Transferreaktionen im System 9Be+16O

SIGLECopy held by FIZ Karlsruhe; available from UB/TIB Hannover / FIZ - Fachinformationszzentrum Karlsruhe / TIB - Technische InformationsbibliothekDEGerman

Mobilise-D insights to estimate real-world walking speed in multiple conditions with a wearable device

This study aimed to validate a wearable device's walking speed estimation pipeline, considering complexity, speed, and walking bout duration. The goal was to provide recommendations on the use of wearable devices for real-world mobility analysis. Participants with Parkinson's Disease, Multiple Sclerosis, Proximal Femoral Fracture, Chronic Obstructive Pulmonary Disease, Congestive Heart Failure, and healthy older adults (n = 97) were monitored in the laboratory and the real-world (2.5 h), using a lower back wearable device. Two walking speed estimation pipelines were validated across 4408/1298 (2.5 h/laboratory) detected walking bouts, compared to 4620/1365 bouts detected by a multi-sensor reference system. In the laboratory, the mean absolute error (MAE) and mean relative error (MRE) for walking speed estimation ranged from 0.06 to 0.12 m/s and - 2.1 to 14.4%, with ICCs (Intraclass correlation coefficients) between good (0.79) and excellent (0.91). Real-world MAE ranged from 0.09 to 0.13, MARE from 1.3 to 22.7%, with ICCs indicating moderate (0.57) to good (0.88) agreement. Lower errors were observed for cohorts without major gait impairments, less complex tasks, and longer walking bouts. The analytical pipelines demonstrated moderate to good accuracy in estimating walking speed. Accuracy depended on confounding factors, emphasizing the need for robust technical validation before clinical application

Correction to: Mobilise-D insights to estimate real-world walking speed in multiple conditions with a wearable device (Scientific Reports, (2024), 14, 1, (1754), 10.1038/s41598-024-51766-5)

\ua9 The Author(s) 2024.Correction to: Scientific Reportshttps://doi.org/10.1038/s41598-024-51766-5, published online 19 January 2024 The original version of this Article contained an error in Figure 7 where an incorrect reference was cited for one of the recommended algorithms for Gait Speed Detection (GSD). The original Figure 7 and accompanying legend appear below. (Figure presented.) Overview over the diferent algorithmic steps of the analytical pipeline with short explanations of the intermediate and fnal outputs of each of the algorithmic blocks; gait sequence detection (GSD), initial contact detection (ICD), cadence estimation (CAD) and stride length estimation (SL). Te algorithm column indicates the used algorithms for the two pipelines P1 (HA, COPD, CHF). (MS, PD, PFF) and P2 (MS, PD, PFF) Short citations for the algorithms are provided below the fgure. For more details see Table 1 in26. In addition, the Supplementary Information 1 file published with this Article contained errors in Tables 1 and 2. The Intraclass Correlation Coefficients (ICCs) for walking speed were incorrectly reported instead of the correct ICC values for stride length and cadence. The original Supplementary Information 1 file is provided below. The original Article and the Supplementary Information 1 file that accompanies the original Article have been corrected