Literature DB >> 22255666

Capturing whole-body mobility of patients with Parkinson disease using inertial motion sensors: expected challenges and rewards.

Fariborz Rahimi, Christian Duval, Mandar Jog, Carina Bee, Angela South, Monica Jog, Roderick Edwards, Patrick Boissy.   

Abstract

While many studies have reported on the use of kinematic analysis on well-controlled, in-laboratory mobility tasks, few studies have examined the challenges of recording dynamic mobility in home environments. This preliminary study evaluated whole body mobility in eleven patients with Parkinson disease (H&Y 2-4). Patients were recorded in their home environment during scripted and non-scripted mobility tasks while wearing a full-body kinematic recording system using 11 inertial motion sensors (IMU). Data were analyzed with principal component analysis (PCA) in order to identify kinematic variables which best represent mobility tasks. Results indicate that there was a large degree of variability within subjects for each task, across tasks for individual subjects, and between scripted and non-scripted tasks. This study underscores the potential benefit of whole body multi-sensor kinematic recordings in understanding the variability in task performance across patients during daily activity which may have a significant impact on rehabilitation assessment and intervention.

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Year:  2011        PMID: 22255666     DOI: 10.1109/IEMBS.2011.6091443

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


  5 in total

Review 1.  How Wearable Sensors Can Support Parkinson's Disease Diagnosis and Treatment: A Systematic Review.

Authors:  Erika Rovini; Carlo Maremmani; Filippo Cavallo
Journal:  Front Neurosci       Date:  2017-10-06       Impact factor: 4.677

2.  Ecological validity of virtual reality daily living activities screening for early dementia: longitudinal study.

Authors:  Ioannis Tarnanas; Winfried Schlee; Magda Tsolaki; René Müri; Urs Mosimann; Tobias Nef
Journal:  JMIR Serious Games       Date:  2013-08-06       Impact factor: 4.143

3.  Auto detection and segmentation of physical activities during a Timed-Up-and-Go (TUG) task in healthy older adults using multiple inertial sensors.

Authors:  Hung P Nguyen; Fouaz Ayachi; Catherine Lavigne-Pelletier; Margaux Blamoutier; Fariborz Rahimi; Patrick Boissy; Mandar Jog; Christian Duval
Journal:  J Neuroeng Rehabil       Date:  2015-04-11       Impact factor: 4.262

4.  Performance of women with fibromyalgia in walking up stairs while carrying a load.

Authors:  Daniel Collado-Mateo; José C Adsuar; Pedro R Olivares; Francisco J Dominguez-Muñoz; Cristina Maestre-Cascales; Narcis Gusi
Journal:  PeerJ       Date:  2016-02-01       Impact factor: 2.984

Review 5.  Gait Recognition for Lower Limb Exoskeletons Based on Interactive Information Fusion.

Authors:  Wei Chen; Jun Li; Shanying Zhu; Xiaodong Zhang; Yutao Men; Hang Wu
Journal:  Appl Bionics Biomech       Date:  2022-03-26       Impact factor: 1.781

  5 in total

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