公开(公告)号：US20240023893A1

公开(公告)日：2024-01-25

申请号：US18479766

申请日：2023-10-02

Applicant: EERS GLOBAL TECHNOLOGIES INC.

Inventor： Jeremie Voix ,  Hami Montsarrat-Chanon ,  Rachel Bou Serhal ,  Patrick Cardinal ,  Philippe Chabot

IPC: A61B5/00 ,  G06N20/00 ,  G06F3/16 ,  G10L15/22 ,  G10L25/51 ,  G06F18/213 ,  G06F18/21 ,  G06F18/241

CPC classification number: A61B5/6817 ,  G06N20/00 ,  G06F3/16 ,  G10L15/22 ,  G10L25/51 ,  G06F18/213 ,  G06F18/217 ,  G06F18/241 ,  G10L2015/227

Abstract: A system and method for training a classification module of nonverbal audio events and a classification module for use in a variety of nonverbal audio event monitoring, detection and command systems. The method comprises capturing an in-ear audio signal from an occluded ear and defining at least one nonverbal audio event associated to the captured in-ear audio signal. Then sampling and extracting features from the in-ear audio signal. Once the extracted features are validated, associating the extracted features to the at least one nonverbal audio event and updating the classification module with the association. The nonverbal audio event comprises one or a combination of user-induced or externally-induced nonverbal audio events such as teeth clicking, tongue clicking, blinking, eye closing, teeth grinding, throat clearing, saliva noise, swallowing, coughing, talking, yawning with inspiration, yawning with expiration, respiration, heartbeat and head or body movement, wind, earpiece insertion or removal, degrading parts, etc.

公开(公告)号：US20230386316A1

公开(公告)日：2023-11-30

申请号：US18248955

申请日：2021-10-13

Applicant: EERS GLOBAL TECHNOLOGIES INC.

Inventor： Alex Guilbeault-Sauve ,  Bruno De Kelper ,  Jeremie Voix

IPC: G08B21/04 ,  G01P15/18

CPC classification number: G08B21/0446 ,  G08B21/043 ,  G01P15/18

Abstract: A system to detect a man down situation using intra-aural inertial measurement units is disclosed. The system comprises an earpiece having an inertial measurement unit (IMU) adapted to capture acceleration and rotation speed of the earpiece. The method comprises a training phase to characterize statistical distribution models of extreme values of feature signals, segmented by their respective optimally-sized time windows and to merge the detection probability provided by the statistical model of the feature signals. The method further comprises a prediction phase. The prediction phase comprises applying the detection strategy on independent data, based on the critical states obtained from the characterization. The data from the said inertial measurement of the MEMS are used for the detection of full (F), immobility (I) and down on the ground (D) states.