Gait assessment in Parkinson's disease: toward an ambulatory system for long-term monitoring

By Arash Salarian, Heike Russmann, François J. G. Vingerhoets, Catherine Dehollain, Yves Blanc, Pierre R. Burkhard, Kamiar Aminian. IEEE Transactions on Biomedical Engineering, 2004

658 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Neurology

View Paper: https://www.semanticscholar.org/paper/a89317cf1a1aab47495c4e15b9dcf9a516151398

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In Biomedical Engineering

In Neurology

Fay B. Horak

Citations in the corpus

The relevance of clinical balance assessment tools to differentiate balance deficits (2010, positive): “We demonstrated how a Physilog® portable data-logger (Salarian et al., 2004) with 7 inertial sensors (on chest, forearms, thighs and shanks) could quantify an extended, 6-meter, Get-Up-and-Go task to automatically identify postural transitions (sit-to-stand, turning, stand-to-sit) as well as gait pa”
A review of wearable sensors and systems with application in rehabilitation (2012, positive): “Based on this body of work, an ambulatory gait analysis system, based on wearable accelerometers, for patients with PD has been proposed by Salarian et al. [105].”
Gait Analysis Methods: An Overview of Wearable and Non-Wearable Systems, Highlighting Clinical Applications (2014, neutral): “In diseases where gait disorders are a symptom such as Parkinson’s, we find several applications of sensors of this type [59]: Tay et al.”
Validity and repeatability of inertial measurement units for measuring gait parameters (2017, methodology): “With sensors at the ankle, the system utilizes the angular velocity of the shank to determine timing of mid-swing, initial contact, and toe-off, and combines these timing signals with a kinematic model of the lower body to estimate cadence and stride length [9].”
Gait Analysis Using Wearable Sensors (2012, methodology): “analysis is applied in the automatic identification of temporal gait parameters of post-stroke individuals to provide an assessment of the functional utilization of the affected lower extremity as part of the behavior enhancing feedback [165,166].”
Wireless Sensor Networks for Healthcare (2010, background): “tings including gait analysis [60], [64], [73], activity classification [29], [52], athletic performance [3], [51], and neuromotor disease rehabilitation [49], [57].”
Mercury: a wearable sensor network platform for high-fidelity motion analysis (2009, methodology): “In recent years there has been increased interest in body sensor networks for wearable applications as diverse as elder care [12, 46], emergency response [28, 9], studying athletic performance [1, 32], gait analysis [38, 40, 44], and activity classification [18, 33].”
3D gait assessment in young and elderly subjects using foot-worn inertial sensors. (2010, methodology): “Temporal detection of gait cycles was done using angular velocity of foot (O) to identify stance phase, adapted from (Salarian et al., 2004).”
Toward Pervasive Gait Analysis With Wearable Sensors: A Systematic Review (2016, methodology): “Previously, work using inertial sensors to estimate gait speed tended to model human gait as an inverse pendulum [7], [100], [106], [112].”