A brain-machine interface (BMI) system includes one or more implantable or non-implantable sensors, each being configured to detect or measure electrophysiological activity of motor units and to transmit an electrophysiological activity signal; one or more wearable apparatuses configured to be worn by or attached to a user and configured to receive and process the one or more electrophysiological activity signals transmitted by the sensors, and configured to transmit the processed signals to one or more processing units, which are configured to produce control signals based on the received processed signals using one or more machine learning algorithms; and one or more effectors configured to receive the control signals and configured to transduce the control signals into a haptic, tactile, chemical, mechanical, auditory, visual, and/or electrical stimuli so as to provide feedback to a user and/or to control operation of an external effector.

A brain-machine interface (BMI) system includes one or more implantable or non-implantable sensors, each being configured to detect or measure electrophysiological activity of motor units and to transmit an electrophysiological activity signal; one or more wearable apparatuses configured to be worn by or attached to a user and configured to receive and process the one or more electrophysiological activity signals transmitted by the sensors, and configured to transmit the processed signals to one or more processing units, which are configured to produce control signals based on the received processed signals using one or more machine learning algorithms; and one or more effectors configured to receive the control signals and configured to transduce the control signals into a haptic, tactile, chemical, mechanical, auditory, visual, and/or electrical stimuli so as to provide feedback to a user and/or to control operation of an external effector.

A training simulator for intraoperative neuromonitoring (IONM) systems includes channels where at least one of the channels is identified as an active stimulation channel and a subset of the rest of the channels is identified as reference or pick up sites. Channels of the subset having signal data that exceed a predefined threshold are retained for further processing, while channels with signal data that do not exceed the threshold are eliminated from further reporting. Response data for the remaining channels are generated in advance of a future time when the response would occur. The generated data is time stamped and stored for display at a time window when requested by the system.

Devices, systems and methods provide forms of asymptomatic diaphragmatic stimulation (ADS) therapy that affect pressures within the intrathoracic cavity, including: 1) dual-pulse ADS therapy, during which a first ADS pulse is delivered during a diastolic phase of a cardiac cycle and a second ADS pulse is delivered during a systolic phase, 2) paired-pulse ADS therapy, during which a first ADS pulse is delivered, closely followed by a second ADS pulse, with the second ADS pulse functioning to extend or enhance a phase of a transient, partial contraction of the diaphragm, and 3) multiple-pulse ADS therapy, during which a stream of ADS pulses is delivered, wherein the time between pulses is based on heart rate. Devices, systems and methods also monitor electromyography (EMG) activity of the diaphragm relative to baseline activity to assess the health of a diaphragm subject to ADS therapy and to adjust ADS therapy parameters or sensing parameters.

Devices, systems and methods provide forms of asymptomatic diaphragmatic stimulation (ADS) therapy that affect pressures within the intrathoracic cavity, including: 1) dual-pulse ADS therapy, during which a first ADS pulse is delivered during a diastolic phase of a cardiac cycle and a second ADS pulse is delivered during a systolic phase, 2) paired-pulse ADS therapy, during which a first ADS pulse is delivered, closely followed by a second ADS pulse, with the second ADS pulse functioning to extend or enhance a phase of a transient, partial contraction of the diaphragm, and 3) multiple-pulse ADS therapy, during which a stream of ADS pulses is delivered, wherein the time between pulses is based on heart rate. Devices, systems and methods also monitor electromyography (EMG) activity of the diaphragm relative to baseline activity to assess the health of a diaphragm subject to ADS therapy and to adjust ADS therapy parameters or sensing parameters.

According to some aspects, a device is presented herein that is configured to be located underneath an eyelid and worn by a user for treating dry eye. The device includes a first surface configured to face a portion of a sclera of the eye, and a second surface configured to face an eyelid and to be completely covered by the eyelid. In some embodiments, the device further includes a plurality of stimulation electrodes proximal to the first surface, wherein the plurality of stimulation electrodes is configured to stimulate the sclera. The device further includes an energy storage element coupled to the plurality of stimulation electrodes. The energy storage element is configured to supply power to the plurality of stimulation electrodes. The device further includes a processor configured to control a supply of energy from the energy storage element to the plurality of stimulation electrodes to stimulate the sclera.

A physical activity monitoring device is provided that includes a muscle activity sensor, an acceleration sensor, and a computation unit. The acceleration sensor can be attached to a leg and can output a first monitoring signal corresponding to an activity of the leg. The muscle activity sensor can be attached to the leg and can output a second monitoring signal corresponding to an activity of a muscle and/or a tendon of the leg. The computation unit can detect the load condition of the body of a wearer or user that includes the body position of the wearer or user by using the first monitoring signal and the second monitoring signal.

A method for diagnosing a joint condition includes in one embodiment: creating a 3d model of the patient specific bone; registering the patient's bone with the bone model; tracking the motion of the patient specific bone through a range of motion; selecting a database including empirical mathematical descriptions of the motion of a plurality actual bones through ranges of motion; and comparing the motion of the patient specific bone to the database.

An integrated circuit, such as included as a portion of a sensor node, can include a regulator circuit having an input coupleable to an energy harvesting transducer. The integrated circuit can include a wireless receiver circuit coupled to the regulator circuit and configured to wirelessly receive at least enough operating energy to establish operation of the sensor node without requiring the energy harvesting transducer. The integrated circuit can include a digital processor circuit coupled to the regulator circuit and a power management processor circuit. The digital processor circuit or one or more other circuits can include a subthreshold operational mode established by the power management processor circuit based on the selected energy consumption level. For example, establishing the subthreshold operational mode can include adjusting or selecting a supply voltage so as to establish subthreshold operation of a field effect transistor (FET) in the digital processor circuit or other circuits.

A personal monitoring device has a sensor assembly configured to sense physiological signals upon contact with a user's skin. The sensor assembly produces electrical signals representing the sensed physiological signals. A converter assembly, integrated with, and electrically connected to the sensor assembly, converts the electrical signals generated by the sensor assembly to a frequency modulated physiological audio signal having a carrier frequency in the range of from about 6 kHz to about 20 kHz.