Aspects of the subject disclosure may include, for example, a device, including: a sensor affixed to an article of clothing; a transducer affixed to the article of clothing; a processing system including a processor; and a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations of: receiving sensor data from the sensor; processing the sensor data; sending the sensor data to a trained machine learning (ML) model having as inputs the sensor data, and providing as output, control data to control the transducer; receiving the control data from the trained ML model; and sending the control data to the transducer to provide therapy to a patient. Other embodiments are disclosed.

A computer-implemented method for detecting onset of a spontaneous breath by a patient coupled to a ventilation system includes receiving, at a processor, an electromyography (EMG) signal from an EMG sensor disposed on the patient. The method also includes pre-conditioning, via the processor, the EMG signal to separate the EMG signal into a plurality of components having EMG information utilizing a set of bandpass filters. The method further includes individually analyzing, via the processor, each component of the plurality of components to detect an onset of the spontaneous breath by the patient. The method still further includes determining, via the processor, the onset of the spontaneous breath by the patient is occurring when at least two components of the plurality of components indicate the onset of the spontaneous breath by the patient.

According to an aspect of an embodiment, a virtual assist device may comprise an agent, an input component, and a communication unit. The agent may be configured to receive an incoming notification and request an input in response to the incoming notification. The input component may be an electroencephalogram (EEG) input component that may be configured to receive the input comprising EEG data and send the input to the agent. The agent may be configured to determine a command based on the input. The communication unit may be configured to cause an action to be performed based on the command.

According to an aspect of an embodiment, a virtual assist device may comprise an agent, an input component, and a communication unit. The agent may be configured to receive an incoming notification and request an input in response to the incoming notification. The input component may be an electroencephalogram (EEG) input component that may be configured to receive the input comprising EEG data and send the input to the agent. The agent may be configured to determine a command based on the input. The communication unit may be configured to cause an action to be performed based on the command.

Automatic electromyography (EMG) electrode selection for robotic devices is disclosed. A plurality of signals from a corresponding plurality of sensors coupled to a skin of a user is received. For each pair of at least some pairs of the plurality of sensors, a sensor pair signature is generated based on differences in signals that are generated by the respective pair of sensors. Each of the sensor pair signatures is compared to a predetermined sensor pair signature to identify a particular pair of sensors. A signal difference between two signals generated by the particular pair of sensors is subsequently utilized to generate a command to drive a motor.

Provided are a physiological signal sensing system and a physiological signal sensing method. The physiological signal sensing system includes a physiological signal sensing apparatus, a variation sensing apparatus, and a signal processing apparatus. The physiological signal sensing apparatus is disposed on a fabric to sense and provide physiological signals of an organism. The physiological signal sensing apparatus includes capacitive coupling devices. The variation sensing apparatus is disposed on the fabric and includes a distance sensing device to sense a distance between the physiological signal sensing apparatus and the organism, and provide a first capacitance variation signal according to the distance. The signal processing apparatus is coupled to the physiological signal sensing apparatus and the variation sensing apparatus to receive the physiological signals and the first capacitance variation signal and correct the physiological signals according to the first capacitance variation signal to obtain corrected physiological signals.

Provided are a physiological signal sensing system and a physiological signal sensing method. The physiological signal sensing system includes a physiological signal sensing apparatus, a variation sensing apparatus, and a signal processing apparatus. The physiological signal sensing apparatus is disposed on a fabric to sense and provide physiological signals of an organism. The physiological signal sensing apparatus includes capacitive coupling devices. The variation sensing apparatus is disposed on the fabric and includes a distance sensing device to sense a distance between the physiological signal sensing apparatus and the organism, and provide a first capacitance variation signal according to the distance. The signal processing apparatus is coupled to the physiological signal sensing apparatus and the variation sensing apparatus to receive the physiological signals and the first capacitance variation signal and correct the physiological signals according to the first capacitance variation signal to obtain corrected physiological signals.

The disclosure includes a biosignal monitoring system for reducing a motion artifact from a biopotential electrical signal input, including a signal processing module, a motion artifact extraction module, and a subtraction module. The motion artifact extraction module and the signal processing module receive the biopotential electrical signal input and the subtraction module receives an extracted signal from an output of the motion artifact extraction module and a biopotential electrical signal from an output of the signal processing module. The subtraction module subtracts the extracted signal from the biopotential electrical signal. The motion artifact extraction module is an analog domain electronic circuit and includes a filter network configured for attenuating differential mode signals of the biopotential electrical signal input from a first frequency, and passing the motion artifact signal from the biopotential electrical signal input up to a second frequency at the output of the motion artifact extraction module.

The disclosure includes a biosignal monitoring system for reducing a motion artifact from a biopotential electrical signal input, including a signal processing module, a motion artifact extraction module, and a subtraction module. The motion artifact extraction module and the signal processing module receive the biopotential electrical signal input and the subtraction module receives an extracted signal from an output of the motion artifact extraction module and a biopotential electrical signal from an output of the signal processing module. The subtraction module subtracts the extracted signal from the biopotential electrical signal. The motion artifact extraction module is an analog domain electronic circuit and includes a filter network configured for attenuating differential mode signals of the biopotential electrical signal input from a first frequency, and passing the motion artifact signal from the biopotential electrical signal input up to a second frequency at the output of the motion artifact extraction module.

A computer-implemented method for pelvic floor feedback. The method includes capturing a strength of action potentials via wireless sensors, the wireless sensors positioned proximate to a pelvic floor of a user. The method also includes transmitting the strength of the action potentials to a mobile device. The method also includes recording the strength of the action potentials on the mobile device.