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Step detection and activity recognition accuracy of seven physical activity monitors.

Storm FA, Heller BW, Mazzà C - PLoS ONE (2015)

Bottom Line: The Movemonitor, One, ActivPAL, Nike+ Fuelband and Sensewear Armband Mini underestimated the number of steps in all the observed walking speeds, whereas the Tractivity significantly overestimated step count.The Movemonitor was the best performing sensor, with an error lower than 2% at all speeds and the smallest error obtained in the outdoor walking.Results of this study can be used to inform choice of a monitor for specific applications.

View Article: PubMed Central - PubMed

Affiliation: Department of Mechanical Engineering, University of Sheffield, Sheffield, United Kingdom; INSIGNEO Institute for in silico Medicine, University of Sheffield, Sheffield, United Kingdom.

ABSTRACT
The aim of this study was to compare the seven following commercially available activity monitors in terms of step count detection accuracy: Movemonitor (Mc Roberts), Up (Jawbone), One (Fitbit), ActivPAL (PAL Technologies Ltd.), Nike+ Fuelband (Nike Inc.), Tractivity (Kineteks Corp.) and Sensewear Armband Mini (Bodymedia). Sixteen healthy adults consented to take part in the study. The experimental protocol included walking along an indoor straight walkway, descending and ascending 24 steps, free outdoor walking and free indoor walking. These tasks were repeated at three self-selected walking speeds. Angular velocity signals collected at both shanks using two wireless inertial measurement units (OPAL, ADPM Inc) were used as a reference for the step count, computed using previously validated algorithms. Step detection accuracy was assessed using the mean absolute percentage error computed for each sensor. The Movemonitor and the ActivPAL were also tested within a nine-minute activity recognition protocol, during which the participants performed a set of complex tasks. Posture classifications were obtained from the two monitors and expressed as a percentage of the total task duration. The Movemonitor, One, ActivPAL, Nike+ Fuelband and Sensewear Armband Mini underestimated the number of steps in all the observed walking speeds, whereas the Tractivity significantly overestimated step count. The Movemonitor was the best performing sensor, with an error lower than 2% at all speeds and the smallest error obtained in the outdoor walking. The activity recognition protocol showed that the Movemonitor performed best in the walking recognition, but had difficulty in discriminating between standing and sitting. Results of this study can be used to inform choice of a monitor for specific applications.

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Related in: MedlinePlus

Summary of MPE for the 7 PAMs included in the study.The figure shows the mean percentage error (MPE) during slow, self-selected and fast walking speed trials for all the sensors included in the study. Error bars are mean ± SD.
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pone.0118723.g003: Summary of MPE for the 7 PAMs included in the study.The figure shows the mean percentage error (MPE) during slow, self-selected and fast walking speed trials for all the sensors included in the study. Error bars are mean ± SD.

Mentions: There was a significant underestimation of Ñ for the Movemonitor, One, ActivPAL, Nike+ Fuelband and Sensewear Armband Mini, whereas the Tractivity significantly overestimated step count. The observed power was 0.999 for the overall ANOVA and ranged from 0.833 to 0.999 for the significantly different contrast tests. The Up did not show any systematic over- or underestimation. These findings were confirmed also when the data were separated by walking speed. Fig. 3 summarises mean and SD between individuals of the mean percentage error (MPE) at all walking speeds for each of the seven PAMs. The best performing device in terms of MPE was the Movemonitor, with MPE<2.0% at every speed, followed by One and ActivPAL, with MPE <2.6% and <3.2%, respectively. These three sensors presented also the smallest SD (≤1.7%, ≤2.5% and ≤1.5%, respectively). See the supporting information for the total number of steps (S1 Table) and MPE (S2 Table) for all the sensors included in the study.


Step detection and activity recognition accuracy of seven physical activity monitors.

Storm FA, Heller BW, Mazzà C - PLoS ONE (2015)

Summary of MPE for the 7 PAMs included in the study.The figure shows the mean percentage error (MPE) during slow, self-selected and fast walking speed trials for all the sensors included in the study. Error bars are mean ± SD.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4366111&req=5

pone.0118723.g003: Summary of MPE for the 7 PAMs included in the study.The figure shows the mean percentage error (MPE) during slow, self-selected and fast walking speed trials for all the sensors included in the study. Error bars are mean ± SD.
Mentions: There was a significant underestimation of Ñ for the Movemonitor, One, ActivPAL, Nike+ Fuelband and Sensewear Armband Mini, whereas the Tractivity significantly overestimated step count. The observed power was 0.999 for the overall ANOVA and ranged from 0.833 to 0.999 for the significantly different contrast tests. The Up did not show any systematic over- or underestimation. These findings were confirmed also when the data were separated by walking speed. Fig. 3 summarises mean and SD between individuals of the mean percentage error (MPE) at all walking speeds for each of the seven PAMs. The best performing device in terms of MPE was the Movemonitor, with MPE<2.0% at every speed, followed by One and ActivPAL, with MPE <2.6% and <3.2%, respectively. These three sensors presented also the smallest SD (≤1.7%, ≤2.5% and ≤1.5%, respectively). See the supporting information for the total number of steps (S1 Table) and MPE (S2 Table) for all the sensors included in the study.

Bottom Line: The Movemonitor, One, ActivPAL, Nike+ Fuelband and Sensewear Armband Mini underestimated the number of steps in all the observed walking speeds, whereas the Tractivity significantly overestimated step count.The Movemonitor was the best performing sensor, with an error lower than 2% at all speeds and the smallest error obtained in the outdoor walking.Results of this study can be used to inform choice of a monitor for specific applications.

View Article: PubMed Central - PubMed

Affiliation: Department of Mechanical Engineering, University of Sheffield, Sheffield, United Kingdom; INSIGNEO Institute for in silico Medicine, University of Sheffield, Sheffield, United Kingdom.

ABSTRACT
The aim of this study was to compare the seven following commercially available activity monitors in terms of step count detection accuracy: Movemonitor (Mc Roberts), Up (Jawbone), One (Fitbit), ActivPAL (PAL Technologies Ltd.), Nike+ Fuelband (Nike Inc.), Tractivity (Kineteks Corp.) and Sensewear Armband Mini (Bodymedia). Sixteen healthy adults consented to take part in the study. The experimental protocol included walking along an indoor straight walkway, descending and ascending 24 steps, free outdoor walking and free indoor walking. These tasks were repeated at three self-selected walking speeds. Angular velocity signals collected at both shanks using two wireless inertial measurement units (OPAL, ADPM Inc) were used as a reference for the step count, computed using previously validated algorithms. Step detection accuracy was assessed using the mean absolute percentage error computed for each sensor. The Movemonitor and the ActivPAL were also tested within a nine-minute activity recognition protocol, during which the participants performed a set of complex tasks. Posture classifications were obtained from the two monitors and expressed as a percentage of the total task duration. The Movemonitor, One, ActivPAL, Nike+ Fuelband and Sensewear Armband Mini underestimated the number of steps in all the observed walking speeds, whereas the Tractivity significantly overestimated step count. The Movemonitor was the best performing sensor, with an error lower than 2% at all speeds and the smallest error obtained in the outdoor walking. The activity recognition protocol showed that the Movemonitor performed best in the walking recognition, but had difficulty in discriminating between standing and sitting. Results of this study can be used to inform choice of a monitor for specific applications.

Show MeSH
Related in: MedlinePlus