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 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.

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)

Metric	Name	Description	Input	Output	Language	References
Gait Sequence Detection	GSDA	Based on a frequency-based approach, this algorithm is implemented on the vertical and anterior–posterior acceleration signals. First, these are band pass filtered to keep frequencies between 0.5 and 3 Hz. Next, a convolution of a 2 Hz sinewave (representing a template for a gait cycle) is performed, from which local maxima will be detected to define the regions of gait	acc_v: vertical acceleration acc_ap: anterior–posterior acceleration WinS = 3 s; window size for convolution OL = 1.5 s; overlap of windows Activity_thresh = 0.01; Motion threshold Fs: sampling frequency	Start: beginning of N gait sequences [s] relative to the start of a recording or a test/trial. Format: 1 × N vector End: termination of N gait sequences [s] relative to the start of a recording or a test/trial. Format: 1 × N vector	Matlab®	Iluz, Gazit [40]
	GSDB	This algorithm, based on a time domain-approach, detects the gait periods based on identified steps. First, the norm of triaxial acceleration signal is low-pass filtered (FIR, fc = 3.2 Hz), then a peak detection procedure using a threshold of 0.1 [g] is applied to identify steps. Consecutive steps, detected using an adaptive step duration threshold are associated to gait sequences	acc_norm: norm of the 3D-accelerometer signal Fs: sampling frequency th: peak detection threshold: 0.1 (g)	Start: beginning of N gait sequences [s] relative to the start of a recording or a test/trial. Format: 1 × N vector End: termination of N gait sequences [s] relative to the start of a recording or a test/trial. Format: 1 × N vector	Matlab®	Paraschiv-Ionescu, Newman [41]
	GSDc	This algorithm utilizes the same approach as GSDB the only difference being a different threshold for peak detection of 0.15 [g]	acc_norm: norm of the 3D-accelerometer signal Fs: sampling frequency th: peak detection threshold: 0.15 (g)	Start: beginning of N gait sequences [s] relative to the start of a recording or a test/trial. Format: 1 × N vector End: termination of N gait sequences [s] relative to the start of a recording or a test/trial. Format: 1 × N vector	Matlab®	Paraschiv-Ionescu, Newman [41]

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:

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”.