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A Context-Aware Application to Increase Elderly Users Compliance with Physical Rehabilitation Exercises at Home via Animatronic Biofeedback

Overview of attention for article published in Journal of Medical Systems, August 2015
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1 tweeter

Citations

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13 Dimensions

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129 Mendeley
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Title
A Context-Aware Application to Increase Elderly Users Compliance with Physical Rehabilitation Exercises at Home via Animatronic Biofeedback
Published in
Journal of Medical Systems, August 2015
DOI 10.1007/s10916-015-0296-1
Pubmed ID
Authors

Borja Gamecho, Hugo Silva, José Guerreiro, Luis Gardeazabal, Julio Abascal

Abstract

Biofeedback from physical rehabilitation exercises has proved to lead to faster recovery, better outcomes, and increased patient motivation. In addition, it allows the physical rehabilitation processes carried out at the clinic to be complemented with exercises performed at home. However, currently existing approaches rely mostly on audio and visual reinforcement cues, usually presented to the user on a computer screen or a mobile phone interface. Some users, such as elderly people, can experience difficulties to use and understand these interfaces, leading to non-compliance with the rehabilitation exercises. To overcome this barrier, latest biosignal technologies can be used to enhance the efficacy of the biofeedback, decreasing the complexity of the user interface. In this paper we propose and validate a context-aware framework for the use of animatronic biofeedback, as a way of potentially increasing the compliance of elderly users with physical rehabilitation exercises performed at home. In the scope of our work, animatronic biofeedback entails the use of pre-programmed actions on a robot that are triggered in response to certain changes detected in the users biomechanical or electrophysiological signals. We use electromyographic and accelerometer signals, collected in real time, to monitor the performance of the user while executing the exercises, and a mobile robot to provide animatronic reinforcement cues associated with their correct or incorrect execution. A context-aware application running on a smartphone aggregates the sensor data and controls the animatronic feedback. The acceptability of the animatronic biofeedback has been tested on a set of volunteer elderly users, and results suggest that the participants found the animatronic feedback engaging and of added value.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 129 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Spain 2 2%
Brazil 1 <1%
Netherlands 1 <1%
Switzerland 1 <1%
United Kingdom 1 <1%
Canada 1 <1%
Germany 1 <1%
Unknown 121 94%

Demographic breakdown

Readers by professional status Count As %
Student > Master 27 21%
Student > Ph. D. Student 20 16%
Student > Bachelor 17 13%
Researcher 12 9%
Professor 7 5%
Other 25 19%
Unknown 21 16%
Readers by discipline Count As %
Nursing and Health Professions 21 16%
Medicine and Dentistry 20 16%
Computer Science 13 10%
Engineering 11 9%
Social Sciences 9 7%
Other 23 18%
Unknown 32 25%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 03 June 2016.
All research outputs
#7,695,650
of 12,316,589 outputs
Outputs from Journal of Medical Systems
#359
of 667 outputs
Outputs of similar age
#152,171
of 277,796 outputs
Outputs of similar age from Journal of Medical Systems
#12
of 25 outputs
Altmetric has tracked 12,316,589 research outputs across all sources so far. This one is in the 23rd percentile – i.e., 23% of other outputs scored the same or lower than it.
So far Altmetric has tracked 667 research outputs from this source. They receive a mean Attention Score of 3.2. This one is in the 34th percentile – i.e., 34% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 277,796 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 35th percentile – i.e., 35% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 25 others from the same source and published within six weeks on either side of this one. This one is in the 36th percentile – i.e., 36% of its contemporaries scored the same or lower than it.