Chères et chers collègues,
Nous avons le plaisir de vous annoncer que Nahime El Abiad soutiendra sa thèse réalisée au LBMC le 25 novembre 2022 à 13h30 à l'Université Gustave Eiffel, campus de Bron et en visioconférence.
Titre : A step toward ubiquitous monitoring of real-life gait fall risk factors using non-dedicated inertial sensors
Direction : Valérie Renaudin (Geoloc, Univ. Eiffel); Thomas Robert (LBMC, Univ. Eiffel)
Résumé :
Background and objective Falls in older adults have great medical, social and financial impact. The aim of this thesis is to propose a ubiquitous method to extract relevant fall risk parameters from real-life inertial data regardless of device type and placement.
Methods The main difficulty is estimating fall risk parameters independent of sensor placement (wrist, pocket, etc.). For this, we propose to calculate fall risk parameters based on discrete step time series. Then, the problem is transferred to finding a robust step detection algorithm which we developed and validated against different populations, placements and activities. Next, we calculated fall risk parameters and tested their association with future falls on an ambulatory dataset (one week of recording) of 300 elderly people.
Results The step detection method had an average precision and recall of 99 % and 95 % respectively when evaluated on datasets with different populations (young, elderly, blind), walking conditions (outdoors, using walking aid), and sensor placements (jacket, pants pocket, handheld). The association between calculated parameters and prospective falls had an AUC of 0.7 if parameters are aggregated on walking bouts greater than 200 steps (2-minutes). This AUC is comparable to models made with a fixed sensor placement. However, selecting long walking bouts causes the exclusion of participants who do not walk long enough (6 % of population considered).
Significance The proposed novel solution is ubiquitous and could reach the broad public. It could open doors toward personal monitoring of fall risk status using consumer devices.
Keywords : Fall risk; Step detection; Consumer devices; Inertial Measurement Units; Fall prediction; Sensor placement; Older adults.
Détails soutenance : Salle Léonard de Vinci, Université Gustave Eiffel, campus de Bron, 25 Av. François Mitterrand, 69500 Bron. Pour le lien visio, contacter
