A medical tool is described for supporting an individual suffering from a mental condition or disorder characterized by auditory psychosis symptoms. The tool can assist in training the individual to distinguish between an acute auditory psychosis episode and ambient sounds. The tool can monitor for a non-audio input by a patient, where the input represents an indication that the patient is hearing sounds potentially symptomatic of psychosis. A microphone can monitor ambient sounds, which are tested against a threshold to determine and whether an auditory psychosis episode may be occurring.

According to an embodiment, a wearable electronic device may include a motion sensor, an audio sensor, a display, a memory, and a processor electrically connected to the motion sensor, the audio sensor, and the memory. The processor may be configured to obtain motion sensing information via the motion sensor, obtain an audio signal corresponding to the motion sensing information via the audio sensor, identify a tooth-brushing hand motion type corresponding to the motion sensing information, identify an audio signal pattern corresponding to the tooth-brushing hand motion type, and identify, based on the tooth-brushing hand motion type and the audio signal pattern, a tooth-brushing hand motion. Other embodiments may also be possible.

A triboelectric sensor device with a substantially cylindrical nonconductive core, and a conductive fiber substantially helically disposed around the conductive core and in an axial direction thereof. Example implementations also include a method of extracting communication from body position, by transforming one or more training body position inputs by a principal component analysis, generating training input to a support vector machine (SVM) based on a target body position, and generating one or more SVM classification outputs associated with the target body position.

Disclosed herein are systems, devices, and methods for evaluating or analyzing complex audio signals using multi-dimensional statistical signatures and machine learning algorithms. One advantage of the present disclosure is the ability for remote evaluation of respiratory tract health using speech analysis. The need for remote collection capabilities that can sensitively and reliably characterize respiratory tract function is particularly pertinent in view of the recent Covid-19 pandemic, which may adversely affect the health of individuals who could already be experiencing health problems with respiratory tract function.

The present disclosure relates to a difficult airway evaluation method and device based on a machine learning voice technology. The method includes the following steps: acquiring voice data of a patient; carrying out feature extraction on the voice data, obtaining a pitch period of pronunciations, and acquiring a voiced sound feature and unvoiced sound features based on the pitch period of pronunciations; and constructing a difficult airway evaluation classifier based on the machine learning voice technology, analyzing the received voiced sound feature and unvoiced sound features by the trained difficult airway evaluation classifier, and carrying out scoring on the severity of a difficult airway to obtain an evaluation result of the difficult airway.

A patient care method through artificial intelligence-based monitoring in accordance with one example of the present disclosure comprises steps of: obtaining image information relating a user by a first collecting portion, obtaining speech information relating to the user by a second collecting portion and obtaining biometrics information relating to the user by a third collecting portion (Step 1); representing at least a part of a plurality of information obtained from the first collecting portion, the second collecting portion and the third collecting portion, through a display portion of a user table (Step 2); determining health condition of the user, based on a part of the plurality of information obtained from the first collecting portion, the second collecting portion and the third collecting portion by a server (Step 3); controlling the display portion of the user table to represent a first information automatically generated based on the determined heath condition by the server (Step 4), wherein the first information is converted in real time based on user's feedback on the first information and represents a change in the determined health condition on the display portion.

Face mask communication system 100 includes face mask 10 worn by user 14 and signal receiving hand glove 16 worn by user 18. Glove 16 includes data receiver 66 for data communication with mask 10 and includes multiple vibrotactile devices for generating haptic signals. Mask 10 includes an elastic element of flexible material, and a plurality of EMG sensors 12 fixed to the element, for sensing electrical activity of face regions of the user's 14 face. Mask 10 includes a processor 60; decoding algorithm 110 and transmitter 62 for, respectively, processing signals from the sensors 12; generating command instructions based on the signals; and wirelessly transmitting the signals to receiver 66 of glove 16. Mask 10 includes thread elements connected to the elastic element of mask 10 enabling tensioning of the element to provide for fitment of mask 10 to users of different sizes, for optimal location sensors 12.

A computer implemented method for providing pain management using a wearable device determines a predictive model estimating an intensity level of pain as a function of at least one physiological parameter of a user of the wearable device and at least one activity of the user. The activity of the user includes one or combination of a type of the activity, a level of the activity, a location of the activity, and a duration of the activity. The method determines measurements of physiological and activity sensors of the wearable device to produce values of the physiological parameter and the activity of the user and predicts the intensity level of the pain based on the predictive model and the values of the physiological parameter and the activity of the user. The method executes actions based on the predicted intensity level of pain.

An embodiment of the invention provides a method and system including a sensor on a vehicle and a processor connected to the sensor. The processor determines a probability of falling based on input from the sensor, whether the probability of falling exceeds a threshold, and a state of an operator of the vehicle. An actuator connected to the processor receives a signal from the processor when the probability of falling exceeds the threshold and when the state of the operator includes an impaired state. Stabilizer wheels are connected to the actuator, where the signal includes a command to deploy the stabilizer wheels.

A method and device for predicting Alzheimer's disease based on voice characteristics are provided. The device for predicting Alzheimer's disease according to an embodiment includes: a voice input unit configured to generate a voice sample by recording a voice of a subject; a data input unit configured to receive demographic information of the subject; a voice characteristic extraction unit configured to extract voice characteristics from the generated voice sample; and a prediction model that is pre-trained to predict presence or absence of Alzheimer's disease in the subject, based on the voice characteristics and the demographic information.