An Algorithm for Accurate Marker-Based Gait Event Detection in Healthy and Pathological Populations During Complex Motor Tasks.
An Algorithm for Accurate Marker-Based Gait Event Detection in Healthy and Pathological Populations During Complex Motor Tasks.
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DOI：
10.3389/fbioe.2022.868928
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发表时间：
2022
影响因子：
5.7
通讯作者：
Mazza, Claudia
中科院分区：
文献类型：
--
作者：
Bonci, Tecla;Salis, Francesca;Scott, Kirsty;Alcock, Lisa;Becker, Clemens;Bertuletti, Stefano;Buckley, Ellen;Caruso, Marco;Cereatti, Andrea;Del Din, Silvia;Gazit, Eran;Hansen, Clint;Hausdorff, Jeffrey M.;Maetzler, Walter;Palmerini, Luca;Rochester, Lynn;Schwickert, Lars;Sharrack, Basil;Vogiatzis, Ioannis;Mazza, Claudia
关键词：
作为复杂运动任务的一部分，步态的量化越来越受到关注。这需要在不同于直线行走的条件下检测步态事件（GE）。本研究旨在提出并验证一种新的基于标记的GE检测方法，该方法也适用于曲线行走和台阶协商。该方法首先使用来自健康年轻人（YA，n = 20）的数据对现有算法进行测试，然后在来自以下五个队列的10个个体的数据中进行评估：老年人，慢性阻塞性肺疾病，多发性硬化症，帕金森病和股骨近端骨折。与GE检测的步长，持续时间，速度和站立/摆动持续时间的计算相关的错误的传播进行了研究。所有参与者进行了各种运动任务，包括曲线行走和步谈判，而参考GE确定使用一种有效的方法，利用压力鞋垫信号。计算灵敏度、阳性预测值（PPV）、F1评分、偏倚、精密度和准确度。还测试了基于标记物和压力鞋垫步幅参数之间的绝对一致性[组内相关系数（）]。在YA队列中，提出的方法优于现有方法，GE和条件的灵敏度、PPV和F1评分均≥ 99%，偏倚几乎为零（<10 ms）。总体而言，时间不准确对步幅持续时间、长度和速度的影响最小（绝对误差中位数≤1%）。在GE检测和传播到步幅参数方面，所有其他五个队列获得了类似的算法性能，其中还发现与压力鞋垫的绝对一致性非常好（）。总之，所提出的方法准确地检测GE标记数据在不同的步行条件下，并为各种步态障碍。
There is growing interest in the quantification of gait as part of complex motor tasks. This requires gait events (GEs) to be detected under conditions different from straight walking. This study aimed to propose and validate a new marker-based GE detection method, which is also suitable for curvilinear walking and step negotiation. The method was first tested against existing algorithms using data from healthy young adults (YA, n = 20) and then assessed in data from 10 individuals from the following five cohorts: older adults, chronic obstructive pulmonary disease, multiple sclerosis, Parkinson’s disease, and proximal femur fracture. The propagation of the errors associated with GE detection on the calculation of stride length, duration, speed, and stance/swing durations was investigated. All participants performed a variety of motor tasks including curvilinear walking and step negotiation, while reference GEs were identified using a validated methodology exploiting pressure insole signals. Sensitivity, positive predictive values (PPV), F1-score, bias, precision, and accuracy were calculated. Absolute agreement [intraclass correlation coefficient ()] between marker-based and pressure insole stride parameters was also tested. In the YA cohort, the proposed method outperformed the existing ones, with sensitivity, PPV, and F1 scores ≥ 99% for both GEs and conditions, with a virtually null bias (<10 ms). Overall, temporal inaccuracies minimally impacted stride duration, length, and speed (median absolute errors ≤1%). Similar algorithm performances were obtained for all the other five cohorts in GE detection and propagation to the stride parameters, where an excellent absolute agreement with the pressure insoles was also found (). In conclusion, the proposed method accurately detects GE from marker data under different walking conditions and for a variety of gait impairments.
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影响因子：
3.7
作者：
Kluge F;Del Din S;Cereatti A;Gaßner H;Hansen C;Helbostad JL;Klucken J;Küderle A;Müller A;Rochester L;Ullrich M;Eskofier BM;Mazzà C;Mobilise-D consortium
通讯作者：
Mobilise-D consortium
DOI：
10.3390/s20185377
发表时间：
2020-09-19
期刊：
Sensors (Basel, Switzerland)
影响因子：
--
作者：
Rehman RZU;Klocke P;Hryniv S;Galna B;Rochester L;Del Din S;Alcock L
通讯作者：
Alcock L
影响因子：
2.9
作者：
Mazzà C;Alcock L;Aminian K;Becker C;Bertuletti S;Bonci T;Brown P;Brozgol M;Buckley E;Carsin AE;Caruso M;Caulfield B;Cereatti A;Chiari L;Chynkiamis N;Ciravegna F;Del Din S;Eskofier B;Evers J;Garcia Aymerich J;Gazit E;Hansen C;Hausdorff JM;Helbostad JL;Hiden H;Hume E;Paraschiv-Ionescu A;Ireson N;Keogh A;Kirk C;Kluge F;Koch S;Küderle A;Lanfranchi V;Maetzler W;Micó-Amigo ME;Mueller A;Neatrour I;Niessen M;Palmerini L;Pluimgraaff L;Reggi L;Salis F;Schwickert L;Scott K;Sharrack B;Sillen H;Singleton D;Soltani A;Taraldsen K;Ullrich M;Van Gelder L;Vereijken B;Vogiatzis I;Warmerdam E;Yarnall A;Rochester L
通讯作者：
Rochester L
影响因子：
2.4
作者：
Crenna, P.;Carpinella, I.;Ferrarin, M.
通讯作者：
Ferrarin, M.
影响因子：
2.3
作者：
Brunnekreef, JJ;van Uden, CJT;Kooloos, JGM
通讯作者：
Kooloos, JGM