Department of Physical Therapy, Rehabilitation Sciences and Athletic Training, School of Health Professions, University of Kansas Medical Center, Kansas City, KS, United States.
Department of Neurology, Parkinson's Disease and Movement Disorder Center, University of Kansas Medical Center, Kansas City, KS, United States.
Gait Posture. 2024 Sep;113:139-144. doi: 10.1016/j.gaitpost.2024.06.001. Epub 2024 Jun 4.
Gait impairment is an early marker of Parkinson's disease (PD) and is frequently monitored to evaluate disease progression. Wearable sensors are increasingly being used to quantify gait in the real-world setting among people with PD (pwPD). Particularly, embedding wearables on devices or clothing that are worn daily may represent a useful strategy to improve compliance and regular monitoring of gait.
The current investigation examined the validity of innovative smart glasses to measure gait among pwPD.
Participants wore the smart glasses and 6 APDM gait sensors simultaneously, while performing two walking tasks: the 3-meters Timed Up and Go test (TUG) and the 7-meters Stand and Walk (SAW) test. The following spatiotemporal gait parameters were calculated from the data collected using the two different devices: step time, step length, swing percentage, TUG duration, turn duration, and turn velocity.
A total of 31 pwPD (mean age=68.6±8.5 years; 35.48 % female(N=11), mean Unified Parkinson's Disease Rating Scale (UPDRS) total score=32.1±14.7) participated in the study. Smart glasses achieved high validity in measuring step time (ICC=0.92, p=0.01) and TUG duration (ICC=0.96, p=0.03) compared to APDM sensors. On the other hand, the smart glasses did not achieve adequate validity when measuring step length, swing percentage, turn duration or turn velocity.
The current study suggests that smart glasses has the potential to measure TUG and step time in individuals living with PD. However, further research is needed to improve algorithms for sensors worn on the head.
步态障碍是帕金森病（PD）的早期标志物，常被用于评估疾病进展。可穿戴传感器越来越多地被用于在 PD 患者（pwPD）的真实环境中量化步态。特别是，将可穿戴设备嵌入到日常佩戴的设备或衣物中，可能是提高依从性和定期监测步态的有用策略。
本研究旨在检验创新智能眼镜在测量 pwPD 步态方面的有效性。
参与者同时佩戴智能眼镜和 6 个 APDM 步态传感器，进行两项行走任务：3 米计时起立行走测试（TUG）和 7 米站立和行走测试（SAW）。从两种不同设备收集的数据中计算了以下时空步态参数：步时、步长、摆动百分比、TUG 持续时间、转弯持续时间和转弯速度。
共有 31 名 pwPD（平均年龄 68.6±8.5 岁；35.48%为女性（N=11），平均帕金森病统一评定量表（UPDRS）总分 32.1±14.7）参与了研究。与 APDM 传感器相比，智能眼镜在测量步时（ICC=0.92，p=0.01）和 TUG 持续时间（ICC=0.96，p=0.03）方面具有较高的有效性。另一方面，智能眼镜在测量步长、摆动百分比、转弯持续时间或转弯速度方面的有效性不足。
本研究表明，智能眼镜具有测量 PD 患者 TUG 和步时的潜力。然而，需要进一步研究来改进戴在头部的传感器的算法。
