A novel accelerometry-based algorithm for the detection of step durations over short episodes of gait in healthy elderly. Issue 1 (December 2016)

• Record Type: Journal Article
• Authors: Micó-Amigo, M.; Kingma, Idsart; Ainsworth, Erik; Walgaard, Stefan; Niessen, Martijn; van Lummel, Rob C.; van Dieën, Jaap H.
• Is Part Of: Journal of neuroengineering and rehabilitation. Volume 13:Issue 1(2016)
• Page Start: 1
• Page End: 12
• Publication Date: 2016-12
• Subjects: Analysis of short episodes of gait -- Body-fixed-sensors -- Accelerometers -- Low-back accelerometry -- Heel accelerometry -- Step duration detection -- Healthy elderly; Nervous system -- Diseases -- Patients -- Rehabilitation -- Periodicals; Nervous system -- Wounds and injuries -- Rehabilitation -- Periodicals; Biomedical engineering; 616.8043005
• DOI: 10.1186/s12984-016-0145-6
• Languages: English
• ISSNs: 1743-0003

Abstract

Abstract Background The assessment of short episodes of gait is clinically relevant and easily implemented, especially given limited space and time requirements. BFS (body-fixed-sensors) are small, lightweight and easy to wear sensors, which allow the assessment of gait at relative low cost and with low interference. Thus, the assessment with BFS of short episodes of gait, extracted from dailylife physical activity or measured in a standardised and supervised setting, may add value in the study of gait quality of the elderly. The aim of this study was to evaluate the accuracy of a novel algorithm based on acceleration signals recorded at different human locations (lower back and heels) for the detection of step durations over short episodes of gait in healthy elderly subjects. Methods Twenty healthy elderly subjects (73.7 ± 7.9 years old) walked twice a distance of 5 m, wearing a BFS on the lower back, and on the outside of each heel. Moreover, an optoelectronic three-dimensional (3D) motion tracking system was used to detect step durations. A novel algorithm is presented for the detection of step durations from low-back and heel acceleration signals separately. The accuracy of the algorithm was assessed by comparing absolute differences in step duration between the three methods: step detection from the optoelectronic 3D motion tracking system, step detection from the application of the novel algorithm to low-back accelerations, and step detection from the application of the novel algorithm to heel accelerations. Results The proposed algorithm successfully detected all the steps, without false positives and without false negatives. Absolute average differences in step duration within trials and across subjects were calculated for each comparison, between low-back accelerations and the optoelectronic system were on average 22.4 ± 7.6 ms (4.0 ± 1.3 % of average step duration), between heel accelerations and the optoelectronic system were on average 20.7 ± 11.8 ms (3.7 ± 1.9 %), and between low-back accelerations and heel accelerations were on average 27.8 ± 15.1 ms (4.9 ± 2.5 % of average step duration). Conclusions This study showed that the presented novel algorithm detects step durations over short episodes of gait in healthy elderly subjects with acceptable accuracy from low-back and heel accelerations, which provides opportunities to extract a range of gait parameters from short episodes of gait.