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.

An apparatus configured for application to a surface of a body, the apparatus comprising: an array of mechanomyography sensors spatially distributed across a substrate, each mechanomyography sensor configured to detect mechanomyography signals from the body to which the apparatus is applied; and a pressure bias system configured to provide a variation in contact pressure of the mechanomyography sensors to the body surface to receive mechanomyography signals at different levels of applied contact pressure.

A leadless implantable medical device (IMD) and method of using same are provided. The IMD comprises: a housing, a fixation element, electrodes configured to sense electrical cardiac activity (CA) signals over a period of time, an HS sensor configured to sense HS signals over the period of time, memory to store specific executable instructions, and one or more processors. The one or more processors and method: identify a characteristic of interest (COI) of a heartbeat from the CA signals, calculate a center of mass (COM) for at least one HS based on the HS signals to obtain a corresponding at least one HS COM, and calculate at least one of a therapy-related (TR) delay or a sensing-related (SR) blanking interval (BI) based on the at least one HS COM.

A system and method for monitoring heart function based on heart sounds (HS) is provided. The system includes electrodes configured to sense electrical cardiac activity (CA) signals over a period of time. An HS sensor is configured to sense HS signals over the period of time. The system includes memory to store specific executable instructions and includes one or more processors that, when executing the specific executable instructions, is configured to: identify a characteristic of interest (COI) of a heartbeat from the CA signals. The processors overlay a HS search window onto an HS segment of the HS signals based on the COI from the CA signals and calculate a center of mass (COM) for at least one of S1 or S2 HS based on the HS segment of the HS signals within the search window to obtain a corresponding at least one of S1 COM or S2 COM. The processors calculate at least one of an electromechanical activation time (EMAT) or a systolic interval (SI) based on the at least one of S1 COM or S2 COM and record the at least one of the EMAT or SI.

The problem to be solved is to provide a new method and system for appropriately detecting a joint state from an acoustic of a joint. The present method comprises the step of obtaining, using a bio-acoustic sensor, an acoustic signal emitted by a joint during a time period that at least includes a first motion period wherein the joint changes from a first state to a second state, a pause period of the joint and a second motion period wherein the joint changes from the second state to the first state, the step of converting the obtained acoustic signal into time trend data that at least shows a relationship between an acoustic signal intensity and time, and the step of setting, from the time trend data, a basic threshold value regarding the acoustic signal intensity to calculate first acoustic information based on the basic threshold value, characterized in that the first acoustic information is an indicator of the joint state.

Apparatus for providing transcutaneous electrical nerve stimulation (TENS) therapy to a user, said apparatus comprising: a stimulation unit for electrically stimulating at least one nerve of the user; a sensing unit for sensing body movement of the user to analyze body movement activity type and activity duration; an application unit for providing mechanical coupling between said sensing unit and the user's body; and a feedback unit for at least one of (i) providing the user with feedback in response to said analysis of said body movement activity type and activity duration of the user, and (ii) modifying the electrical stimulation provided to the user by said stimulation unit in response to said analysis of said body movement activity type and activity duration of the user.

Systems and methods for assessing a patient's risk of renal dysfunction are described. A system may include sensor circuits to sense physiological signals and processors to generate signal metrics from the physiological signals. The system may generate a primary renal risk indication using a first signal metric, and a secondary renal risk indication using at least a second signal metric. The system may generate a composite renal risk indication and estimate a glomerular filtration rate or a chronic kidney disease stage using at least the primary and secondary risk indications. The composite renal risk indication, which indicative of a degree of renal dysfunction, may be presented to a clinician, or provided to a detector for detecting worsening heart failure.

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 system and method for detecting brain concussion includes detecting and measuring of acceleration at one or more points on a subject's head. Sensors, which can be accelerometers placed against the head, detect and measure natural motions of the patient's head due to blood flow in the brain and resultant movement of tissue in the brain. The acceleration data are then analyzed, including as to frequency of motions of the skull at the subject location in a frequency range of about 1 to 20 Hz. An observation is then made, as compared with data corresponding to non-concussion, of a change in frequency response pattern exhibited when accelerations are plotted as a function of time or frequency, to identify probable concussion if the frequency response pattern indicates concussion. Preferably the observation and comparison are made by a computer using an algorithm.

An illustrative system includes two wearable devices configured to be worn near a jaw on opposite sides of a user's head, each device comprising: at least one microphone, at least two accelerometers, at least one loudspeaker, wherein the system is configured to process data acquired by the accelerometers and by the microphones to: detect chewing, and output an audible notification via the loudspeakers to notify the user to brush the user's teeth after chewing is no longer detected.