Biomedical Signals and Systems, MIRA - Institute for Biomedical Technology and Technical Medicine, University of Twente , Enschede , Netherlands.
Biomedical Signals and Systems, MIRA - Institute for Biomedical Technology and Technical Medicine, University of Twente, Enschede, Netherlands; Centre for Telematics and Information Technology, University of Twente, Enschede, Netherlands.
Front Bioeng Biotechnol. 2016 Jan 13;3:210. doi: 10.3389/fbioe.2015.00210. eCollection 2015.
Stroke survivors are commonly left with disabilities that impair activities of daily living. The main objective of their rehabilitation program is to maximize the functional performance at home. However, the actual performance of patients in their home environment is unknown. Therefore, objective evaluation of daily life activities of stroke survivors in their physical interaction with the environment is essential for optimal guidance of rehabilitation therapy. Monitoring daily life movements could be very challenging, as it may result in large amounts of data, without any context. Therefore, suitable metrics are necessary to quantify relevant aspects of movement performance during daily life. The objective of this study is to develop data processing methods, which can be used to process movement data into relevant metrics for the evaluation of intra-patient differences in quality of movements in a daily life setting.
Based on an iterative requirement process, functional and technical requirements were formulated. These were prioritized resulting in a coherent set of metrics. An activity monitor was developed to give context to captured movement data at home. Finally, the metrics will be demonstrated in two stroke participants during and after their rehabilitation phases.
By using the final set of metrics, quality of movement can be evaluated in a daily life setting. As example to demonstrate potential of presented methods, data of two stroke patients were successfully analyzed. Differences between in-clinic measurements and measurements during daily life are observed by applying the presented metrics and visualization methods. Heel height profiles show intra-patient differences in height, distance, stride profile, and variability between strides during a 10-m walk test in the clinic and walking at home. Differences in distance and stride profile between both feet were larger at home, than in clinic. For the upper extremities, the participant was able to reach further away from the pelvis and cover a larger area.
Presented methods can be used for the objective evaluation of intra-patient differences in movement quality between in-clinic and daily life measurements. Any observed progression or deterioration of movement quality could be used to decide on continuing, stopping, or adjusting rehabilitation programs.
中风幸存者通常会留下影响日常生活活动的残疾。他们康复计划的主要目标是最大限度地提高在家中的功能表现。然而,患者在家庭环境中的实际表现是未知的。因此,客观评估中风幸存者在与环境的物理交互中的日常生活活动对于康复治疗的最佳指导至关重要。监测日常生活中的运动可能非常具有挑战性,因为它可能会产生大量没有任何上下文的数据。因此,需要合适的指标来量化日常生活中运动表现的相关方面。本研究的目的是开发数据处理方法,可用于将运动数据处理成相关指标,以评估日常生活中患者之间运动质量的差异。
基于迭代需求过程,制定了功能和技术要求。这些要求进行了优先级排序,从而形成了一套连贯的指标。在家中,开发了一种活动监视器来为捕获的运动数据提供上下文。最后,将在两名中风参与者的康复阶段期间和之后展示这些指标。
通过使用最终的指标集,可以在日常生活环境中评估运动质量。作为演示方法潜力的示例,成功分析了两名中风患者的数据。通过应用所提出的指标和可视化方法,在诊所进行的 10 米步行测试和在家中行走时观察到患者之间运动质量的差异。双脚之间的距离和步幅特征差异在家庭中比在诊所中更大。对于上肢,参与者能够从骨盆更远的地方伸出,并覆盖更大的区域。
所提出的方法可用于客观评估诊所和日常生活测量之间患者之间运动质量的差异。任何观察到的运动质量的进展或恶化都可以用来决定继续、停止或调整康复计划。