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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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“Resegmented signal”, “Segmented signal” and “Template signal” with a sampling rate of 100 samples/s. Typical example of an AP acceleration signal collected at the lower back and segmented from shortly before the first heel-strike of the gait episode until shortly before the foremost foot reached the end of the trial (“Segmented signal”, blue). New segmentation of the raw AP acceleration signal, which contains three sections with a number of samples equivalent to the template length, one section prior to the start marker of the initial segmentation, and two sections after the end marker of the initial segmentation (“Resegmented signal”, green). “Template signal” (black) is the average of all the sections defined in step 2.3
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Fig3: “Resegmented signal”, “Segmented signal” and “Template signal” with a sampling rate of 100 samples/s. Typical example of an AP acceleration signal collected at the lower back and segmented from shortly before the first heel-strike of the gait episode until shortly before the foremost foot reached the end of the trial (“Segmented signal”, blue). New segmentation of the raw AP acceleration signal, which contains three sections with a number of samples equivalent to the template length, one section prior to the start marker of the initial segmentation, and two sections after the end marker of the initial segmentation (“Resegmented signal”, green). “Template signal” (black) is the average of all the sections defined in step 2.3

Mentions: Repeat step 2.4 until a single new signal, named “Template signal”, is obtained. The length of this signal is TL samples. This signal is the average of all the sections defined in step 2.3 (Fig. 3).Fig. 3


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)

“Resegmented signal”, “Segmented signal” and “Template signal” with a sampling rate of 100 samples/s. Typical example of an AP acceleration signal collected at the lower back and segmented from shortly before the first heel-strike of the gait episode until shortly before the foremost foot reached the end of the trial (“Segmented signal”, blue). New segmentation of the raw AP acceleration signal, which contains three sections with a number of samples equivalent to the template length, one section prior to the start marker of the initial segmentation, and two sections after the end marker of the initial segmentation (“Resegmented signal”, green). “Template signal” (black) is the average of all the sections defined in step 2.3
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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

Fig3: “Resegmented signal”, “Segmented signal” and “Template signal” with a sampling rate of 100 samples/s. Typical example of an AP acceleration signal collected at the lower back and segmented from shortly before the first heel-strike of the gait episode until shortly before the foremost foot reached the end of the trial (“Segmented signal”, blue). New segmentation of the raw AP acceleration signal, which contains three sections with a number of samples equivalent to the template length, one section prior to the start marker of the initial segmentation, and two sections after the end marker of the initial segmentation (“Resegmented signal”, green). “Template signal” (black) is the average of all the sections defined in step 2.3
Mentions: Repeat step 2.4 until a single new signal, named “Template signal”, is obtained. The length of this signal is TL samples. This signal is the average of all the sections defined in step 2.3 (Fig. 3).Fig. 3

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