Apparatus and associated methods relate to assisting gait impaired patients. In an illustrative example, a gait assisting apparatus may be wearable by a user including a sensor module and an actuator. The sensor module may be configured to generate a sensor measurement from measured data associated with the user. For example, a controller operably coupled to the sensor module may include a local classification model configured to classify a gait situation based on a classification input received from the sensor module. In some implementations, an activation module of the controller may generate an activation level to control the actuator. In operation, the activation module may apply the local classification model to the classification input to determine the activation level of the actuator to generate a vibration gait assistance and/or illumination guidance. Various embodiments may advantageously provide a gait assistant function to prevent gait impairment injuries.
A61B 5/291 - Bioelectric electrodes therefor specially adapted for particular uses for electroencephalography [EEG]
A61B 5/256 - Wearable electrodes, e.g. having straps or bands
A61B 6/46 - Arrangements for interfacing with the operator or the patient
A61B 6/50 - Apparatus or devices for radiation diagnosisApparatus or devices for radiation diagnosis combined with radiation therapy equipment specially adapted for specific body partsApparatus or devices for radiation diagnosisApparatus or devices for radiation diagnosis combined with radiation therapy equipment specially adapted for specific clinical applications
Apparatus and associated methods relate to emergency stroke detection and classification. In an illustrative example, a stroke detection device may include an ensemble stroke classification model (ESCM). The ESCM may, for example, include class-specific model sets applicable for at least four classes of features, and a general model set applicable for all classes of features. Each model set, for example, may be stacked with multiple class-specific models for each of a corresponding group of architectures. The stroke detection device may, for example, extract predetermined features from a rolling window of a first predetermined duration of EEG data. The predetermined features are extracted and combined into a 1-D input vector. By applying the input vector, the stroke detection device may generate a binary stroke prediction result. Various embodiments may advantageously accurately predict whether a patient is experiencing a stroke within a finite time to assist an emergency service personnel.
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
Apparatus and associated methods relate to a signal acquisition cap. In an illustrative example, an EEG cap may include a soft support structure and a clip-on electrode unit. The soft support structure, for example, may include elastic bands in a lattice formation. The clip-on electrode unit may be releasably coupled, for example, to the soft support structure. The clip-on electrode unit may include an electrode body of radiotranslucent materials. A conductive coating, for example, may be disposed on a surface of the electrode body such that the clip-on electrode unit is substantially radiotranslucent. For example, when the clip-on electrode unit is deployed on a skin surface, the spring loaded recording channel is configured to conduct EEG signals from the skin surface to a remote computer system. Various embodiments may advantageously deploy the EEG cap without obstructing a view of a concurrently operating radioactivity imaging tool.
Apparatus and associated methods relate to emergency stroke detection and classification. In an illustrative example, a stroke detection device may include an ensemble stroke classification model (ESCM). The ESCM may, for example, include class¬ specific model sets applicable for at least four classes of features, and a general model set applicable for all classes of features. Each model set, for example, may be stacked with multiple class-specific models for each of a corresponding group of architectures. The stroke detection device may, for example, extract predetermined features from a rolling window of a first predetermined duration of EEG data. The predetermined features are extracted and combined into a 1-D input vector. By applying the input vector, the stroke detection device may generate a binary stroke prediction result. Various embodiments may advantageously accurately predict whether a patient is experiencing a stroke within a finite time to assist an emergency service personnel.
G16B 40/00 - ICT specially adapted for biostatisticsICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
Apparatus and associated methods relate to emergency stroke detection and classification. In an illustrative example, a stroke detection device may include an ensemble stroke classification model (ESCM). The ESCM may, for example, include class-specific model sets applicable for at least four classes of features, and a general model set applicable for all classes of features. Each model set, for example, may be stacked with multiple class-specific models for each of a corresponding group of architectures. The stroke detection device may, for example, extract predetermined features from a rolling window of a first predetermined duration of EEG data. The predetermined features are extracted and combined into a 1-D input vector. By applying the input vector, the stroke detection device may generate a binary stroke prediction result. Various embodiments may advantageously accurately predict whether a patient is experiencing a stroke within a finite time to assist an emergency service personnel.
G16B 40/00 - ICT specially adapted for biostatisticsICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
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