目的 基于佩戴于3个关键部位的惯性传感器分析早期帕金森病(PD)患者坐站转换动作与弯道行走任务的运动参数,构建早期PD患者运动功能障碍的客观评估方法。方法 2025-05至2025-12在解放军总医院第一医学中心神经内科门诊就诊招募的31例受试者。采用穿戴在腰部及双侧足背的三个惯性传感器采集受试者3 m计时起立-行走测试的运动参数,对比分析PD组与对照组在坐到站体位转换及转弯行走的运动参数差异,并分析上述运动参数与对应临床评估量表评分的相关性。结果 最终纳入有效受试者30例,PD组11例,对照组19例。坐站转换及弯道行走任务中,PD组的重心左右摆幅、重心平均速度、加速度平滑度及步速均显著低于对照组,组间差异均具有统计学意义(P<0.01)。PD组坐到站的重心投影包络面积、重心平均速度与10 m步行测试步数呈显著负相关(r=-0.628、r=-0.803),且与高级平衡量表(FAB)评分呈显著正相关(r=0.642,r=0.725);转弯步数与10 m步行测试步数呈显著正相关(r=0.668),与FAB评分呈显著负相关(r=-0.783)。结论 基于惯性传感器的重心平均速度、加速度平滑度及步速可敏感捕捉早期PD运动功能障碍的细微变化,为早期筛查和临床评估提供客观的数字化工具。
Abstract
Objective To analyze the kinematic parameters of sit-to-stand transition and turning phase tasks in early Parkinson's disease (PD) patients using inertial sensors worn at three key anatomical sites, and to establish an objective assessment method for motor dysfunction in early PD patients. Methods A total of 31 subjects were enrolled from the neurology outpatient department of the First Medical Center of PLA General Hospital from May 2025 to December 2025. Three inertial sensors were attached to the lumbar region and the dorsal sides of both feet to collect kinematic parameters during the 3-meter timed standing-walking test. The kinematic parameters of sit-to-stand phase and turning phase were compared between the two groups, and the correlations between these parameters and corresponding clinical scale scores were analyzed. Results A total of 30 effective subjects were finally included, with 11 in the PD group and 19 in the control group. During sit-to-stand phase and turning phase tasks, the PD group demonstrated significantly lower center of Mass (COM) lateral sway amplitude, mean COM velocity, acceleration smoothness, and gait speed compared with the healthy control group, and the differences between the groups were statistically significant (all P<0.01). In the PD group, the COM projection envelope area and mean COM velocity during sit-to-stand were significantly negatively correlated with the number of steps in the 10-Meter Walk Test (r=-0.628, r=-0.803), and significantly positively correlated with the Fullerton Advanced Balance Scale (FAB) score (r=0.642, r=0.725). The turn step count was significantly positively correlated with the number of steps in the 10-Meter Walk Test (r=0.668), and significantly negatively correlated with the FAB score (r=-0.783). Conclusions The mean COM velocity, acceleration smoothness, and gait speed based on inertial sensors can sensitively capture the subtle changes in motor dysfunction in early PD, providing an objective digital tool for early screening and clinical assessment.
关键词
帕金森病 /
惯性传感器 /
3 m计时起立-行走 /
体位转换 /
弯道行走 /
运动参数 /
临床量表
Key words
Parkinson's disease /
inertial sensor /
3-meter timed standing-walking /
postural transition /
turning walking /
kinematic parameters /
clinical scale
{{custom_sec.title}}
{{custom_sec.title}}
{{custom_sec.content}}
参考文献
[1] 中华医学会神经病学分会帕金森病及运动障碍学组, 中国医师协会神经内科医师分会帕金森病及运动障碍学组. 中国帕金森病步态障碍管理专家共识[J]. 中华神经科杂志,2025,58(3):228-243.
[2] Jankovic J. Parkinson's disease: clinical features and diagnosis[J]. J Neurol Neurosurg Psychiatry, 2008,79(4):368-376.
[3] Li J, Jin M, Wang L, et al. MDS clinical diagnostic criteria for Parkinson's disease in China[J]. J Neurol, 2017,264(3):476-481.
[4] Goetz C G, Tilley B C, Shaftman S R, et al. Movement Disorder Society-Sponsored Revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS): scale presentation and clinimetric testing results[J]. Mov Disord, 2008,23(15):2129-2170.
[5] Johansson H, Löfgren N, Porciuncula F, et al. Impact of dual-tasking and balance confidence on turns and transitions: a cross-sectional study in Parkinson's disease[J]. Sci Rep, 2026, 16(1):1953.
