Godfrey A, Del Din S, Barry G, Mathers J C, Rochester L
Institute of Neuroscience, Newcastle University, Campus for Ageing & Vitality, Newcastle upon Tyne, UK; Clinical Ageing Research Unit, Newcastle University, Campus for Ageing & Vitality, Newcastle upon Tyne, UK.
Institute of Neuroscience, Newcastle University, Campus for Ageing & Vitality, Newcastle upon Tyne, UK; Clinical Ageing Research Unit, Newcastle University, Campus for Ageing & Vitality, Newcastle upon Tyne, UK.
Med Eng Phys. 2015 Apr;37(4):400-7. doi: 10.1016/j.medengphy.2015.02.003. Epub 2015 Mar 4.
Gait is an important clinical assessment tool since changes in gait may reflect changes in general health. Measurement of gait is a complex process which has been restricted to the laboratory until relatively recently. The application of an inexpensive body worn sensor with appropriate gait algorithms (BWM) is an attractive alternative and offers the potential to assess gait in any setting. In this study we investigated the use of a low-cost BWM, compared to laboratory reference using a robust testing protocol in both younger and older adults. We observed that the BWM is a valid tool for estimating total step count and mean spatio-temporal gait characteristics however agreement for variability and asymmetry results was poor. We conducted a detailed investigation to explain the poor agreement between systems and determined it was due to inherent differences between the systems rather than inability of the sensor to measure the gait characteristics. The results highlight caution in the choice of reference system for validation studies. The BWM used in this study has the potential to gather longitudinal (real-world) spatio-temporal gait data that could be readily used in large lifestyle-based intervention studies, but further refinement of the algorithm(s) is required.
步态是一种重要的临床评估工具，因为步态变化可能反映整体健康状况的变化。步态测量是一个复杂的过程，直到最近一直局限于实验室环境。应用带有适当步态算法的低成本可穿戴式传感器（BWM）是一种有吸引力的替代方法，它有潜力在任何环境中评估步态。在本研究中，我们在年轻人和老年人中使用稳健的测试方案，将低成本BWM的使用与实验室参考方法进行了比较。我们观察到，BWM是估计总步数和平均时空步态特征的有效工具，然而在变异性和不对称性结果方面的一致性较差。我们进行了详细调查以解释系统之间一致性差的原因，并确定这是由于系统之间的固有差异，而非传感器无法测量步态特征。研究结果凸显了在验证研究中选择参考系统时需谨慎。本研究中使用的BWM有潜力收集纵向（现实世界）时空步态数据，这些数据可轻松用于大型基于生活方式的干预研究，但需要进一步完善算法。
