Literature DB >> 9299915

Assessment of posture and motion by multichannel piezoresistive accelerometer recordings.

J Fahrenberg1, F Foerster, M Smeja, W Müller.   

Abstract

Modern assessment of posture and motion involves the use of wide bandwidth piezoresistive accelerometers. The direct current (DC) component allows for assessment of slow motion and change in position referring to the gravitational axis: the alternating current (AC) component, calibrated in g. represents acceleration along the sensitive axis of the device. A method study was designed to evaluate the division in DC and AC components, reliabilities, discrimination between conditions, and detection of types of physical activities. Recordings were made in 26 student participants for eight conditions: sitting, standing, lying supine, sitting and typing on a PC keyboard, walking, climbing stairs, walking downstairs, and cycling. This procedure was repeated in reversed order. A classification of physical activities according to the eight conditions (first trial) and based on four parameters, that is DC components trunk, thigh, and lower leg and AC component trunk, was correct in almost 100% of patterns, when applied to the second trial.

Entities:  

Mesh:

Year:  1997        PMID: 9299915     DOI: 10.1111/j.1469-8986.1997.tb01747.x

Source DB:  PubMed          Journal:  Psychophysiology        ISSN: 0048-5772            Impact factor:   4.016


  15 in total

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2.  Detection of daily physical activities using a triaxial accelerometer.

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Journal:  Med Biol Eng Comput       Date:  2003-05       Impact factor: 2.602

3.  Classification of basic daily movements using a triaxial accelerometer.

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Review 4.  A review of signals used in sleep analysis.

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5.  Preliminary study: reliability of the spinal wheel. A novel device to measure spinal postures applied to sitting and standing.

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Review 6.  Position-sensing technologies for movement analysis in stroke rehabilitation.

Authors:  H Zheng; N D Black; N D Harris
Journal:  Med Biol Eng Comput       Date:  2005-07       Impact factor: 2.602

7.  Performance of Activity Classification Algorithms in Free-Living Older Adults.

Authors:  Jeffer Eidi Sasaki; Amanda M Hickey; John W Staudenmayer; Dinesh John; Jane A Kent; Patty S Freedson
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8.  Validation and Reliability of a Classification Method to Measure the Time Spent Performing Different Activities.

Authors:  Marie-Ève Riou; François Rioux; Gilles Lamothe; Éric Doucet
Journal:  PLoS One       Date:  2015-06-08       Impact factor: 3.240

9.  Feature fusion using deep learning for smartphone based human activity recognition.

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Journal:  Int J Inf Technol       Date:  2021-06-12

10.  Interactive Multimodal Ambulatory Monitoring to Investigate the Association between Physical Activity and Affect.

Authors:  U W Ebner-Priemer; S Koudela; G Mutz; M Kanning
Journal:  Front Psychol       Date:  2013-01-18
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