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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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a Typical example of a “SD Difference signal” (blue) and a “Correlation signal” (green) with a sampling rate of 100 samples/s. The “SD Difference signal” was obtained from the standard deviation of the difference in amplitude between the “Template signal” and a sliding window (with a number of samples equivalent to the template length) through the “Resegmented signal”. This signal has lower values at the start of the intervals along which the “Resegmented signal” and the “Template signal” are more similar in shape and amplitude. “Correlation signal” was obtained from the calculation of correlation coefficients between the “Template signal” and a sliding window through the “Resegmented signal”, being multiplied by the ratio of ranges of the “Resegmented signal” and the “Template signal”. The resulting signal has higher values at the start of the intervals along which the “Resegmented signal” and the “Template signal” are more similar in shape and amplitude. b “Coefficient signal” (blue) and “Selected peaks” (red squares) with a sampling rate of 100 samples/s. The normalized ratio signal between the “Correlation signal” and the “SD Difference signal” permitted to obtain the “Coefficient signal”. “Selected peaks” are the peaks from the “Coefficient signal” which are found within the dimension of the “Segmented signal” and are located at a distance of at least a 60 % TL (template length) samples from each other
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Fig4: a Typical example of a “SD Difference signal” (blue) and a “Correlation signal” (green) with a sampling rate of 100 samples/s. The “SD Difference signal” was obtained from the standard deviation of the difference in amplitude between the “Template signal” and a sliding window (with a number of samples equivalent to the template length) through the “Resegmented signal”. This signal has lower values at the start of the intervals along which the “Resegmented signal” and the “Template signal” are more similar in shape and amplitude. “Correlation signal” was obtained from the calculation of correlation coefficients between the “Template signal” and a sliding window through the “Resegmented signal”, being multiplied by the ratio of ranges of the “Resegmented signal” and the “Template signal”. The resulting signal has higher values at the start of the intervals along which the “Resegmented signal” and the “Template signal” are more similar in shape and amplitude. b “Coefficient signal” (blue) and “Selected peaks” (red squares) with a sampling rate of 100 samples/s. The normalized ratio signal between the “Correlation signal” and the “SD Difference signal” permitted to obtain the “Coefficient signal”. “Selected peaks” are the peaks from the “Coefficient signal” which are found within the dimension of the “Segmented signal” and are located at a distance of at least a 60 % TL (template length) samples from each other

Mentions: Normalize the resulting signal. This signal, denoted as “SD Difference signal”, has local minima at the start of the intervals along which the “Resegmented signal” and the “Template signal” have the best match, and therefore are more similar in shape and amplitude (Fig. 4a).Fig. 4


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)

a Typical example of a “SD Difference signal” (blue) and a “Correlation signal” (green) with a sampling rate of 100 samples/s. The “SD Difference signal” was obtained from the standard deviation of the difference in amplitude between the “Template signal” and a sliding window (with a number of samples equivalent to the template length) through the “Resegmented signal”. This signal has lower values at the start of the intervals along which the “Resegmented signal” and the “Template signal” are more similar in shape and amplitude. “Correlation signal” was obtained from the calculation of correlation coefficients between the “Template signal” and a sliding window through the “Resegmented signal”, being multiplied by the ratio of ranges of the “Resegmented signal” and the “Template signal”. The resulting signal has higher values at the start of the intervals along which the “Resegmented signal” and the “Template signal” are more similar in shape and amplitude. b “Coefficient signal” (blue) and “Selected peaks” (red squares) with a sampling rate of 100 samples/s. The normalized ratio signal between the “Correlation signal” and the “SD Difference signal” permitted to obtain the “Coefficient signal”. “Selected peaks” are the peaks from the “Coefficient signal” which are found within the dimension of the “Segmented signal” and are located at a distance of at least a 60 % TL (template length) samples from each other
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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

Fig4: a Typical example of a “SD Difference signal” (blue) and a “Correlation signal” (green) with a sampling rate of 100 samples/s. The “SD Difference signal” was obtained from the standard deviation of the difference in amplitude between the “Template signal” and a sliding window (with a number of samples equivalent to the template length) through the “Resegmented signal”. This signal has lower values at the start of the intervals along which the “Resegmented signal” and the “Template signal” are more similar in shape and amplitude. “Correlation signal” was obtained from the calculation of correlation coefficients between the “Template signal” and a sliding window through the “Resegmented signal”, being multiplied by the ratio of ranges of the “Resegmented signal” and the “Template signal”. The resulting signal has higher values at the start of the intervals along which the “Resegmented signal” and the “Template signal” are more similar in shape and amplitude. b “Coefficient signal” (blue) and “Selected peaks” (red squares) with a sampling rate of 100 samples/s. The normalized ratio signal between the “Correlation signal” and the “SD Difference signal” permitted to obtain the “Coefficient signal”. “Selected peaks” are the peaks from the “Coefficient signal” which are found within the dimension of the “Segmented signal” and are located at a distance of at least a 60 % TL (template length) samples from each other
Mentions: Normalize the resulting signal. This signal, denoted as “SD Difference signal”, has local minima at the start of the intervals along which the “Resegmented signal” and the “Template signal” have the best match, and therefore are more similar in shape and amplitude (Fig. 4a).Fig. 4

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