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Related Concept Videos

Calculating Standard Deviation01:08

Calculating Standard Deviation

The standard deviation is the most common measure of variation. It is a value that tells us how far a data value is from the mean value in a dataset. Further, the standard deviation is always a positive value or zero.
The standard deviation value is small when all the data is concentrated close to the mean. Here the data exhibits low variation. The standard deviation value is larger when the data values are more spread out from the mean. Here, the data displays high variation.       
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A Method for Quantifying Upper Limb Performance in Daily Life Using Accelerometers
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Moving Standard Deviation of Trunk Acceleration as a Quantification Index for Physical Activities: Validation Study.

Takuya Suzuki1, Yuji Kono1, Takayuki Ogasawara2

  • 1Department of Rehabilitation, Fujita Health University Hospital, Toyoake, Japan.

JMIR Formative Research
|April 8, 2025
PubMed
Summary

Moving standard deviation of acceleration (MSDA) effectively quantifies physical activity in stroke patients, including wheelchair users. This method overcomes limitations of step counts for individuals with impaired mobility.

Keywords:
MSDAaccelerometeractivity quantificationaginghemiparesismeasurement systemmobilitymotor impairmentsmoving standard deviation of accelerationolder peoplephysical activitiesregular gait patternsrehabilitationsmart clothingstep countstep detectionstrokevalidation studywalkingwheelchair

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Area of Science:

  • Biomedical Engineering
  • Rehabilitation Science
  • Wearable Technology

Background:

  • Step count, a common physical activity metric, faces challenges with irregular gait and is inapplicable to wheelchair users.
  • Existing device-specific measures lack cross-applicability.
  • Moving standard deviation of acceleration (MSDA) from trunk movements is proposed as a universal physical activity metric.

Purpose of the Study:

  • To evaluate the validity and feasibility of MSDA for quantifying physical activity in stroke patients.
  • To compare MSDA with traditional step count in individuals with varying mobility levels.

Main Methods:

  • 197 stroke hemiparesis patients were enrolled.
  • Trunk movement MSDA and step count were measured using the hitoe smart clothing system.
  • Participants were categorized by Functional Independence Measure (FIM) mobility levels (walking vs. wheelchair use).

Main Results:

  • A strong correlation (r=0.78) was found between MSDA and step count, robust across walking and wheelchair groups.
  • MSDA showed a normal distribution across mobility subgroups, unlike step count which exhibited a floor effect in wheelchair users.
  • Median MSDA values correlated with FIM mobility scores, reflecting functional independence.

Conclusions:

  • MSDA is a valid and feasible measure of physical activity for stroke patients, regardless of mobility status.
  • MSDA demonstrates broad utility in rehabilitation, particularly for individuals with motor impairments like wheelchair users.
  • This metric offers a more inclusive approach to physical activity assessment compared to step count.