Learning Efficient Spatial-Temporal Gait Features with Deep Learning for Human Identification

2018 ◽  
Vol 16 (3-4) ◽  
pp. 457-471 ◽  
Author(s):  
Wu Liu ◽  
Cheng Zhang ◽  
Huadong Ma ◽  
Shuangqun Li
Keyword(s):  
Deep Learning ◽  
Human Identification ◽  
Gait Features

Experiment of ECG Human Identification with Deep Learning Algorithm

2018 ◽  
Vol 24 (11) ◽  
pp. 8980-8984
Author(s):  
Tuerxun Waili ◽  
Rizal Mohd Nor ◽  
Abdul Wahab Abdurahman ◽  
Khairul Azami Sidek ◽  
Ayixiamu Litifu
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Human Identification ◽  
Deep Learning Algorithm

Human Identification with Finger Vien Image Using Deep Learning

2021 ◽  
Author(s):  
Zinah Khalid Jasim Jasim ◽  
Alaa Hamid Mohammed ◽  
Lina Elwiya ◽  
Baraa Dhafer Al-Jabbari ◽  
Haitham Alhaji
Keyword(s):  
Deep Learning ◽  
Human Identification

scholarly journals Quantifying normal and parkinsonian gait features from home movies: Practical application of a deep learning–based 2D pose estimator

2019 ◽  
Author(s):  
Kenichiro Sato ◽  
Yu Nagashima ◽  
Tatsuo Mano ◽  
Atsushi Iwata ◽  
Tatsushi Toda
Keyword(s):  
Deep Learning ◽  
Freezing Of Gait ◽  
Video Camera ◽  
Pitch Detection ◽  
Home Video ◽  
Detection Approach ◽  
Joint Coordinates ◽  
Gait Features ◽  
Time Autocorrelation Function ◽  
Short Time

AbstractObjectiveGait movies recorded in daily clinical practice are usually not filmed with specific devices, which prevents neurologists benefitting from leveraging gait analysis technologies. Here we propose a novel unsupervised approach to quantifying gait features and to extract cadence from normal and parkinsonian gait movies recorded with a home video camera by applying OpenPose, a deep learning–based 2D-pose estimator that can obtain joint coordinates from pictures or videos recorded with a monocular camera.MethodsOur proposed method consisted of two distinct phases: obtaining sequential gait features from movies by extracting body joint coordinates with OpenPose; and estimating cadence of periodic gait steps from the sequential gait features using the short-time pitch detection approach.ResultsThe cadence estimation of gait in its coronal plane (frontally viewed gait) as is frequently filmed in the daily clinical setting was successfully conducted in normal gait movies using the short-time autocorrelation function (ST-ACF). In cases of parkinsonian gait with prominent freezing of gait and involuntary oscillations, using ACF-based statistical distance metrics, we quantified the periodicity of each gait sequence; this metric clearly corresponded with the subjects’ baseline disease statuses.ConclusionThe proposed method allows us to analyze gait movies that have been underutilized to date in a completely data-driven manner, and might broaden the range of movies for which gait analyses can be conducted.

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