[6] Bouça-Machado R, Duarte G S, Patriarca M, et al. Measurement instruments to assess functional mobility in Parkinson's disease: a systematic review[J]. Mov Disord Clin Pract, 2020,7(2):129-139.
[7] Morris S, Morris M E, Iansek R. Reliability of measurements obtained with the timed “up & go” test in people with Parkinson disease[J]. Phys Ther, 2001,81(2):810-818.
[8] Salarian A, Horak F B, Zampieri C, et al. iTUG, a sensitive and reliable measure of mobility[J]. IEEE Trans Neural Syst Rehabil Eng, 2010,18(3):303-310.
[9] Podsiadlo D, Richardson S. The timed “up & go”: a test of basic functional mobility for frail elderly persons[J]. Am Geriatr Soc, 1991,39(2):142-148.
[10] Youssef H, Sabıroglu M, Mahmoud Abdel-Rahman S, et al. Can wearable sensor based measures of gait accurately reflect Parkinson's disease severity? A systematic review and meta-analysis[J]. Parkinsonism Relat Disord, 2026,142:108108.
[11] 马 銮, 李波陈, 何娟娟, 等. 基于惯性测量单元的帕金森病患者冻结步态检测系统[J]. 中国医疗器械杂志,2019,43(4):238-242.
[12] Geroin C, Artusi C A, Nonnekes J, et al. Axial postural abnormalities in Parkinsonism: gaps in predictors, pathophysiology, and management[J]. Mov Disord, 2023,38(5):732-739.
[13] Greene B R, Premoli I, Mcmanus K, et al. Predicting fall counts using wearable sensors: a novel digital biomarker for Parkinson's disease[J]. Sensors (Basel), 2021,22(1):54
[14] Zhang W, Ling Y, Chen Z, et al. Wearable sensor-based quantitative gait analysis in Parkinson's disease patients with different motor subtypes[J]. npj Digit Med, 2024, 7(1): 169.
[15] Ortega-Bastidas P, Gómez B, Aqueveque P, et al. Instrumented timed up and go test (itug)-more than assessing time to predict falls: a systematic review[J]. Sensors (Basel), 2023, 23(7):3426
[16] Mancini M, Horak F B, Zampieri C, et al. Trunk accelerometry reveals postural instability in untreated Parkinson's disease[J]. Parkinsonism Relat Disord, 2011,17(7):557-562.
[17] Smith B A, Carlson-Kuhta P, Horak F B. Consistency in administration and response for the backward push and release test: a clinical assessment of postural responses[J]. Physiother Res Int, 2016,21(1):36-46.
[18] Morgan C, Masullo A, Mirmehdi M, et al. Automated real-world video analysis of sit-to-stand transitions predicts Parkinson's disease severity[J]. Digit Biomark, 2023,7(1): 92-103.
[19] Mellone S, Mancini M, King L A, et al. The quality of turning in Parkinson's disease: a compensatory strategy to prevent postural instability?[J].J Neuroeng Rehabil, 2016,13:39.
[20] Beck Y, Herman T, Brozgol M, et al. SpARC: a new approach to quantifying gait smoothness in patients with Parkinson's disease[J].J Neuroeng Rehabil, 2018,15(1):49.
[21] Pinto C, Schuch C P, Balbinot G, et al. Movement smoothness during a functional mobility task in subjects with Parkinson's disease and freezing of gait-an analysis using inertial measurement units[J]. J Neuroeng Rehabil, 2019,16(1):110.
[22] Mirelman A, Heman T, Yasinovsky K, et al. Fall risk and gait in Parkinson's disease: the role of the LRRK2 G2019S mutation[J]. Mov Disord, 2013,28(12):1683-1690.
[23] Akram S, Frank J S, Jog M. Parkinson's disease and segmental coordination during turning: II. Walking turns[J]. Can J Neurol Sci, 2013,40(4):520-526.
[24] Castagna A, Frittoli S, Ferrarin M, et al. Quantitative gait analysis in parkin disease: Possible role of dystonia[J]. Mov Disord, 2016,31(11):1720-1728.
[25] Krzysztoń K, Stolarski J, Kochanowski J. Evaluation of balance disorders in Parkinson's disease using simple diagnostic tests-not so simple to choose[J]. Front Neurol, 2018,9:932.
[26] Hubble R P, Naughton G A, Silburn P A, et al. Wearable sensor use for assessing standing balance and walking stability in people with Parkinson's disease: a systematic review[J]. PLoS One, 2015,10(4):e0123705.
基金
国家重点研发计划(2018YFC2001403)
PDF(1503 KB)
