McGinnis Ryan S, Mahadevan Nikhil, Moon Yaejin, Seagers Kirsten, Sheth Nirav, Wright John A, DiCristofaro Steven, Silva Ikaro, Jortberg Elise, Ceruolo Melissa, Pindado Jesus A, Sosnoff Jacob, Ghaffari Roozbeh, Patel Shyamal
MC10, Inc., Lexington, Massachusetts, United States of America.
Department of Biomedical Engineering, University of Vermont, Burlington, Vermont, United States of America.
PLoS One. 2017 Jun 1;12(6):e0178366. doi: 10.1371/journal.pone.0178366. eCollection 2017.
Gait speed is a powerful clinical marker for mobility impairment in patients suffering from neurological disorders. However, assessment of gait speed in coordination with delivery of comprehensive care is usually constrained to clinical environments and is often limited due to mounting demands on the availability of trained clinical staff. These limitations in assessment design could give rise to poor ecological validity and limited ability to tailor interventions to individual patients. Recent advances in wearable sensor technologies have fostered the development of new methods for monitoring parameters that characterize mobility impairment, such as gait speed, outside the clinic, and therefore address many of the limitations associated with clinical assessments. However, these methods are often validated using normal gait patterns; and extending their utility to subjects with gait impairments continues to be a challenge. In this paper, we present a machine learning method for estimating gait speed using a configurable array of skin-mounted, conformal accelerometers. We establish the accuracy of this technique on treadmill walking data from subjects with normal gait patterns and subjects with multiple sclerosis-induced gait impairments. For subjects with normal gait, the best performing model systematically overestimates speed by only 0.01 m/s, detects changes in speed to within less than 1%, and achieves a root-mean-square-error of 0.12 m/s. Extending these models trained on normal gait to subjects with gait impairments yields only minor changes in model performance. For example, for subjects with gait impairments, the best performing model systematically overestimates speed by 0.01 m/s, quantifies changes in speed to within 1%, and achieves a root-mean-square-error of 0.14 m/s. Additional analyses demonstrate that there is no correlation between gait speed estimation error and impairment severity, and that the estimated speeds maintain the clinical significance of ground truth speed in this population. These results support the use of wearable accelerometer arrays for estimating walking speed in normal subjects and their extension to MS patient cohorts with gait impairment.
步态速度是患有神经系统疾病患者行动能力受损的一个有力临床指标。然而，将步态速度评估与提供全面护理相结合的工作通常局限于临床环境，并且由于对训练有素的临床工作人员的可用性要求不断增加，往往受到限制。评估设计中的这些局限性可能导致生态效度不佳，以及针对个体患者量身定制干预措施的能力有限。可穿戴传感器技术的最新进展推动了新方法的开发，这些方法用于在诊所外监测表征行动能力受损的参数，如步态速度，从而解决了许多与临床评估相关的局限性。然而，这些方法通常使用正常步态模式进行验证；将其应用扩展到步态受损的受试者仍然是一个挑战。在本文中，我们提出了一种机器学习方法，用于使用可配置的皮肤贴合式共形加速度计阵列来估计步态速度。我们在具有正常步态模式的受试者以及患有多发性硬化症引起的步态障碍的受试者的跑步机行走数据上确定了该技术的准确性。对于步态正常的受试者，性能最佳的模型系统性地仅高估速度0.01米/秒，检测速度变化的误差小于1%，均方根误差为0.12米/秒。将这些基于正常步态训练的模型扩展到步态受损的受试者时，模型性能仅有微小变化。例如，对于步态受损的受试者，性能最佳的模型系统性地高估速度0.01米/秒，量化速度变化的误差在1%以内，均方根误差为0.14米/秒。进一步分析表明，步态速度估计误差与损伤严重程度之间没有相关性，并且估计速度在该人群中保持了地面真值速度的临床意义。这些结果支持使用可穿戴加速度计阵列来估计正常受试者的步行速度，并将其扩展到患有步态障碍的多发性硬化症患者队列。
