公开(公告)号：US20170181915A1

公开(公告)日：2017-06-29

申请号：US15328010

申请日：2015-07-23

Applicant: AGENCY FOR SCIENCE, TECHNOLOGY AND RESEARCH

Inventor： Kai Keng ANG ,  Cuntai GUAN ,  Kok Soon PHUA ,  Longjiang ZHOU ,  Chuan Chu WANG

IPC: A61H1/02 ,  A61B5/22 ,  A61B5/0476 ,  G09B5/06 ,  A61B5/11

CPC classification number: A61H1/0274 ,  A61B5/0476 ,  A61B5/0482 ,  A61B5/1121 ,  A61B5/1126 ,  A61B5/225 ,  A61B5/7475 ,  A61B2560/0223 ,  A61H1/02 ,  A61H1/0285 ,  A61H1/0288 ,  A61H2001/0203 ,  A61H2201/1635 ,  A61H2201/1671 ,  A61H2201/5043 ,  A61H2201/5048 ,  A61H2201/5061 ,  A61H2201/5064 ,  A61H2230/105 ,  G06F3/014 ,  G06F3/015 ,  G06F3/016 ,  G09B5/06

Abstract: A method for calibrating and executing a rehabilitation exercise for a stroke-affected limb of a stroke patient is disclosed, the method comprising the steps of providing a haptic device for an able limb of the stroke patient to manipulate to perform a calibration action to result in a first position of the haptic device, and providing the haptic device for the stroke-affected limb to manipulate to perform the calibration action to result in a second position of the haptic device. The method further comprises the steps of moving the haptic device coupled with the stroke-affected limb from the second position towards the first position until a predetermined counterforce is detected, indicating an extreme position for the stroke-affected limb using the haptic device, and calibrating the haptic device with the extreme position such that during the rehabilitation exercise, the haptic device is prevented from moving beyond the extreme position.

公开(公告)号：US20230380740A1

公开(公告)日：2023-11-30

申请号：US18030717

申请日：2020-10-07

Applicant: Agency for Science, Technology and Research ,  NATIONAL UNIVERSITY OF SINGAPORE

Inventor： Zheng Yang CHIN ,  Haihong ZHANG ,  Cuntai GUAN ,  Chuan Chu WANG ,  Tih Shih LEE

IPC: A61B5/16 ,  A61B5/378

CPC classification number: A61B5/163 ,  A61B5/378

Abstract: Disclosed is a system for sensor-based training intervention. The system includes one or more electroencephalogram (EEG) sensors for retrieving brain signals of a subject, one or more sensors for retrieving eye tracking data of one or both eyes of the subject, and one or more processors. The one or more processors are configured to model a joint state space of the brain signals and eye tacking data by combining the brain signals and eye tracking data into combined data using sequential Bayesian fusion, and measure a visuospatial attention indicator from combined data.

公开(公告)号：US20220183611A1

公开(公告)日：2022-06-16

申请号：US17598139

申请日：2019-03-29

Applicant: AGENCY FOR SCIENCE, TECHNOLOGY AND RESEARCH ,  TAN TOCK SENG HOSPITAL PTE LTD

Inventor： Kok Soon PHUA ,  Hai Hong ZHANG ,  Su-Yin YANG ,  Fong Ling LOY ,  Ka Yin Christina TANG ,  Soon Huat NG ,  Chuan Chu WANG ,  Cuntai GUAN ,  Soon Yin TJAN

IPC: A61B5/372 ,  A61B5/00 ,  G16H50/30

Abstract: Disclosed is a process for identifying and extracting pain-related electroencephalogram (EEG) signals. The process comprises receiving, from one or more trials, EEG data for each trial; determining a current density for each signal; estimating the current density for a set of neural activity regions of interest, based on the computed current density; and computing at least one spectrum characteristic for each trial based on the estimated current density. Thus mean and variance of changes in the EEG data between EEG data labeled as being indicative of a pain state and EEG data labeled as being indicative of a non-pain state, for each neural activity region of interest can be calculated, and pain-related EEG signals can be identified based on at least one a region of interest at which the variance is below a predetermined threshold.

公开(公告)号：US20220172023A1

公开(公告)日：2022-06-02

申请号：US17599148

申请日：2019-03-29

Applicant: AGENCY FOR SCIENCE, TECHNOLOGY AND RESEARCH

Inventor： Haihong ZHANG ,  Huijuan YANG ,  Zhuo ZHANG ,  Chuan Chu WANG ,  Dajiang HE ,  Kai Keng ANG

IPC: G06N3/04 ,  G06N3/12

Abstract: Disclosed is a system and method for measuring a non-stationary brain signal. Per the method, the system receives brain signals, extracts one or more features from the brain signals, determines, based on the Receive brain signals extracted one or more features, a super feature set describing dynamic behaviour of the brain signals, and forms a cluster-recurrent-neural-network (CRNN) from one or more samples taken from the super feature set, by formExtract one or more features ing at least one cluster of the one or more samples based on the one or more from the brain signals features, to estimate a brain state of interest in each cluster of brain signals; using a Monte Carlo approach to estimate an a posteriori probability density function of the brain state of interest by applying the CRNN to each cluster of the at least one cluster; and determining the brain state of interest from the estimated density function.