背景:步态冻结(FOG)是帕金森病(PD)的一种偶发性和高度致残症状。传统上,FOG评估依赖于耗时的相机镜头视觉检查。因此,以前的研究已经提出了便携式和自动化的解决方案来注释FOG。然而,由于药物作用和不同的引起FOG的任务引起的步态变异性,自动FOG评估具有挑战性。此外,自动化方法是否可以将FOG与典型的日常运动区分开来,比如自愿停止,还有待确定。为了解决这些问题,我们评估了基于惯性测量单元(IMU)的深度学习(DL)自动FOG评估模型。我们评估了其在所有标准化的FOG激发任务和药物状态下的表现,以及特定的任务和药物状态。此外,我们研究了添加停止期对FOG检测性能的影响。
方法:12名自我报告FOG的PD患者(平均年龄69.33±6.02岁)完成了FOG激发方案,包括开启/关闭多巴胺能药物状态下的定时启动和360度转弯任务,有/无自愿停止。IMU连接到骨盆以及胫骨和距骨的两侧。时间卷积网络(TCN)用于检测FOG发作。通过冷冻时间百分比(%TF)和冷冻发作次数(#FOG)量化FOG严重程度。通过类内相关系数(ICC)评估模型生成结果与黄金标准专家视频注释之间的一致性。
结果:对于不停止试验的FOG评估,我们模型的一致性很强(ICC(%TF)=0.92[0.68,0.98];ICC(#FOG)=0.95[0.72,0.99])。在特定的引起FOG的任务上训练的模型无法推广到看不见的任务,而在特定药物状态下训练的模型可以推广到看不见的状态。为了在停止试验中进行评估,我们模型的一致性中等强度(ICC(%TF)=0.95[0.73,0.99];ICC(#FOG)=0.79[0.46,0.94]),但只有在训练数据中包含停止时。
结论:在IMU信号上训练的TCN允许在有/没有包含不同药物状态和引起FOG的任务的停止的试验中进行有效的FOG评估。这些结果令人鼓舞,并使未来的工作能够在日常生活中调查自动FOG评估。
Freezing of gait (FOG) is an episodic and highly disabling symptom of Parkinson\'s Disease (PD). Traditionally, FOG assessment relies on time-consuming visual inspection of camera footage. Therefore, previous studies have proposed portable and automated solutions to annotate FOG. However, automated FOG assessment is challenging due to gait variability caused by medication effects and varying FOG-provoking tasks. Moreover, whether automated approaches can differentiate FOG from typical everyday movements, such as volitional stops, remains to be determined. To address these questions, we evaluated an automated FOG assessment model with deep learning (DL) based on inertial measurement units (IMUs). We assessed its performance trained on all standardized FOG-provoking tasks and medication states, as well as on specific tasks and medication states. Furthermore, we examined the effect of adding stopping periods on FOG detection performance.
Twelve PD patients with self-reported FOG (mean age 69.33 ± 6.02 years) completed a FOG-provoking protocol, including timed-up-and-go and 360-degree turning-in-place tasks in On/Off dopaminergic medication states with/without volitional stopping. IMUs were attached to the pelvis and both sides of the tibia and talus. A temporal convolutional network (TCN) was used to detect FOG episodes. FOG severity was quantified by the percentage of time frozen (%TF) and the number of freezing episodes (#FOG). The agreement between the model-generated outcomes and the gold standard experts\' video annotation was assessed by the intra-class correlation coefficient (ICC).
For FOG assessment in trials without stopping, the agreement of our model was strong (ICC (%TF) = 0.92 [0.68, 0.98]; ICC(#FOG) = 0.95 [0.72, 0.99]). Models trained on a specific FOG-provoking task could not generalize to unseen tasks, while models trained on a specific medication state could generalize to unseen states. For assessment in trials with stopping, the agreement of our model was moderately strong (ICC (%TF) = 0.95 [0.73, 0.99]; ICC (#FOG) = 0.79 [0.46, 0.94]), but only when stopping was included in the training data.
A TCN trained on IMU signals allows valid FOG assessment in trials with/without stops containing different medication states and FOG-provoking tasks. These results are encouraging and enable future work investigating automated FOG assessment during everyday life.