A wearable sensor apparatus comprises a motion sensor configured to sense two or three dimensional movement and orientation of the sensor and a vibration sensor configured to sense acoustic vibrations. The apparatus includes means for attaching the motion sensor and the vibration sensor to a body. The sensor apparatus enables long term monitoring of mechanomyographic muscle activity in combination with body motion for a number of applications.

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 system comprising a plurality of neuromuscular sensors, each of which is configured to record a time-series of neuromuscular signals from a surface of a user's body; and at least one computer hardware processor programmed to perform: applying a source separation technique to the time series of neuromuscular signals recorded by the plurality of neuromuscular sensors to obtain a plurality of neuromuscular source signals and corresponding mixing information; providing features, obtained from the plurality of neuromuscular source signals and/or the corresponding mixing information, as input to a trained statistical classifier and obtaining corresponding output; and identifying, based on the output of the trained statistical classifier, and for each of one or more of the plurality of neuromuscular source signals, an associated set of one or more biological structures.

A method, comprising applying a source separation technique to the time series of neuromuscular signals recorded by the plurality of neuromuscular sensors to obtain a plurality of neuromuscular source signals and corresponding mixing information; aligning the plurality of neuromuscular source signals to a plurality of template neuromuscular source signals, the aligning comprising: determining, using a cost function, a distance between first features and second features, the first features obtained from the plurality of neuromuscular source signals and/or the corresponding mixing information, the second features obtained from the template neuromuscular source signals and/or corresponding template mixing information; and identifying, based on results of the aligning and for each of one or more of the plurality of neuromuscular source signals, an associated set of one or more biological structures.

The present invention relates to a device, system and method for automatic detection and progression monitoring of dysphagia of a subject, in particular after stroke. The device comprises a signal input for obtaining a mastication signal indicating mastication of the subject, an audio signal representing sounds generated by the subject and a swallowing signal indicating swallowing of the subject, a signal processor for detecting mastication abnormalities from said mastication signal, mastication-to-swallow abnormalities from said mastication signal and said swallowing signal, and aspiration episodes from said audio signal and said swallowing signal, a dysphagia detector for detecting onset and/or progression of dysphagia from detected mastication abnormalities, mastication-to-swallow abnormalities and aspiration episodes and for generating a dysphagia signal indicating information regarding dysphagia of the subject, and a signal output for outputting said dysphagia signal.

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.

Wearable devices and methods of using the same for inputting ambient conditions and the movement and bio-signals of a user to an external device are provided. The wearable device can include a finger sensor and a wrist sensor, wherein each includes an accelerometer, a gyroscope, and a compass. The compass can be a magnetometer. The wrist sensor can also include a power source, a computer, and a touchscreen.

Multi-modal sensing relating to joint acoustic emission and joint bioimpedance. Custom-design analog electronics and electrodes provide high resolution sensing of bioimpedance, microphones and their front-end electronics for capturing sound signals from the joints, rate sensors for identifying joint motions (linear and rotational), and a processor unit for interpretation of the signals. These components are packed into a wearable form factor, which also encapsulates the hardware required to minimize the negative effects of motion artifacts on the signals.

A new non-invasive tool for cartilage assessment, exercise and sports management, and prevention of osteoarthritis is provided. In various embodiments, cartilage condition is assessed using audible signals from joints. Assessment test results are used to provide feedback regarding joint stress and friction that is related to physiological or pathological loads. Data obtained from audible signals are processed to provide an index that can be interpreted by a user or third parties. The index is useful as a baseline for exercise practices, training routines, wellness programs, or rehabilitation protocols.