Armando Coccia

TL;DR: Investigation of the agreement between two commercial wearable inertial systems for gait analysis: Opal and G-Walk Systems, and their repeatability showed a not perfect agreement between the two systems although they both showed good repeatability.

TL;DR: In this article, the authors explored the machine learning feasibility to classify biomechanical risk according to the revised NIOSH lifting equation, where acceleration and angular velocity signals were collected using a wearable sensor during lifting tasks performed by seven subjects and further segmented to extract time domain features: root mean square, minimum, maximum and standard deviation.

TL;DR: The “wearability” of the system and its comfortable use make it suitable to be used in domestic environment for the continuous remote health monitoring of de-hospitalized patients but also in the ergonomic assessment of health workers, thanks to its low invasiveness.

TL;DR: Study results suggested that there is a drastic change in spatiotemporal and kinematic parameters related to gait underlining how the backpack alters the latter.

TL;DR: Evaluated repeatability of spatio-temporal gait measurements are more repeatable than sway and anticipatory postural adjustments variables, and they are proper indexes to better identify Parkinsonian walking features.