A Machine Learning Pipeline for Gait Analysis in a Semi Free-Living Environment.
Sylvain JungNicolas de L'EscalopierLaurent OudreCharles TruongEric DorveauxLouis GorintinDamien RicardPublished in: Sensors (Basel, Switzerland) (2023)
This paper presents a novel approach to creating a graphical summary of a subject's activity during a protocol in a Semi Free-Living Environment. Thanks to this new visualization, human behavior, in particular locomotion, can now be condensed into an easy-to-read and user-friendly output. As time series collected while monitoring patients in Semi Free-Living Environments are often long and complex, our contribution relies on an innovative pipeline of signal processing methods and machine learning algorithms. Once learned, the graphical representation is able to sum up all activities present in the data and can quickly be applied to newly acquired time series. In a nutshell, raw data from inertial measurement units are first segmented into homogeneous regimes with an adaptive change-point detection procedure, then each segment is automatically labeled. Then, features are extracted from each regime, and lastly, a score is computed using these features. The final visual summary is constructed from the scores of the activities and their comparisons to healthy models. This graphical output is a detailed, adaptive, and structured visualization that helps better understand the salient events in a complex gait protocol.
Keyphrases
- machine learning
- big data
- end stage renal disease
- artificial intelligence
- electronic health record
- chronic kidney disease
- ejection fraction
- newly diagnosed
- endothelial cells
- randomized controlled trial
- deep learning
- cerebral palsy
- prognostic factors
- minimally invasive
- peritoneal dialysis
- magnetic resonance imaging
- data analysis
- single molecule
- sensitive detection
- patient reported outcomes
- patient reported
- quantum dots