A surface electromyography signal-torque matching method based on multi-segmentation parallel CNN model (MSP-CNN model), step 1: collecting torque signals and surface electromyography (sEMG) signals when tightening a bolt; step 2: dividing a range of a transducer by at least two granularities, generating a plurality of torque sub-ranges corresponding to the at least two granularities and labeling the plurality of torque sub-ranges with torque labels; step 3: generating sEMG graphs of the sEMG signals in each time window; step 4: determining the torque labels of each time window under each of the at least two granularities according to the torque sub-ranges that average values of torques fall in; step 5: establishing a sample set; step 6: building a MSP-CNN model, and training parallel independent CNN models with sample datasets; and step 7: inputting the sEMG signals of the operator during assembly into trained MSP-CNN model and identifying assembly torques.

A surface electromyography signal-torque matching method based on multi-segmentation parallel CNN model (MSP-CNN model), step 1: collecting torque signals and surface electromyography (sEMG) signals when tightening a bolt; step 2: dividing a range of a transducer by at least two granularities, generating a plurality of torque sub-ranges corresponding to the at least two granularities and labeling the plurality of torque sub-ranges with torque labels; step 3: generating sEMG graphs of the sEMG signals in each time window; step 4: determining the torque labels of each time window under each of the at least two granularities according to the torque sub-ranges that average values of torques fall in; step 5: establishing a sample set; step 6: building a MSP-CNN model, and training parallel independent CNN models with sample datasets; and step 7: inputting the sEMG signals of the operator during assembly into trained MSP-CNN model and identifying assembly torques.

Electrophysiological signals from a graphical representation of an electrophysiology map including a plurality of electrophysiology data points can be sorted by receiving user inputs specifying a number of virtual electrodes for a virtual catheter and defining a pathway of the virtual catheter. A corresponding number of virtual electrodes can be defined on the pathway of the virtual catheter, and one or more electrophysiology data points relevant to electrical activity at the virtual electrodes can be identified, allowing output of a graphical representation of electrophysiological signals corresponding to the identified electrophysiology data points. Relevant electrophysiology data points can be identified by applying one or more relevance criterion, such as a distance criterion, a bipole orientation criterion, a time criterion, and/or a morphology criterion.

A system includes a processor in communication with a set of bioelectrical sensors and a user interface device that provides functionality to monitor one or more bioelectrical signals from a set of bioelectrical electrodes. Processor automatically adjusts a selected one of: (i) a resting threshold; and (ii) a switch threshold that is greater than the resting threshold based at least in part on a trend of the bioelectrical signal. The processor determines whether an amplitude of the bioelectrical signal is less than the resting threshold. In response to determining that the amplitude is less than the resting threshold, the processor determines whether an amplitude of the bioelectrical signal subsequently is equal to or greater than the switch threshold. In response to determining that the bioelectrical signal is greater than the switch threshold, the processor triggers the user interface device with a switch signal. The present disclosure illustrates various techniques and configurations to enable a series of dynamic workflows for the selection and presentation of content from an information system relevant to activities of a human user. The dynamic workflows used with the NeuroNode as described herein enable the integration of user interfaces and user communication platforms to achieve relevant and timely communication among users and others and related actions. The dynamic workflows described herein further may be integrated with social networks and portable communication mediums to provide additional availability and delivery of content to users in a variety of settings.

A system includes a processor in communication with a set of bioelectrical sensors and a user interface device that provides functionality to monitor one or more bioelectrical signals from a set of bioelectrical electrodes. Processor automatically adjusts a selected one of: (i) a resting threshold; and (ii) a switch threshold that is greater than the resting threshold based at least in part on a trend of the bioelectrical signal. The processor determines whether an amplitude of the bioelectrical signal is less than the resting threshold. In response to determining that the amplitude is less than the resting threshold, the processor determines whether an amplitude of the bioelectrical signal subsequently is equal to or greater than the switch threshold. In response to determining that the bioelectrical signal is greater than the switch threshold, the processor triggers the user interface device with a switch signal. The present disclosure illustrates various techniques and configurations to enable a series of dynamic workflows for the selection and presentation of content from an information system relevant to activities of a human user. The dynamic workflows used with the NeuroNode as described herein enable the integration of user interfaces and user communication platforms to achieve relevant and timely communication among users and others and related actions. The dynamic workflows described herein further may be integrated with social networks and portable communication mediums to provide additional availability and delivery of content to users in a variety of settings.

This document describes methods and devices for using electrical stimulation to control physiological functions such as breathing of patients suffering from respiratory impairment. For example, this document describes methods and devices for generating effective breaths and airway protection by determining times and depths of breaths in accordance with physiological demand, and coordinating respiratory muscle stimulation with the breaths to control breathing.

This document describes methods and devices for using electrical stimulation to control physiological functions such as breathing of patients suffering from respiratory impairment. For example, this document describes methods and devices for generating effective breaths and airway protection by determining times and depths of breaths in accordance with physiological demand, and coordinating respiratory muscle stimulation with the breaths to control breathing.

A controller includes a memory that stores instructions and a processor that executes the instructions. The instructions cause the controller to execute a process that includes receiving sensor data from a first sensor and a second sensor. The sensor data includes a time-series observation representing a first activity and a second activity. The controller generates models for each activity involving progressions through states indicated by the sensor data from each sensor. The controller receives from each sensor additional sensor data including a time-series observation representing the first activity and the second activity. The controller determines likelihoods that the models generated a portion of the additional sensor data and calculates a pair-wise distance between each sensor-specific determined likelihood to obtain calculated distances. The calculated distances for each sensor are grouped and a relevance of each sensor to each activity is determined by executing a regression model using the grouped calculated distances.

A program causes a computer to execute a process of acquiring motion information relating to motion of respiratory muscles or accessory respiratory muscles from a detection sensor that detects the motion information and action potential information relating to the respiratory muscles or the accessory respiratory muscles from an electromyogram sensor that acquires the action potential information, a process of detecting an abnormality in a respiratory system disease on the basis of the acquired motion information and action potential information, and a process of outputting information on the abnormality when the abnormality is detected.

A program causes a computer to execute a process of acquiring motion information relating to motion of respiratory muscles or accessory respiratory muscles from a detection sensor that detects the motion information and action potential information relating to the respiratory muscles or the accessory respiratory muscles from an electromyogram sensor that acquires the action potential information, a process of detecting an abnormality in a respiratory system disease on the basis of the acquired motion information and action potential information, and a process of outputting information on the abnormality when the abnormality is detected.