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A framework to enhance assistive technology based mobility tracking in individuals with spinal cord injury

机译：一个增强基于辅助技术的脊髓损伤患者活动追踪的框架

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Assistive technologies such as wheelchairs, canes, and walkers have significantly improved the mobility, function, and quality of life for individuals with spinal cord injury (SCI). In this article, we propose a framework which combines machine learning algorithms with wearable sensors to capture and track mobility in individuals with SCI. Pilot testing in two individuals without SCI indicated that four to seven features obtained from sensors worn on the body or placed on the assistive technology could successfully detect mobility and mobility modes. The classification accuracy for Naïve Bayes and Decision Tree algorithms to detect mobility from non-mobility activity varied from 87.4% to 97.6%. The classification accuracy for detecting six mobility modes within mobility ranged from 88.5% to 90.6%. The proposed framework has the potential to assist researchers and clinicians to study complex mobility patterns of individuals with SCI and provide adaptive rehabilitation and physical activity interventions in the community.
机译：轮椅，手杖和助行器等辅助技术已大大改善了脊髓损伤（SCI）患者的活动能力，功能和生活质量。在本文中，我们提出了一个将机器学习算法与可穿戴式传感器相结合的框架，以捕获和跟踪具有SCI的个体的移动性。在没有SCI的两个人中进行的试点测试表明，从佩戴在身体上或置于辅助技术上的传感器获得的四到七个特征可以成功检测出移动性和移动性模式。朴素贝叶斯和决策树算法从非移动性活动中检测移动性的分类精度从87.4％到97.6％不等。用于检测移动性内的六个移动性模式的分类精度范围为88.5 \％至90.6 \％。拟议的框架有可能协助研究人员和临床医生研究SCI患者的复杂活动模式，并在社区中提供适应性康复和体育锻炼干预措施。

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