Improving the Accuracy of Pedometer Used by the Elderly with the FFT Algorithm

Ichinoseki‐Sekine, Noriko; Kuwae, Yutaka; Higashi, Yuji; Fujimoto, Toshiro; Shimizu, Masaki; Tamura, Toshiyo

doi:10.1249/01.mss.0000227641.68360.c2

Cited by 20 publications

(16 citation statements)

References 26 publications

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“…Previous studies have also reported difficulties in obtaining accurate step counts at slow and high walking speeds [8, 14, 31, 32]. A small number of studies report good accuracy for all walking speeds [14, 20, 21, 31]. However, many of these studies do not specify gait velocities and refer to them as ‘slow, normal, or fast-paced’, while studies that specify gait velocities look at smaller ranges starting at 0.5 to 0.9 m/s [14, 32] and don’t exceed 2 m/s.…”

Section: Discussionmentioning

confidence: 99%

“…Increases in walking speed and the ability to vary cadence demonstrate increased function level [18], reduced risk, and higher predictions of survival [17, 19]. While a small number of studies have shown that results from the methods they used are not affected by different walking speeds, accuracy during shuffling, stair climbing, and jogging have yet to be investigated and only limited gait velocity ranges are examined [14, 20, 21]. Furthermore, the use of step counts as a measure of physical activity is limited as the characteristics of the steps are unknown.…”

Section: Introductionmentioning

confidence: 99%

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Validity of using tri-axial accelerometers to measure human movement – Part II: Step counts at a wide range of gait velocities

Fortune

Lugade

Morrow

et al. 2014

Medical Engineering & Physics

130

115

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A subject-specific step counting method with a high accuracy level at all walking speeds is needed to assess the functional level of impaired patients. The study aim was to validate step counts and cadence calculations from acceleration data by comparison to video data during dynamic activity. Custom-built activity monitors, each containing one tri-axial accelerometer, were placed on the ankles, thigh, and waist of 11 healthy adults. ICC values were greater than 0.98 for video inter-rater reliability of all step counts. The activity monitoring system (AMS) algorithm demonstrated a median (interquartile range; IQR) agreement of 92% (8%) with visual observations during walking/jogging trials at gait velocities ranging from 0.1 m/s to 4.8 m/s, while FitBits (ankle and waist), and a Nike Fuelband (wrist) demonstrated agreements of 92% (36%), 93% (22%), and 33% (35%), respectively. The algorithm results demonstrated high median (IQR) step detection sensitivity (95% (2%)), positive predictive value (PPV) (99% (1%)), and agreement (97% (3%)) during a laboratory-based simulated free-living protocol. The algorithm also showed high median (IQR) sensitivity, PPV, and agreement identifying walking steps (91% (5%), 98% (4%), and 96% (5%)), jogging steps (97% (6%), 100% (1%), and 95% (6%)), and less than 3% mean error in cadence calculations.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Validity of using tri-axial accelerometers to measure human movement – Part II: Step counts at a wide range of gait velocities

Fortune

Lugade

Morrow

et al. 2014

Medical Engineering & Physics

130

115

View full text Add to dashboard Cite

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“…A previous study reported 84% adherence with adults age 60 years and over and just 60–62% adherence for subjects of ages 12 to 39 years in a study involving a hip-located ActiGraph worn for four days (Troiano et al , 2008), which offers room for improvement. In addition to adherence issues, many studies have reported reduced accuracy of many step counting devices at lower gait velocities (Dijkstra et al , 2008; Ryan et al , 2006; Le Masurier and Tudor-Locke, 2003; Greene et al , 2010; Ichinoseki-Sekine et al , 2006). Individuals with reduced physical or cognitive function, who present greater issues with adherence, often walk at slower gait velocities.…”

