Correction: Assessing real-world gait with digital technology? Validation, insights and recommendations from the Mobilise-D consortium

Correction: Journal of NeuroEngineering and Rehabilitation (2023) 20:78 https://doi.org/10.1186/s12984-023-01198-5

Following publication of the original article [1], the author noticed the errors in Table 1, and in Discussion section.

In Table 1 under Metric (Gait sequence detection) column, the algorithms GSD_B was updated with wrong description, input, output, language and citation and GSD_c with wrong description has been corrected as shown below:

Table 1 Description of algorithms for each metric: gait sequence detection (GSD), initial contact event detection (ICD), cadence estimation (CAD) and stride length estimation (SL)

In Discussion section, the paragraph should read as "Based on our findings collectively, we recommend using GSD_B on cohorts with slower gait speeds and substantial gait impairments (e.g., proximal femoral fracture). This may be because this algorithm is based on the acceleration norm (overall accelerometry signal rather than a specific axis/direction (e.g., vertical), hence it is more robust to sensor misalignments that are common in unsupervised real-life settings. Moreover, the use of adaptive threshold, that are derived from the features of a subject’s data and applied to step duration for detection of steps belonging to gait sequences, allows increased robustness of the algorithm to irregular and unstable gait patterns" instead of “Based on our findings collectively, we recommend using GSD_B on cohorts with slower gait speeds and substantial gait impairments (e.g., proximal femoral fracture). This may be because this algorithm is based on the acceleration norm (overall accelerometry signal rather than a specific axis/direction (e.g., vertical), hence it is more robust to sensor misalignments that are common in unsupervised real-life settings [41]. Moreover, the use of adaptive thresholds, that are derived from the features of a subject’s data and applied to the amplitude of acceleration norm and to step duration for detection of steps belonging to gait sequences, allows increased robustness of the algorithm to irregular and unstable gait patterns”.