进的信号处理技术，包括Morlet小波和希尔伯特变换，将用于过滤从多模式传感器输入收集的数据。这将确保为多个护理环境进行可靠的系统设计，而不考虑该空间内的生命支持和技术设备。此外，还将与肌萎缩侧索硬化症的参与者进行人体研究，以获得相关的数据集。

The broad impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project will benefit the members of the ALS community as this wearable technology and data collection enables greater independence for individual ALS patients allowing them to remotely communicate with caregivers and clinicians, and control environmental elements within their living spaces as well as being able to monitor their progression for self-care. Expanding the communication connection between and among the ALS patient, caregivers and clinical care team members creates a dynamic, multidisciplinary care environment, which can thoroughly address the changing needs of an ALS patient. The proposed technology could bring immediate and ongoing assistance to ALS patients and will remain functional as physical changes occur due to the progression of ALS, even when a patient has lost all movement ability. The immediate target market for this type of technology is individuals with severely impaired ability to move and speak. Neuromuscular conditions including multiple sclerosis, cerebral palsy, spinal cord injuries and severe stroke affect 1.5M people in the US and 30M people globally. This population has very limited options for effective communication and control and are largely underserved by existing technologies.The proposed project aims to create a technological platform to address communication and accessibility issues within care environments for patients affected by Amyotrophic Lateral Sclerosis (ALS). Patients with ALS develop a progressively reduced ability to communicate and interact with interface technology such as mice and keyboards. Each patient has dynamic needs due to the differing progression rates of the disease. This creates an opportunity to provide a personalized human-computer interface with data collection capabilities. The objectives of the proposal involve testing the effects of skin-electrode impedance and electromagnetic interference (EMI) from medical equipment such as ventilators and powered wheelchairs on the quality of biopotential signals. Furthermore, it involves personalizing signal classification models based on an individual's physical impairment. Advanced signal processing techniques, including Morlet wavelet and Hilbert transform, will be used to filter the data collected from multimodal sensor inputs. This will ensure a robust system design for multiple care environments, regardless of life support and technological equipment within that space. In addition, a human research study will be conducted with ALS participants in order to acquire a relevant data set.