Section: Introductionmentioning

confidence: 99%

“…This lack of adaptivity means that the settings need to be calibrated per individual and may result in an overestimation of step counts in the free-living environment (Bassett Jr and John, 2010). We have previously developed a step detection algorithm with adaptive thresholds using an AM system consisting of four accelerometers (waist, right thigh, and bilateral ankles) and demonstrated that, unlike other algorithms or devices used in other studies (Dijkstra et al , 2008; Ryan et al , 2006; Le Masurier and Tudor-Locke, 2003; Greene et al , 2010; Ichinoseki-Sekine et al , 2006), it performs with acceptable accuracy at slow gait velocities, even as low as 0.1 m/s, in addition to normal and fast walking speeds, and under simulated free-living conditions without requiring calibration (Fortune et al , 2014a). However, an algorithm which can perform accurately at a number of locations using a minimal number of AMs would be beneficial in cases where subject adherence may be an issue.…”

Section: Introductionmentioning

confidence: 99%

Step detection using multi- versus single tri-axial accelerometer-based systems

et al. 2015

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Multiple sensors are often considered necessary for increased step count accuracy. However, subject adherence to device-wear increases using a minimal number of activity monitors (AMs). The study aims were to determine and compare the validity of using multiple AMs versus a single AM to detect steps by comparison to video using a modification of an algorithm previously developed for a four accelerometer-AM system capable, unlike other algorithms, of accurate step detection for gait velocities as low as 0.1 m/s. Twelve healthy adults wore ankle, thigh, and waist AMs while performing walking/jogging trials at gait velocities from 0.1–4.8 m/s and a simulated free-living dynamic activities protocol. Nineteen older adults wore ankle and waist AMs while walking at velocities from 0.5–2.0 m/s. As little as one AM (thigh or waist) accurately detected steps for velocities >0.5 m/s. A single ankle AM accurately detected steps for velocities ≥0.1 m/s. Only the thigh AM could not accurately detect steps during the dynamic activities. Only the thigh-ankle combination or single waist AM could accurately distinguish between walking and jogging steps. These laboratory-based results suggest that the presented algorithm can accurately detect steps in the free-living environment using only one ankle or waist AM.

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“…the step frequency (SF). Algorithms based on Autocorrelation (AC) 7,8 and Power Spectral Density (PSD) 9 have been used to this end. These methods require analysis of data collected over a time window (TW) of a given size.…”

Section: Introductionmentioning

confidence: 99%

Accuracy of Estimates of Step Frequency From a Wearable Gait Monitor

Punt¹,

Wittink²,

Bent³

et al. 2015

JournalMTM

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Background: Assessment of gait activity by accelerometry requires data analysis. Currently several methods are used to estimate step frequency. At present the relation between step frequency estimation, gait speed and minimal required time window length remains unknown.Aims: The purpose of the study was to assess the accuracy of estimates of step frequency (SF) from trunk acceleration data analyzed with commonly used algorithms and time window lengths, at a wide range of gait speeds.Method: Twenty healthy young subjects performed an incremental treadmill protocol from 1 km/h up to 6 km/h, with steps of 1 km/h. Each speed condition was maintained for two minutes. A waist worn accelerometer recorded trunk accelerations, while video analysis provided the correct number of steps taken during each gait speed condition. Accuracy of two commonly used signal analysis methods (autocorrelation, fast Fourier transformation) was examined with time windows of two, four and eight seconds.Results: Our main finding was that accuracy of SF estimates with fast Fourier transformation and autocorrelation improved with increasing time window size, only at the lower gait speeds. Accuracy of SF estimation was lower at low gait speeds independent of the algorithm and time window used. Conclusion:We recommend a minimum TW length of 4 seconds when using AC and PSD algorithms and when using the PSD algorithm to use spectral averaging, as this leads to better results at short TW and low gait speeds.

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Improving the Accuracy of Pedometer Used by the Elderly with the FFT Algorithm

Abstract: We suggest that our FFT method is suitable for estimating the number of steps during walking in this population.

Cited by 20 publications

References 26 publications

Validity of using tri-axial accelerometers to measure human movement – Part II: Step counts at a wide range of gait velocities

Validity of using tri-axial accelerometers to measure human movement – Part II: Step counts at a wide range of gait velocities

Step detection using multi- versus single tri-axial accelerometer-based systems

Accuracy of Estimates of Step Frequency From a Wearable Gait Monitor

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