Spatio-temporal parameters of gait measured by an ambulatory system using miniature gyroscopes.

By Kamiar Aminian, Bijan Najafi, Christophe J. Büla, P. Leyvraz, Philippe A. Robert. Journal of Biomechanics, 2002

838 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Endocrinology, Diabetes and Metabolism, Physical Therapy, Sports Therapy and Rehabilitation

View Paper: https://www.semanticscholar.org/paper/d99950ef6e7f60df79c52a2734877a2d5eb4199e

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In Biomedical Engineering

In Endocrinology, Diabetes and Metabolism

In Physical Therapy, Sports Therapy and Rehabilitation

Citations in the corpus

Automatic detection of gait events using kinematic data. (2007, background): “information can also be extracted from the signals recorded from additional sensors such as accelerometers [6,7] or gyroscopes [8].”
Wearable Sensor-Based Real-Time Gait Detection: A Systematic Review (2021, background): “[80] reported that TO and HS events consist of combined features that can be well resolved in the time-frequency domain.”
Gait Partitioning Methods: A Systematic Review (2016, background): “…of the swing cannot be discriminated; (ii) the placement of the sensors on patients with pathological gaits affects the accuracy and reliability [91]; (iii) the wire connections can decrease the system service life [27]; and, (iv) the force generated in the gait cycle cannot be isolated by the…”
Capturing human motion using body‐fixed sensors: outdoor measurement and clinical applications (2004, methodology): “let analysis was proposed to compute the values of spatial and temporal gait parameters from the angular velocity of the lower limbs.(45) Lower limb rotation, stride length and stride velocity were estimated using the”
Assessment of walking features from foot inertial sensing (2005, methodology): “We can provide a reasonable assessment of the influence of the mentioned error sources by comparing the walking speed and incline estimates with their nominal values—see the typical example in Fig. 8. The achieved measurement accuracy is remarkable and favorably compares with competing approaches [6”
Window Size Impact in Human Activity Recognition (2014, background): “…from W min size 0.25 to 3.25 s. Rehabilitation exercises for the upper body involve arms and the trunk and arm activities ( i.e. , 9–11, 13, 16, 19–25) that are optimally recognized for W max perf between 1.25 and 2.25 s, while W min size values range from one to 1.25 s. Activities similar to…”
Activity identification using body-mounted sensors—a review of classification techniques (2009, methodology): “This feature was calculated after a wavelet-based algorithm had been used to identify stance phase (Aminian et al 2002).”
Machine Learning Methods for Classifying Human Physical Activity from On-Body Accelerometers (2010, background): “gyros has fostered several research reports, where they are used for applications in gait analysis, either alone or in combination with accelerometers [24,25].”
3D gait assessment in young and elderly subjects using foot-worn inertial sensors. (2010, background): “Compared to other inertial-based gait analysis system (Aminian et al., 2002; Salarian et al., 2004; Sabatini et al., 2005; Schepers et al., 2007), similar or slightly better accuracy and precision was obtained for SL and SV.”
Two simple methods for determining gait events during treadmill and overground walking using kinematic data. (2008, methodology): “Novel techniques, such as the use of miniature gyroscopes have also been used to determine the timing of gait events [3,4].”
Gait assessment in Parkinson's disease: toward an ambulatory system for long-term monitoring (2004, methodology): “For this purpose, a portable analysis system, Physilog 1 [ 12 ], developed in our institute, was used.”
Measurement of stand-sit and sit-stand transitions using a miniature gyroscope and its application in fall risk evaluation in the elderly (2002, methodology): “Furthermore, using footswitches [11] to detect the gait events has several limitations in pathological cases [12].”