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Monitoring functional capability of individuals with lower limb amputations using mobile phones.

Albert MV, McCarthy C, Valentin J, Herrmann M, Kording K, Jayaraman A - PLoS ONE (2013)

Bottom Line: To be effective, a prescribed prosthetic device must match the functional requirements and capabilities of each patient.Here, we quantify participant activity using mobile phones and relate activity measured during real world activity to the assigned K-levels.We observe a correlation between K-level and the proportion of moderate to high activity over the course of a week.

View Article: PubMed Central - PubMed

Affiliation: Sensory Motor Performance Program, Rehabilitation Institute of Chicago, Chicago, Illinois, USA. markvalbert@gmail.com

ABSTRACT
To be effective, a prescribed prosthetic device must match the functional requirements and capabilities of each patient. These capabilities are usually assessed by a clinician and reported by the Medicare K-level designation of mobility. However, it is not clear how the K-level designation objectively relates to the use of prostheses outside of a clinical environment. Here, we quantify participant activity using mobile phones and relate activity measured during real world activity to the assigned K-levels. We observe a correlation between K-level and the proportion of moderate to high activity over the course of a week. This relationship suggests that accelerometry-based technologies such as mobile phones can be used to evaluate real world activity for mobility assessment. Quantifying everyday activity promises to improve assessment of real world prosthesis use, leading to a better matching of prostheses to individuals and enabling better evaluations of future prosthetic devices.

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Related in: MedlinePlus

Schematic of data analysis.A) Example data acquired from normal cell phone use, recorded for this illustration. B) 10 second segments extracted from part A. The labels are only used for interpretation. C) The clips were then placed on a scale by their averaged standard deviations of the accelerations for each axis and binned appropriately. Colors are associated with each bin of activity. Example activities are given for each bin when the phone is worn on the belt. D) Proportions in those bins when including inactive data. E) Proportions when excluding inactive data–used to exclude all times when the phone is not worn or the subject is not moving.
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pone-0065340-g002: Schematic of data analysis.A) Example data acquired from normal cell phone use, recorded for this illustration. B) 10 second segments extracted from part A. The labels are only used for interpretation. C) The clips were then placed on a scale by their averaged standard deviations of the accelerations for each axis and binned appropriately. Colors are associated with each bin of activity. Example activities are given for each bin when the phone is worn on the belt. D) Proportions in those bins when including inactive data. E) Proportions when excluding inactive data–used to exclude all times when the phone is not worn or the subject is not moving.

Mentions: The week-long accelerometer recordings are distilled into a general measure of activity for each participant (fig. 2). Different activities led to distinct acceleration patterns. These patterns were scored based on the measured movement of the device (see methods). The amount of movement, as measured by changes in acceleration on the phone, is indicative of the types of activities participants are engaged in. We observe the general amount of activity by observing the fraction of time spent at each of these levels of activity.


Monitoring functional capability of individuals with lower limb amputations using mobile phones.

Albert MV, McCarthy C, Valentin J, Herrmann M, Kording K, Jayaraman A - PLoS ONE (2013)

Schematic of data analysis.A) Example data acquired from normal cell phone use, recorded for this illustration. B) 10 second segments extracted from part A. The labels are only used for interpretation. C) The clips were then placed on a scale by their averaged standard deviations of the accelerations for each axis and binned appropriately. Colors are associated with each bin of activity. Example activities are given for each bin when the phone is worn on the belt. D) Proportions in those bins when including inactive data. E) Proportions when excluding inactive data–used to exclude all times when the phone is not worn or the subject is not moving.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3672103&req=5

pone-0065340-g002: Schematic of data analysis.A) Example data acquired from normal cell phone use, recorded for this illustration. B) 10 second segments extracted from part A. The labels are only used for interpretation. C) The clips were then placed on a scale by their averaged standard deviations of the accelerations for each axis and binned appropriately. Colors are associated with each bin of activity. Example activities are given for each bin when the phone is worn on the belt. D) Proportions in those bins when including inactive data. E) Proportions when excluding inactive data–used to exclude all times when the phone is not worn or the subject is not moving.
Mentions: The week-long accelerometer recordings are distilled into a general measure of activity for each participant (fig. 2). Different activities led to distinct acceleration patterns. These patterns were scored based on the measured movement of the device (see methods). The amount of movement, as measured by changes in acceleration on the phone, is indicative of the types of activities participants are engaged in. We observe the general amount of activity by observing the fraction of time spent at each of these levels of activity.

Bottom Line: To be effective, a prescribed prosthetic device must match the functional requirements and capabilities of each patient.Here, we quantify participant activity using mobile phones and relate activity measured during real world activity to the assigned K-levels.We observe a correlation between K-level and the proportion of moderate to high activity over the course of a week.

View Article: PubMed Central - PubMed

Affiliation: Sensory Motor Performance Program, Rehabilitation Institute of Chicago, Chicago, Illinois, USA. markvalbert@gmail.com

ABSTRACT
To be effective, a prescribed prosthetic device must match the functional requirements and capabilities of each patient. These capabilities are usually assessed by a clinician and reported by the Medicare K-level designation of mobility. However, it is not clear how the K-level designation objectively relates to the use of prostheses outside of a clinical environment. Here, we quantify participant activity using mobile phones and relate activity measured during real world activity to the assigned K-levels. We observe a correlation between K-level and the proportion of moderate to high activity over the course of a week. This relationship suggests that accelerometry-based technologies such as mobile phones can be used to evaluate real world activity for mobility assessment. Quantifying everyday activity promises to improve assessment of real world prosthesis use, leading to a better matching of prostheses to individuals and enabling better evaluations of future prosthetic devices.

Show MeSH
Related in: MedlinePlus