TY - JOUR
T1 - An Algorithm for Accurate Marker-Based Gait Event Detection in Healthy and Pathological Populations During Complex Motor Tasks
AU - on behalf of the Mobilise-D consortium
AU - Bonci, Tecla
AU - Salis, Francesca
AU - Scott, Kirsty
AU - Alcock, Lisa
AU - Becker, Clemens
AU - Bertuletti, Stefano
AU - Buckley, Ellen
AU - Caruso, Marco
AU - Cereatti, Andrea
AU - Del Din, Silvia
AU - Gazit, Eran
AU - Hansen, Clint
AU - Hausdorff, Jeffrey M.
AU - Maetzler, Walter
AU - Palmerini, Luca
AU - Rochester, Lynn
AU - Schwickert, Lars
AU - Sharrack, Basil
AU - Vogiatzis, Ioannis
AU - Mazzà, Claudia
N1 - Publisher Copyright:
Copyright © 2022 Bonci, Salis, Scott, Alcock, Becker, Bertuletti, Buckley, Caruso, Cereatti, Del Din, Gazit, Hansen, Hausdorff, Maetzler, Palmerini, Rochester, Schwickert, Sharrack, Vogiatzis and Mazzà.
PY - 2022/6/2
Y1 - 2022/6/2
N2 - 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 ((Formula presented.))] 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 ((Formula presented.)). In conclusion, the proposed method accurately detects GE from marker data under different walking conditions and for a variety of gait impairments.
AB - 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 ((Formula presented.))] 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 ((Formula presented.)). In conclusion, the proposed method accurately detects GE from marker data under different walking conditions and for a variety of gait impairments.
KW - gait analysis
KW - gait cycle
KW - spatio-temporal gait parameters
KW - stereophotogrammetry
KW - stride duration
KW - stride length
KW - stride speed
UR - http://www.scopus.com/inward/record.url?scp=85133278810&partnerID=8YFLogxK
U2 - 10.3389/fbioe.2022.868928
DO - 10.3389/fbioe.2022.868928
M3 - ???researchoutput.researchoutputtypes.contributiontojournal.article???
C2 - 35721859
AN - SCOPUS:85133278810
SN - 2296-4185
VL - 10
JO - Frontiers in Bioengineering and Biotechnology
JF - Frontiers in Bioengineering and Biotechnology
M1 - 868928
ER -