Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.