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A novel accelerometry-based algorithm for the detection of step durations over short episodes of gait in healthy elderly.

Micó-Amigo ME, Kingma I, Ainsworth E, Walgaard S, Niessen M, van Lummel RC, van Dieën JH - J Neuroeng Rehabil (2016)

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: MOVE Research Institute Amsterdam, Department of Human Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.

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.

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Average absolute differences in step duration within trials and for each subject are presented for each pair of compared methods with a boxplot (a). Percentage of average absolute differences in step duration over the average step duration calculated with both systems within trials and for each subject are presented for each pair of compared methods with a boxplot (b). *LB = low-back accelerometry, HE = heel accelerometry, OP = Optotrak. The boxplot includes minimum, first quartile (q1, 25 %), median, third quartile (q3, 75 %), maximum and outlier values
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Fig6: Average absolute differences in step duration within trials and for each subject are presented for each pair of compared methods with a boxplot (a). Percentage of average absolute differences in step duration over the average step duration calculated with both systems within trials and for each subject are presented for each pair of compared methods with a boxplot (b). *LB = low-back accelerometry, HE = heel accelerometry, OP = Optotrak. The boxplot includes minimum, first quartile (q1, 25 %), median, third quartile (q3, 75 %), maximum and outlier values

Mentions: A Shapiro-Wilk’s test showed that absolute differences between methods did not deviate from normal distribution for absolute differences in step duration between low-back accelerometry and Optotrak (p = 0.97) and between low-back accelerometry and heel accelerometry (p = 0.50). However, when comparing estimates from heel accelerometry versus Optotrak, absolute differences were not normally distributed (p = 0.01), with the presence of two outliers (Fig. 6).Fig. 6


A novel accelerometry-based algorithm for the detection of step durations over short episodes of gait in healthy elderly.

Micó-Amigo ME, Kingma I, Ainsworth E, Walgaard S, Niessen M, van Lummel RC, van Dieën JH - J Neuroeng Rehabil (2016)

Average absolute differences in step duration within trials and for each subject are presented for each pair of compared methods with a boxplot (a). Percentage of average absolute differences in step duration over the average step duration calculated with both systems within trials and for each subject are presented for each pair of compared methods with a boxplot (b). *LB = low-back accelerometry, HE = heel accelerometry, OP = Optotrak. The boxplot includes minimum, first quartile (q1, 25 %), median, third quartile (q3, 75 %), maximum and outlier values
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4837611&req=5

Fig6: Average absolute differences in step duration within trials and for each subject are presented for each pair of compared methods with a boxplot (a). Percentage of average absolute differences in step duration over the average step duration calculated with both systems within trials and for each subject are presented for each pair of compared methods with a boxplot (b). *LB = low-back accelerometry, HE = heel accelerometry, OP = Optotrak. The boxplot includes minimum, first quartile (q1, 25 %), median, third quartile (q3, 75 %), maximum and outlier values
Mentions: A Shapiro-Wilk’s test showed that absolute differences between methods did not deviate from normal distribution for absolute differences in step duration between low-back accelerometry and Optotrak (p = 0.97) and between low-back accelerometry and heel accelerometry (p = 0.50). However, when comparing estimates from heel accelerometry versus Optotrak, absolute differences were not normally distributed (p = 0.01), with the presence of two outliers (Fig. 6).Fig. 6

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: MOVE Research Institute Amsterdam, Department of Human Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.

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.

Show MeSH