Methods, apparatuses, and computer readable mediums for waistband monitoring analysis for a user are provided. In a particular embodiment, a waistband monitoring device is configured to generate a motion pattern based on motion data from a motion sensor coupled to the waistband monitoring device; receive physiological data generated by a physiological sensor coupled to the waistband monitoring device, wherein the physiological data indicates at least one vital sign of the user; identify an activity of the user that corresponds to the motion data and the physiological data; and generate an analysis of the user's performance of the identified activity based on the motion data and the physiological data.

Concepts presented herein relate to an interface module that can be electrically coupled to an electrical stimulation generator, a radio frequency generator and an instrument. A selection module is coupled to the interface module and operates in a first mode to deliver electrical stimulation signals from the electrical stimulation generator to the instrument and in a second mode to deliver radio frequency signals from the radio frequency generator to the instrument.

A method is provided for monitoring and managing muscle activity and soft tissue loading. The method includes providing to a subject a plurality of sensors for measuring muscle activity and soft tissue loading levels; directing the subject to undertake a program of exercise; measuring muscle activity and soft tissue loading during the program of exercise; comparing the measured muscle activity and soft tissue loading levels against calibrated muscle activity and soft tissue loading levels for the subject; and alerting the subject if the comparison of measured muscle activity and soft tissue loading levels against calibrated muscle activity and soft tissue loading levels indicates that a desirable level of muscle activity and/or soft tissue loading is being exceeded.

Wearable systems and methods to comprehensively analyze physical activity of a user for training and/or therapeutic purposes, by analyzing multiple channels of data about both muscle activity, using non-invasive surface electromyography (sEMG), and associated motion from that muscle activity, using inertial measurement units (IMU), are disclosed.

Neurophysiological instruments and techniques are improved through various enhancements. Stimulation of an instrument is possible while it is advancing into the spine or elsewhere, alerting the surgeon to the first sign the instrument or device (screw) may be too near a nerve. A directional probe helps surgeons determine the location of the hole in the pedicle. Electrically insulating sleeves prevent shunting into the soft tissues. According to a different improvement, the same probe to be used to stimulate different devices, such as screws and wires. Electrical impulses may be recorded from non-muscle regions of the body, including the spine and other portions of the central nervous system as opposed to just the extremities.

The present invention is directed to systems and methods for monitoring characteristics of a subject. A system according to an exemplary embodiment of the invention includes a sensor subsystem including at least one respiratory sensor disposed proximate to the subject and configured to detect a respiratory characteristic of the subject, wherein the sensor subsystem is configured to generate and transmit at least one respiratory signal representing the respiratory characteristic, and at least one physiological sensor disposed proximate to the subject and configured to detect a physiological characteristic of the subject, wherein the sensor subsystem is configured to generate and transmit at least one physiological signal representing the physiological characteristic, and a processor subsystem in communication with the sensor subsystem, the processor subsystem being configured to receive at least one of the at least one respiratory signal and the at least one physiological signal.

A computer-assisted surgery system may have a robotic arm including a surgical tool and a processor communicatively connected to the robotic arm. The processor may be configured to receive, from a neural monitor, a signal indicative of a distance between the surgical tool and a portion of a patient's anatomy including nervous tissue. The processor may be further configured to generate a command for altering a degree to which the robotic arm resists movement based on the signal received from the neural monitor; and send the command to the robotic arm.

Described is a system for system for gait intervention and fall prevention. The system is incorporated into a body suit having a plurality of distributed sensors and a vestibulo-muscular biostim array. The sensors are operable for providing biosensor data to the analytics module, while the vestibulo-muscular biostim array includes a plurality of distributed effectors. The analytics module is connected with the body suit and sensors and is operable for receiving biosensor data and analyzing a particular user's gait and predicting falls. Finally, a closed-loop biostim control module is included for activating the vestibulo-muscular biostim array to compensate for a risk of a predicted fall.

The present invention relates to a device for measuring bladder pressure, comprising: a measurement rod inserted into the abdomen of a user to measure both abdomen pressure and bladder pressure; and a body unit provided to be worn on the waist of the user and electrically connected to the measurement rod so as to calculate and store urinary detrusor pressure measured by subtracting a measurement abdomen pressure from a measurement bladder pressure measured by the measurement rod.

Embodiments of the present disclosure disclose a fall-off detection method for a wearable device and a wearable device. The method includes collecting physiological parameter information and activity information of a wearer of the wearable device; when an abnormality is detected in the physiological parameter information of the wearer of the wearable device and a time of the abnormality exceeds a preset time, determining that the wearable device falls off from the wearer; when it is determined that the wearable device falls off from the wearer, determining, according to the collected physiological parameter information and activity information of the wearer of the wearable device, a scenario mode that the wearer is in; and sending, according to the scenario mode that the wearer is in, an indication corresponding to the scenario mode.