SocialBit: Establishing the accuracy of a wearable sensor to detect social interactions after stroke

中风幸存者容易受到社交互动减少的影响。互动减少与更糟糕的情况有关 中风后的身体恢复。加强中风后的社会互动可能是最重要的 改善中风康复的有力策略。社会互动被定义为同步的 通常是口头上的相互作用，通常是在同一物理环境中同时存在的个体之间的相互作用 地点。目前检测社交互动的方法依赖于自我报告，而自我报告不能可靠地执行 有语言或认知缺陷的患者。有这种缺陷的患者最容易受到社会 与世隔绝。该项目介绍了一种新的可穿戴社交传感器SocialBit，它可以检测音频 真实世界环境中的社交互动签名。我们的初步数据显示，Social Bit可以 准确检测社交互动(~95%)，它可以通过处理选定的音频功能来实现这一点，而不需要 存储原始音频数据。因此，该技术检测和测量社交网络的持续时间 交互，同时在交互期间保护内容的隐私。基于这些发现，我们 已经制定了一项研究计划，以确定Social Bit在中风幸存者中的有用性 中风后的紧随时期。中风后时期适合进行这样的研究，因为1)患者 在这一时期易受社会剥夺的影响，以及2)住院环境的有限性质 提供了一个理想的环境，可以对照直接观察到的社交网络的基本事实来测试社交网络 互动。我们的中心假设是，Social Bit可以准确地检测中风患者的社交互动 住院环境中的幸存者。该项目主要是为了确定SocialBit到 检测有不同缺陷的患者的社交互动，对照视频辅助、实时 卒中后时间观察。首先，我们将检查SocialBit检测 200例患者不能直接观察的社会交往时间(目标1)。第二，我们将确定 社会交往时间与社会隔离和3个月后中风预后的关系(目标2)。最后， 我们将确定与社会交往时间相关的医学因素(目标3)。这项研究将 建立中风康复研究中量化社会互动的关键标准。该项目将(A) 确定自动和不显眼的方法来衡量社交互动，(B)确定关键设计和 未来干预试验的结果标准，以及(C)增加我们对潜在风险的理解 中风后社会变化的机制。在这样做的时候，这项研究将解决公共卫生的优先事项 为中风患者建立更好的行为矫正策略。

Stroke survivors are vulnerable to reduced social interactions. Reduced interactions are related to worse physical recovery after stroke. Enhancing social interactions after stroke may be one of the most powerful strategies to improve stroke recovery. Social interactions are defined as the synchronous interactions, commonly verbal, between individuals who are usually co-present in the same physical location. Current ways to detect social interactions rely on self-report, which cannot be performed reliably by patients with language or cognitive deficits. Patients with such deficits are most vulnerable to social isolation. This project introduces a new wearable social sensor, SocialBit, that can detect audio signatures of social interactions in real-world settings. Our preliminary data show that SocialBit can detect social interactions accurately (~95%), and it can do so by processing select audio features without storing raw audio data. Therefore, the technology detects and measures the duration of the social interaction while preserving the privacy of the content during the interaction. Based on these findings, we have developed a research plan to establish the usefulness of SocialBit in stroke survivors in the immediate post-stroke period. The post-stroke period is apt for such a study because 1) patients are vulnerable to social deprivation in this time period, and 2) the bounded nature of an inpatient setting provides an ideal environment to test SocialBit against a ground truth of directly observed social interactions. Our central hypothesis is that SocialBit can accurately detect social interactions in stroke survivors in inpatient settings. This project is primarily designed to establish the accuracy of SocialBit to detect social interaction in patients with varying deficits against the ground truth of video-assisted, real- time observation in the post-stroke period. First, we will examine the accuracy of SocialBit to detect the social interaction time against direct observation in 200 patients (Aim 1). Second, we will determine the association of social interaction time to social isolation and stroke outcomes at 3 months (Aim 2). Finally, we will determine the medical factors associated with social interaction time (Aim 3). This study will establish the key criteria of quantifying social interaction in stroke recovery research. The project will (a) identify automatic and unobtrusive methods to measure social interaction, (b) determine key design and outcome criteria for a future intervention trial, and (c) increase our understanding of underlying mechanisms in social changes after stroke. In so doing, this study will address the public health priority of building better behavioral modification strategies for patients with stroke.