An Algorithm for Accurate Marker-Based Gait Event Detection in Healthy and Pathological Populations During Complex Motor Tasks

被引:8
|
作者
Bonci, Tecla [1 ]
Salis, Francesca [2 ]
Scott, Kirsty [1 ]
Alcock, Lisa [3 ]
Becker, Clemens [4 ]
Bertuletti, Stefano [2 ]
Buckley, Ellen [1 ]
Caruso, Marco [5 ]
Cereatti, Andrea [5 ]
Del Din, Silvia [3 ]
Gazit, Eran [6 ]
Hansen, Clint [7 ]
Hausdorff, Jeffrey M. [6 ,8 ,9 ]
Maetzler, Walter [7 ]
Palmerini, Luca [10 ,11 ]
Rochester, Lynn [3 ,12 ]
Schwickert, Lars [4 ]
Sharrack, Basil [13 ]
Vogiatzis, Ioannis [14 ]
Mazza, Claudia [1 ]
机构
[1] Univ Sheffield, Insigno Inst Sil Med, Dept Mech Engn, Sheffield, England
[2] Univ Sassari, Dept Biomed Sci, Sassari, Italy
[3] Newcastle Univ, Translat & Clin Res Inst, Fac Med Sci, Newcastle Upon Tyne, England
[4] Robert Bosch Krankenhaus, Dept Geriatr Rehabil, Stuttgart, Germany
[5] Politecn Torino, Dept Elect & Telecommun, Turin, Italy
[6] Tel Aviv Sourasky Med Ctr, Ctr Study Movement Cognit & Mobil, Tel Aviv, Israel
[7] Univ Hosp Schleswig Holstein, Kiel Univ, Dept Neurol, Campus Kiel, Kiel, Germany
[8] Tel Aviv Univ, Sackler Fac Med, Sagol Sch Neurosci, Dept Phys Therapy, Tel Aviv, Israel
[9] Rush Univ Med Ctr, Rush Alzheimers Dis Ctr, Dept Orthopaed Surg, Chicago, IL USA
[10] Univ Bologna, Dept Elect Elect & Informat Engn Guglielmo Marconi, Bologna, Italy
[11] Univ Bologna, Hlth Sci & Technol Interdept Ctr Ind Res, CIRI, SDV, Bologna, Italy
[12] Newcastle Tyne Hosp NHS Fdn Trust, Newcastle Upon Tyne, England
[13] Sheffield Teaching Hosp NHS Fdn Trust, Dept Neurosci, Sheffield NIHR Translat Neurosci BRC, Sheffield, England
[14] Northumbria Univ, Dept Sport Exercise & Rehabil, Newcastle Upon Tyne, England
来源
FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY | 2022年 / 10卷
基金
英国惠康基金; 英国工程与自然科学研究理事会; 欧盟地平线“2020”;
关键词
gait analysis; spatio-temporal gait parameters; gait cycle; stride length; stride duration; stride speed; stereophotogrammetry; PARKINSONS-DISEASE; WALKING; PEOPLE; ADULTS; VALIDATION; TREADMILL; MOBILITY;
D O I
10.3389/fbioe.2022.868928
中图分类号
Q81 [生物工程学(生物技术)]; Q93 [微生物学];
学科分类号
071005 ; 0836 ; 090102 ; 100705 ;
摘要
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 (ICC2,1 )] 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 (ICC (2,1) = 0.817 - 0.999 ). In conclusion, the proposed method accurately detects GE from marker data under different walking conditions and for a variety of gait impairments.
引用
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页数:14
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