Use of Accelerometry to Classify Activity Beneficial to Bone in Premenopausal Women

Stiles, Victoria; Griew, Pippa; Rowlands, Alex V.

doi:10.1249/mss.0b013e31829ba765

Cited by 36 publications

(47 citation statements)

References 21 publications

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“…Body-worn accelerometers are accurate and useful tools for tracking physical activity, 3 and may provide the means to indirectly assess GRF and LR in the field. A small number of studies have shown positive correlations of peak accelerations, measured with body-worn accelerometers, with peak vertical and resultant GRF and LR in both adults 4–6 and children. 7 However, these studies used accelerometers placed on the hip, thigh, tibia, and/or wrist but did not investigate the relationship of ankle accelerations with peak GRF and LR.…”

Section: Introductionmentioning

confidence: 99%

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Assessment of Gait Kinetics Using Triaxial Accelerometers

Fortune¹,

Morrow²,

Kaufman³

2014

Journal of Applied Biomechanics

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Repeated durations of dynamic activity with high ground reaction forces (GRFs) and loading rates (LRs) can be beneficial to bone health. To fully characterize dynamic activity in relation to bone health, field-based measurements of gait kinetics are desirable to assess free-living lower-extremity loading. The study aims were to determine correlations of peak vertical GRF and peak vertical LR with ankle peak vertical accelerations, and of peak resultant GRF and peak resultant LR with ankle peak resultant accelerations and to compare them to correlations with tibia, thigh, and waist accelerations. GRF data were collected as ten healthy subjects (26 (19–34) years) performed 8–10 walking trials at velocities ranging from 0.19–3.05 m/s, wearing ankle, tibia, thigh, and waist accelerometers. While peak vertical accelerations of all locations were positively correlated with peak vertical GRF and LR (r2>0.53, P<0.001), ankle peak vertical accelerations were the most correlated (r2>0.75, P<0.001). All peak resultant accelerations were positively correlated with peak resultant GRF and LR (r2>0.57, P<0.001) with waist peak resultant acceleration being the most correlated (r2>0.70, P<0.001). The results suggest that ankle or waist accelerometers give the most accurate peak GRF and LR estimates and could be useful tools in relating physical activity to bone health.

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Section: Introductionmentioning

confidence: 99%

“…Previous studies have not investigated a wide range of gait velocities or gait velocities less than 0.67 m/s. 4–6,9,10 Normal gait velocities of less than 0.69 m/s have been characterized as a risk factor for fractures. 11 …”

Section: Introductionmentioning

confidence: 99%

Assessment of Gait Kinetics Using Triaxial Accelerometers

Fortune¹,

Morrow²,

Kaufman³

2014

Journal of Applied Biomechanics

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“…The method used to aggregate the data combines the ideas behind SMA and the differential signal vector magnitude (DSVM) method [9]. The equation, which can be referred to as the differential signal magnitude (DSM) method, is shown in (1).…”

Section: Discussionmentioning

confidence: 99%

“…Stiles et al [9] studied the use of accelerometers worn at either the hip or wrist and found either location can be used to classify activity that is beneficial to bone in premenopausal women. Zhang et al [10] experimented with various sampling frequencies and number of axes when using accelerometer data from the wrist to classify activity level in adults (average age 49.4).…”

Section: Introductionmentioning

confidence: 99%

Wrist-Gathered Acceleration Data and their Correlation with Physical Activity in the Elderly

Papadopoulos¹,

Vivaldi

Crump³

et al. 2014

IJBSE

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Estimating physical activity in the elderly from wrist-gathered acceleration data was studied. Thirty individuals (65+ years) were video-recorded while wearing a wrist device and going about their normal activities within their regular living environment for four hours each. Acceleration data were summarized into an activity value [via the "differential signal magnitude" (DSM) method] and compared to metabolic equivalent of task (MET) values determined by video analysis for each time period ("epoch"). Different sampling rates and epoch sizes were evaluated. Sampling at 4 Hz and using 60-second epochs provided the best results, with a moderate Pearson's correlation coefficient of 0.58 between DSM activity values and MET values. The area under the receiver operating characteristic curve (AUC) for classifying each minute of data as active (MET >= 2.0) versus moderately active (MET > 1.2 and < 2.0) was 0.87 (sensitivity 80%, specificity 79%). DSM activity values (sampling at 4 Hz) were compared to the widely known signal magnitude area (SMA) values (requiring low-pass filtering and sampling at 40 Hz), with an excellent correlation of 0.994.

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“…Data regarding exercise motions such as standing triceps extension with a dumbbell and wide-grip bench press with a barbell were obtained using sensors worn on the forearm [9]. Another study classified the daily activities that are beneficial to the bones of premenopausal women [10]. A previous study also classified posture and motion with four accelerometer sensors at sternum, wrist, thigh, and lower leg [11].…”

Section: Introductionmentioning

confidence: 99%

Recognition of Daily Human Activity Using an Artificial Neural Network and Smartwatch

Kwon

Choi

2018

Wireless Communications and Mobile Computing

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Human activity recognition using wearable devices has been actively investigated in a wide range of applications. Most of them, however, either focus on simple activities wherein whole body movement is involved or require a variety of sensors to identify daily activities. In this study, we propose a human activity recognition system that collects data from an off-the-shelf smartwatch and uses an artificial neural network for classification. The proposed system is further enhanced using location information. We consider 11 activities, including both simple and daily activities. Experimental results show that various activities can be classified with an accuracy of 95%.

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Use of Accelerometry to Classify Activity Beneficial to Bone in Premenopausal Women

Abstract: GENEActiv and ActiGraph GT3X+ accelerometers worn at the wrist or hip can be used as an unobtrusive tool to identify the occurrence of loading rates likely beneficial to bone in premenopausal women during their daily activity.

Cited by 36 publications

References 21 publications

Assessment of Gait Kinetics Using Triaxial Accelerometers

Assessment of Gait Kinetics Using Triaxial Accelerometers

Wrist-Gathered Acceleration Data and their Correlation with Physical Activity in the Elderly

Recognition of Daily Human Activity Using an Artificial Neural Network and Smartwatch

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