Literature DB >> 26868046

A novel algorithm for detecting active propulsion in wheelchair users following spinal cord injury.

Werner L Popp1, Michael Brogioli2, Kaspar Leuenberger3, Urs Albisser2, Angela Frotzler4, Armin Curt2, Roger Gassert3, Michelle L Starkey2.   

Abstract

Physical activity in wheelchair-bound individuals can be assessed by monitoring their mobility as this is one of the most intense upper extremity activities they perform. Current accelerometer-based approaches for describing wheelchair mobility do not distinguish between self- and attendant-propulsion and hence may overestimate total physical activity. The aim of this study was to develop and validate an inertial measurement unit based algorithm to monitor wheel kinematics and the type of wheelchair propulsion (self- or attendant-) within a "real-world" situation. Different sensor set-ups were investigated, ranging from a high precision set-up including four sensor modules with a relatively short measurement duration of 24 h, to a less precise set-up with only one module attached at the wheel exceeding one week of measurement because the gyroscope of the sensor was turned off. The "high-precision" algorithm distinguished self- and attendant-propulsion with accuracy greater than 93% whilst the long-term measurement set-up showed an accuracy of 82%. The estimation accuracy of kinematic parameters was greater than 97% for both set-ups. The possibility of having different sensor set-ups allows the use of the inertial measurement units as high precision tools for researchers as well as unobtrusive and simple tools for manual wheelchair users.
Copyright © 2016 IPEM. Published by Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Algorithm; Inertial measurement unit; Long-term; Machine learning; Monitoring; Propulsion; Spinal cord injury; Wheelchair

Mesh:

Year:  2016        PMID: 26868046     DOI: 10.1016/j.medengphy.2015.12.011

Source DB:  PubMed          Journal:  Med Eng Phys        ISSN: 1350-4533            Impact factor:   2.242


  16 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.  Predicting task performance from upper extremity impairment measures after cervical spinal cord injury.

Authors:  J Zariffa; A Curt; M C Verrier; M G Fehlings; S Kalsi-Ryan
Journal:  Spinal Cord       Date:  2016-05-31       Impact factor: 2.772

3.  Outcome Measures of Free-Living Activity in Spinal Cord Injury Rehabilitation.

Authors:  Brianna M Goodwin; Emma Fortune; Meegan G P Van Straaten; Melissa M B Morrow
Journal:  Curr Phys Med Rehabil Rep       Date:  2019-05-28

4.  Monitoring Upper Limb Recovery after Cervical Spinal Cord Injury: Insights beyond Assessment Scores.

Authors:  Michael Brogioli; Sophie Schneider; Werner L Popp; Urs Albisser; Anne K Brust; Inge-Marie Velstra; Roger Gassert; Armin Curt; Michelle L Starkey
Journal:  Front Neurol       Date:  2016-08-31       Impact factor: 4.003

Review 5.  Measurement of Physical Activity and Energy Expenditure in Wheelchair Users: Methods, Considerations and Future Directions.

Authors:  Tom E Nightingale; Peter C Rouse; Dylan Thompson; James L J Bilzon
Journal:  Sports Med Open       Date:  2017-03-01

6.  Profiling walking dysfunction in multiple sclerosis: characterisation, classification and progression over time.

Authors:  Linard Filli; Tabea Sutter; Christopher S Easthope; Tim Killeen; Christian Meyer; Katja Reuter; Lilla Lörincz; Marc Bolliger; Michael Weller; Armin Curt; Dominik Straumann; Michael Linnebank; Björn Zörner
Journal:  Sci Rep       Date:  2018-03-21       Impact factor: 4.379

7.  Protocol of a systematic review on the application of wearable inertial sensors to quantify everyday life motor activity in people with mobility impairments.

Authors:  Fabian Marcel Rast; Rob Labruyère
Journal:  Syst Rev       Date:  2018-10-24

8.  Estimation of manual wheelchair-based activities in the free-living environment using a neural network model with inertial body-worn sensors.

Authors:  Emma Fortune; Beth A Cloud-Biebl; Stefan I Madansingh; Che G Ngufor; Meegan G Van Straaten; Brianna M Goodwin; Dennis H Murphree; Kristin D Zhao; Melissa M Morrow
Journal:  J Electromyogr Kinesiol       Date:  2019-07-17       Impact factor: 2.368

9.  Sudden stop detection and automatic seating support with neural stimulation during manual wheelchair propulsion.

Authors:  Kevin M Foglyano; Lisa M Lombardo; John R Schnellenberger; Ronald J Triolo
Journal:  J Spinal Cord Med       Date:  2020-08-14       Impact factor: 1.985

10.  Estimation of Energy Expenditure in Wheelchair-Bound Spinal Cord Injured Individuals Using Inertial Measurement Units.

Authors:  Werner L Popp; Lea Richner; Michael Brogioli; Britta Wilms; Christina M Spengler; Armin E P Curt; Michelle L Starkey; Roger Gassert
Journal:  Front Neurol       Date:  2018-07-03       Impact factor: 4.003

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