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

• DOI: 10.3389/fbioe.2022.868928
• Publication Date: 2022-06-02T11:54:35Z
• Authors: Tecla Bonci; Francesca Salis; Kirsty Scott; Lisa Alcock; Clemens Becker; Stefano Bertuletti; Ellen Buckley; Marco Caruso; Andrea Cereatti; Silvia Del Din; Eran Gazit; Clint Hansen; Jeffrey M. Hausdorff; Walter Maetzler; Luca Palmerini; Lynn Rochester; Lars Schwickert; Basil Sharrack; Ioannis Vogiatzis; Claudia Mazzà
• Keywords: STRIDE

There is growing interest in the quantification of gait as part complex motor tasks. This requires events (GEs) to be detected under conditions different from straight walking. study aimed propose and validate a new marker-based GE detection method, which also suitable for curvilinear walking step negotiation. The method was first tested against existing algorithms using data healthy young adults (YA, n = 20) then assessed 10 individuals following five cohorts: older adults, chronic obstructive pulmonary disease, multiple sclerosis, Parkinson's proximal femur fracture. propagation errors associated with on calculation stride length, duration, speed, stance/swing durations investigated. All participants performed variety tasks including negotiation, while reference GEs were identified validated methodology exploiting pressure insole signals. Sensitivity, positive predictive values (PPV), F1-score, bias, precision, accuracy calculated. Absolute agreement [intraclass correlation coefficient ( ICC2,1 )] between parameters tested. In YA cohort, proposed outperformed ones, sensitivity, PPV, F1 scores ≥ 99% both conditions, virtually null bias (<10 ms). Overall, temporal inaccuracies minimally impacted speed (median absolute ≤1%). Similar algorithm performances obtained all other cohorts parameters, where an excellent insoles found ICC2,1=0.817- 0.999 ). conclusion, accurately detects marker impairments.