A Machine Learning Pipeline for Gait Analysis in a Semi Free-Living Environment.

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.

Adaptive Change-Point Detection for Studying Human Locomotion.

This paper presents an innovative method to analyze inertial signals recorded in a semi-controlled environment. It uses an adaptive and supervised change point detection procedure to decompose the signals into homogeneous segments, allowing a refined analysis of the successive phases within a gait protocol. Thanks to a training procedure, the algorithm can be applied to a wide range of protocols and handles different levels of granularity. The method is tested on a cohort of 15 healthy subjects performing a complex protocol composed of different activities and shows promising results for the automated and adaptive study of human gait and activity.Clinical relevance- A new approach to study human activity and locomotion in Free-Living Environments FLEs through an adaptive change-point detection which isolates homogeneous phases.

The Use of Inertial Measurement Units for the Study of Free Living Environment Activity Assessment: A Literature Review.

This article presents an overview of fifty-eight articles dedicated to the evaluation of physical activity in free-living conditions using wearable motion sensors. This review provides a comprehensive summary of the technical aspects linked to sensors (types, number, body positions, and technical characteristics) as well as a deep discussion on the protocols implemented in free-living conditions (environment, duration, instructions, activities, and annotation). Finally, it presents a description and a comparison of the main algorithms and processing tools used for assessing physical activity from raw signals.

Thickness dependent sensing mechanism in sorted semi-conducting single walled nanotube based sensors.

Single walled carbon nanotube (SWCNT) networks present outstanding potential for the development of SWCNT-based gas sensors. Due to the complexity of the transport properties of this material, the physical mechanisms at stake during exposure to gas are still under debate. Previously suggested mechanisms are charge transfer between gas molecules and SWCNT and Schottky barrier modulation. By comparing electrical measurements with an analytical model based on Schottky barrier modulation, we demonstrate that one mechanism or the other is predominant depending on the percolation of metallic carbon nanotubes. Below the metallic SWCNT percolation threshold, sensing is dominated by the modulation of the Schottky barrier, while above this threshold, it is only attributed to a charge transfer between SWCNT and gas molecules. Both mechanisms are discussed in terms of sensitivity and resolution leading to routes for the optimization of a gas sensor architecture based on highly enriched semiconducting carbon nanotube films.