A method of determining regularity of a bio-signal is provided. The method of determining regularity of a bio-signal according may include acquiring a plurality of pulse waveforms of the bio-signal, acquiring a plurality of slope waveforms corresponding to the plurality of pulse waveforms, binarizing the plurality of slope waveforms, acquiring synchronization information of the plurality of pulse waveforms based on binarizing the plurality of pulse waveforms; acquiring a synchronization rate of a reference interval based on the synchronization information, and determining whether the bio-signal is regular or irregular based on the synchronization rate of the reference interval.

In an approach to predicting physiological and behavioral states utilizing models representing relationships between driver health states and vehicle dynamics data, one or more computer processors capture one or more vehicle motion parameters. The one or more computer processors to capture one or more physiological parameters; identify contextual data associated with the one or more captured vehicle motion parameters and the one or more captured physiological parameters; predict one or more driving behavior parameters by utilizing one or more physical models fed with the one or more vehicle motion parameters and the identified contextual data; predict one or more driver health parameters by utilizing a model trained with the one or more captured physiological parameters and the identified contextual data; generate a risk assessment based on the one or more predicted driving behavior parameters and the one or more predicted driver health parameters.

In an approach to predicting physiological and behavioral states utilizing models representing relationships between driver health states and vehicle dynamics data, one or more computer processors capture one or more vehicle motion parameters. The one or more computer processors to capture one or more physiological parameters; identify contextual data associated with the one or more captured vehicle motion parameters and the one or more captured physiological parameters; predict one or more driving behavior parameters by utilizing one or more physical models fed with the one or more vehicle motion parameters and the identified contextual data; predict one or more driver health parameters by utilizing a model trained with the one or more captured physiological parameters and the identified contextual data; generate a risk assessment based on the one or more predicted driving behavior parameters and the one or more predicted driver health parameters.

An implantable neurostimulator (INS) and method of use, the INS including an electrical lead having formed thereon at least a pair of bi-polar electrodes, wherein the electrical lead is configured for placement of the pair of bi-polar electrodes proximate protrusor muscles of a patient, a pulse generator electrically connected to the electrical lead and configured to deliver electrical energy to the pair of bi-polar electrodes, the pulse generator having mounted therein a sensor and a control circuit, and the sensor is configured to generate signals representative of an orientation of the pulse generator and communicate the signals to the control circuit and the control circuit is configured to determine the orientation of the pulse generator and deliver electrical energy to the bi-polar electrodes when the determined orientation correlates to a pre-determined orientation.

An embodiment electromyography signal detection device includes a noise signal obtaining device configured to obtain a noise signal of an unknown reference frequency at a periphery of a user, an electromyography signal acquisition device configured to measure an electromyography signal from the user, and a controller configured to remove a noise signal included in the electromyography signal of the user based on the obtained noise signal of the unknown reference frequency.

An embodiment electromyography signal detection device includes a noise signal obtaining device configured to obtain a noise signal of an unknown reference frequency at a periphery of a user, an electromyography signal acquisition device configured to measure an electromyography signal from the user, and a controller configured to remove a noise signal included in the electromyography signal of the user based on the obtained noise signal of the unknown reference frequency.

A system for control of a device includes at least one sensor module detecting orientation of a user's body part. The at least one sensor module is in communication with a device module configured to command an associated device. The at least one sensor module detects orientation of the body part. The at least one sensor module sends output signals related to orientation of the user's body part to the device module and the device module controls the associated device based on the signals from the at least one sensor module.

Biometric information about a person may be collected and analyzed to gain insight into the person's physical and/or emotional conditions. The collection and analysis may be performed using a uniquely designed sensing device that includes multiple sets of sensors configured to collect EEG, EOG, EMG, EDA, and/or PPG signals from the person's head and/or facial areas. The sensing device may include a multi-layered facepad and may be coupled to a VR/AR headset and/or a scalp engagement apparatus to monitor the person's physiological and/or neural reactions to audio/visual stimuli.

Systems and methods for Emotional-Imaging Composer are disclosed. The method may include recording a real-time biosignal from a plurality of biosignal sensors. The method may further include determining an emotion that is associated with the real-time biosignal. The method may further include outputting a display feature corresponding to the emotion, wherein the display feature is a lighting effect on a graphical user interface.

The invention discloses an object, a method and a system for detecting heartbeat or whether an electrode is in good contact. The heartbeat is detected by arranging multiple textile electrodes on the textile, using ECG equipotential line diagram, considering interference caused of human movement, and designing a separating electrode structure, electrode position, area and lead layout in an innovative manner; the dry electrode or capacitive coupling electrode is selected along with the change of environmental state so as to pick up the ECG signals; the contact between the electrode and the human body can be detected whether it is in a good state or not by measuring the noise, body surface impedance, muscle impedance and the like; in addition, the posture and action of human body can be speculated according to the wave mode and noise of the ECG signals.