A monitoring apparatus for a human body includes a node network with at least one motion sensor and at least one acoustic sensor. A processor is coupled to the node network, and receives motion information and acoustic information from the node network. The processor determines from the motion information and the acoustic information a source of acoustic emissions within the human body by analyzing the acoustic information in the time domain to identify an event envelope representing an acoustic event, determining a feature vector related to the event envelope, calculating a distance between the feature vector and each of a set of predetermined event silhouettes, and identifying one of the predetermined event silhouettes for which the distance is a minimum.

A system is provided for monitoring patients with dysphagia, or swallowing impairments, that monitors muscle movement during intensive swallowing exercises to provide adjuvant visual feedback from surface electromyography.

A method and device for detecting phrenic nerve stimulation (PNS) in, or using, a cardiac medical device. A test signal sensitive to contraction of a diaphragm of a patient may be sensed and signal artifacts of the test signal within each of a first window of the test signal prior to a predetermined cardiac signal and a second window of the test signal subsequent to the predetermined cardiac signal may be determined. The PNS beat criteria may be evaluated, for example, using the test signal, which may be a heart sounds signal.

A device includes an EMG processing application operable with a processing module to receive an output signal from EMG testing, wherein the output signal represents electrical activity of at least one muscle. The EMG processing application is operable to process the output signal to detect at least one type of waveform of a plurality of types of waveforms from the output signal and display the detected at least one type of waveform.

A monitoring apparatus for a human body includes a node network with at least one motion sensor and at least one acoustic sensor. A processor is coupled to the node network, and receives motion information and acoustic information from the node network. The processor determines from the motion information and the acoustic information a source of acoustic emissions within the human body by analyzing the acoustic information in the time domain to identify an event envelope representing an acoustic event, determining a feature vector related to the event envelope, calculating a distance between the feature vector and each of a set of predetermined event silhouettes, and identifying one of the predetermined event silhouettes for which the distance is a minimum.

Real-time head and spine alignment, position and other physiological parameters are detected to measure human being health and wellness status. A wearer is reminded to actively correct spinal curvatures and the system may further interfere if head and spinal mal-position or other injury or disease parameters are detected by the system's sensor devices. The system includes wearable universal sensor modules that are attached and detached easily from receiver units having different formats for different body locations depending on their physiological functions. The system is designed to analyze, by artificial intelligence, data from each universal sensor module and provide accurate feedback that can be used by health professionals to monitor remote individual health status and notify a wearer to prevent and correct mal-position or injury or disease status.

A sensing device for sensing perinatal maternal uterine activity and fetal heart activity is provided. The sensing device includes a body with an electromechanical system and a housing, a passive acoustic system with a plurality of microphones and an acoustic waveguide, an attachment component, an accelerometer, a signal analysis system including a microcontroller unit, and a wireless transceiver. The signal analysis system is configured to process biopotential signals and acoustic signals detected by the passive acoustic system and to reduce motion artifacts from the signals using the accelerometer.

Devices and methods for providing sensory feedback during an exercise are disclosed. An exertion target is set, for a user performing the exercise, based on a self-calibration that estimates the user's ability using signal amplitudes of surface electromyography (sEMG) data, wherein the exertion target includes a target signal amplitude of muscle contractions to be reached during the exercise. sEMG data are received from a measurement device attached to the user as the user performs the exercise. Upon processing the sEMG data, sensory feedback is generated at a computing device operated by the user, wherein the sensory feedback has an intensity proportional to the user's exertion level as the user performs the exercise, and wherein the sensory feedback changes over a course of the exercise in dependence on a duration that the user maintains a muscle contraction at or above the target signal amplitude, and the change in sensory feedback is configured to encourage the user to prolong the duration.

A wearable device for detecting eating episodes uses a contact microphone to provide audio signals through an analog front end to an analog-to-digital converter to digitize the audio and provide digitized audio to a processor; and a processor configured with firmware in a memory to extract features from the digitized audio. A classifier determines eating episodes from the extracted features. In embodiments, messages describing the detected eating episodes are transmitted to a cell phone, insulin pump, or camera configured to record video of the wearer's mouth.

A method and device for detecting phrenic nerve stimulation (PNS) in, or using, a cardiac medical device. A test signal sensitive to contraction of a diaphragm of a patient may be sensed and signal artifacts of the test signal within each of a first window of the test signal prior to a predetermined cardiac signal and a second window of the test signal subsequent to the predetermined cardiac signal may be determined. The PNS beat criteria may be evaluated, for example, using the test signal, which may be a heart sounds signal.