Literature DB >> 25570890

The variable relationship between arm and hand use: a rationale for using finger magnetometry to complement wrist accelerometry when measuring daily use of the upper extremity.

Justin B Rowe, Nizan Friedman, Vicky Chan, Steven C Cramer, Mark Bachman, David J Reinkensmeyer.   

Abstract

Wrist-worn accelerometers are becoming more prevalent as a means to assess use of the impaired upper extremity in daily life after stroke. However, wrist accelerometry does not measure joint movements of the hand, which are integral to functional use of the upper extremity. In this study, we used a custom-built, non-obtrusive device called the manumeter to measure both arm use (via wrist accelerometry) and hand use (via finger magnetometry) of a group of unimpaired subjects while they performed twelve motor tasks at three intensities. We also gave the devices to four stroke subjects and asked them to wear them for six hours a day for one month. From the in-lab testing we found that arm use was a strong predictor of hand use for individual tasks, but that the slope of the relationship varied by up to a factor of ~12 depending on the task being performed. Consistent with this, in the daily use data collected from stroke subjects we found a broad spread in the relationship between arm and hand use. These results suggest that analyzing the spread of the relationship between daily hand and arm use will give more insight into upper extremity recovery than wrist accelerometry or finger magnetometry alone, because the spread reflects the nature of the daily tasks performed as well as the amount of upper extremity use.

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Year:  2014        PMID: 25570890     DOI: 10.1109/EMBC.2014.6944522

Source DB:  PubMed          Journal:  Conf Proc IEEE Eng Med Biol Soc        ISSN: 1557-170X


  11 in total

1.  Views of individuals with spinal cord injury on the use of wearable cameras to monitor upper limb function in the home and community.

Authors:  Jirapat Likitlersuang; Elizabeth R Sumitro; Pirashanth Theventhiran; Sukhvinder Kalsi-Ryan; José Zariffa
Journal:  J Spinal Cord Med       Date:  2017-07-24       Impact factor: 1.985

2.  Upper Limb Performance in Daily Life Approaches Plateau Around Three to Six Weeks Post-stroke.

Authors:  Catherine E Lang; Kimberly J Waddell; Jessica Barth; Carey L Holleran; Michael J Strube; Marghuretta D Bland
Journal:  Neurorehabil Neural Repair       Date:  2021-10       Impact factor: 4.895

3.  Envisioning the use of in-situ arm movement data in stroke rehabilitation: Stroke survivors' and occupational therapists' perspectives.

Authors:  Hee-Tae Jung; Yoojung Kim; Juhyeon Lee; Sunghoon Ivan Lee; Eun Kyoung Choe
Journal:  PLoS One       Date:  2022-10-20       Impact factor: 3.752

4.  Identifying Hand Use and Hand Roles After Stroke Using Egocentric Video.

Authors:  Meng-Fen Tsai; Rosalie H Wang; Jose Zariffa
Journal:  IEEE J Transl Eng Health Med       Date:  2021-04-09       Impact factor: 3.316

5.  Does Task-Specific Training Improve Upper Limb Performance in Daily Life Poststroke?

Authors:  Kimberly J Waddell; Michael J Strube; Ryan R Bailey; Joseph W Klaesner; Rebecca L Birkenmeier; Alexander W Dromerick; Catherine E Lang
Journal:  Neurorehabil Neural Repair       Date:  2016-12-13       Impact factor: 4.895

6.  The Reality of Myoelectric Prostheses: Understanding What Makes These Devices Difficult for Some Users to Control.

Authors:  Alix Chadwell; Laurence Kenney; Sibylle Thies; Adam Galpin; John Head
Journal:  Front Neurorobot       Date:  2016-08-22       Impact factor: 2.650

7.  How Therapists Use Visualizations of Upper Limb Movement Information From Stroke Patients: A Qualitative Study With Simulated Information.

Authors:  Bernd Ploderer; Justin Fong; Marlena Klaic; Siddharth Nair; Frank Vetere; L Eduardo Cofré Lizama; Mary Pauline Galea
Journal:  JMIR Rehabil Assist Technol       Date:  2016-10-05

8.  Thumb and finger movement is reduced after stroke: An observational study.

Authors:  Helleana Eschmann; Martin E Héroux; James H Cheetham; Stephanie Potts; Joanna Diong
Journal:  PLoS One       Date:  2019-06-12       Impact factor: 3.240

9.  Recognizing Manual Activities Using Wearable Inertial Measurement Units: Clinical Application for Outcome Measurement.

Authors:  Ghady El Khoury; Massimo Penta; Olivier Barbier; Xavier Libouton; Jean-Louis Thonnard; Philippe Lefèvre
Journal:  Sensors (Basel)       Date:  2021-05-07       Impact factor: 3.576

Review 10.  Computational neurorehabilitation: modeling plasticity and learning to predict recovery.

Authors:  David J Reinkensmeyer; Etienne Burdet; Maura Casadio; John W Krakauer; Gert Kwakkel; Catherine E Lang; Stephan P Swinnen; Nick S Ward; Nicolas Schweighofer
Journal:  J Neuroeng Rehabil       Date:  2016-04-30       Impact factor: 5.208

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