Literature DB >> 21096303

Tracking motor recovery in stroke survivors undergoing rehabilitation using wearable technology.

Shyamal Patel1, Richard Hughes, Todd Hester, Joel Stein, Metin Akay, Jennifer Dy, Paolo Bonato.   

Abstract

Quantitative assessment of motor abilities in stroke survivors undergoing rehabilitation can be a valuable feedback to guide the rehabilitation process. The Functional Ability Scale (FAS) part of Wolf Motor Function Test (WMFT) is used to evaluate movement quality during performance of a set of functional motor tasks. In this paper, we show that information collected using body worn sensors such as accelerometers during performance of functional motor tasks by stroke survivors can be used to build accurate classifiers of FAS scores for individual tasks. We perform feature selection to improve classification accuracy and show that it is possible to estimate the total FAS score from a subset of functional motor tasks taken from the WMFT.

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Year:  2010        PMID: 21096303     DOI: 10.1109/IEMBS.2010.5626446

Source DB:  PubMed          Journal:  Annu Int Conf IEEE Eng Med Biol Soc        ISSN: 2375-7477


  17 in total

1.  Minimal detectable change of the actual amount of use test and the motor activity log: the EXCITE Trial.

Authors:  Shuya Chen; Steven L Wolf; Qin Zhang; Paul A Thompson; Carolee J Winstein
Journal:  Neurorehabil Neural Repair       Date:  2012-01-24       Impact factor: 3.919

Review 2.  What is the role of brain mechanisms underlying arousal in recovery of motor function after structural brain injuries?

Authors:  Andrew M Goldfine; Nicholas D Schiff
Journal:  Curr Opin Neurol       Date:  2011-12       Impact factor: 5.710

3.  A novel fuzzy approach for automatic Brunnstrom stage classification using surface electromyography.

Authors:  Luca Liparulo; Zhe Zhang; Massimo Panella; Xudong Gu; Qiang Fang
Journal:  Med Biol Eng Comput       Date:  2016-12-01       Impact factor: 2.602

Review 4.  A review of wearable technology in medicine.

Authors:  Mohammed H Iqbal; Abdullatif Aydin; Oliver Brunckhorst; Prokar Dasgupta; Kamran Ahmed
Journal:  J R Soc Med       Date:  2016-10       Impact factor: 5.344

Review 5.  The promise of mHealth: daily activity monitoring and outcome assessments by wearable sensors.

Authors:  Bruce H Dobkin; Andrew Dorsch
Journal:  Neurorehabil Neural Repair       Date:  2011 Nov-Dec       Impact factor: 3.919

6.  Quantifying Poststroke Apathy With Actimeters.

Authors:  Andrew M Goldfine; Behdad Dehbandi; Juliana M Kennedy; Briana Sabot; Cory Semper; David Putrino
Journal:  J Neuropsychiatry Clin Neurosci       Date:  2016-02-22       Impact factor: 2.198

7.  Machine Learning and Mobile Health Monitoring Platforms: A Case Study on Research and Implementation Challenges.

Authors:  Omar Boursalie; Reza Samavi; Thomas E Doyle
Journal:  J Healthc Inform Res       Date:  2018-05-22

8.  Prediction of responders for outcome measures of locomotor Experience Applied Post Stroke trial.

Authors:  Bruce H K Dobkin; Stephen E Nadeau; Andrea L Behrman; Samuel S Wu; Dorian K Rose; Mark Bowden; Stephanie Studenski; Xiaomin Lu; Pamela W Duncan
Journal:  J Rehabil Res Dev       Date:  2014

9.  Quantitative assessment of lower limbs gross motor function in children with cerebral palsy based on surface EMG and inertial sensors.

Authors:  Xiang Chen; Qi Wu; Lu Tang; Shuai Cao; Xu Zhang; Xun Chen
Journal:  Med Biol Eng Comput       Date:  2019-11-21       Impact factor: 2.602

Review 10.  Wearable motion sensors to continuously measure real-world physical activities.

Authors:  Bruce H Dobkin
Journal:  Curr Opin Neurol       Date:  2013-12       Impact factor: 5.710

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