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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. Low-Back acceleration signal (with a sampling rate of 100 samples/s) and detected events. Typical example of a segmented AP acceleration signal collected at the lower back (blue), and the “Events” (magenta) detected by the algorithm applied to low-back accelerometry. These “Events” were obtained from the shift by a 15 % TL samples of the instants at which the peaks were found. The intervals between “Events” permitted to calculate step durations. b. Heel acceleration signals (with a sampling rate of 100 samples/s) and detected events. Typical example of segmented AP acceleration signals collected at the heels, left heel (red) and right heel (blue), and the respective “Events” detected by the algorithm. These “Events” were obtained from the shift by a 5 % TL samples of the instants at which the peaks were found. The intervals between “Events” calculated for each of the heel acceleration signal correspond to stride durations, and the combination of the events detected from both heel accelerometry permitted to obtain step durations
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Fig5: a. Low-Back acceleration signal (with a sampling rate of 100 samples/s) and detected events. Typical example of a segmented AP acceleration signal collected at the lower back (blue), and the “Events” (magenta) detected by the algorithm applied to low-back accelerometry. These “Events” were obtained from the shift by a 15 % TL samples of the instants at which the peaks were found. The intervals between “Events” permitted to calculate step durations. b. Heel acceleration signals (with a sampling rate of 100 samples/s) and detected events. Typical example of segmented AP acceleration signals collected at the heels, left heel (red) and right heel (blue), and the respective “Events” detected by the algorithm. These “Events” were obtained from the shift by a 5 % TL samples of the instants at which the peaks were found. The intervals between “Events” calculated for each of the heel acceleration signal correspond to stride durations, and the combination of the events detected from both heel accelerometry permitted to obtain step durations

Mentions: Shift forwards by 15 % TL samples the instants at which the peaks were selected, in order to define the instants that approximate heel-strike events in time. The intervals defined between the shifted peaks, named “Events”, allow step durations to be calculated (Fig. 5a).Fig. 5


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. Low-Back acceleration signal (with a sampling rate of 100 samples/s) and detected events. Typical example of a segmented AP acceleration signal collected at the lower back (blue), and the “Events” (magenta) detected by the algorithm applied to low-back accelerometry. These “Events” were obtained from the shift by a 15 % TL samples of the instants at which the peaks were found. The intervals between “Events” permitted to calculate step durations. b. Heel acceleration signals (with a sampling rate of 100 samples/s) and detected events. Typical example of segmented AP acceleration signals collected at the heels, left heel (red) and right heel (blue), and the respective “Events” detected by the algorithm. These “Events” were obtained from the shift by a 5 % TL samples of the instants at which the peaks were found. The intervals between “Events” calculated for each of the heel acceleration signal correspond to stride durations, and the combination of the events detected from both heel accelerometry permitted to obtain step durations
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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

Fig5: a. Low-Back acceleration signal (with a sampling rate of 100 samples/s) and detected events. Typical example of a segmented AP acceleration signal collected at the lower back (blue), and the “Events” (magenta) detected by the algorithm applied to low-back accelerometry. These “Events” were obtained from the shift by a 15 % TL samples of the instants at which the peaks were found. The intervals between “Events” permitted to calculate step durations. b. Heel acceleration signals (with a sampling rate of 100 samples/s) and detected events. Typical example of segmented AP acceleration signals collected at the heels, left heel (red) and right heel (blue), and the respective “Events” detected by the algorithm. These “Events” were obtained from the shift by a 5 % TL samples of the instants at which the peaks were found. The intervals between “Events” calculated for each of the heel acceleration signal correspond to stride durations, and the combination of the events detected from both heel accelerometry permitted to obtain step durations
Mentions: Shift forwards by 15 % TL samples the instants at which the peaks were selected, in order to define the instants that approximate heel-strike events in time. The intervals defined between the shifted peaks, named “Events”, allow step durations to be calculated (Fig. 5a).Fig. 5

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