Equipment for self-care of patients with chronic pain, through inducing, intensifying and maintaining their own frissons and where multisensory and multimodal stimuli are used to achieve it; musical, visual, aromatic and vibrotactile and cold are applied on the cutaneous surface of the spine. In addition, the method for using them through perceptual learning is presented. The equipment includes a computer and a computer system with a music and video player, lighting, presentation of aromas and a closed hydraulic circuit with a hydraulic actuator.

An apparatus may be configured for documenting a code blue scenario when adhered to the chest of a subject undergoing resuscitation by sensing and transmitting information associated with the code blue scenario. Such information may include one or more of vital signs of the subject during resuscitation, information associated with chest movements of the subject during resuscitation, and audio information from an environment of the subject during resuscitation. A computing platform that is separate and distinct from the apparatus may provide code blue documentation conveying information related to the vital signs of the subject and derived from the audio information from the environment of the subject during resuscitation.

The present disclosure pertains to a system configured to determine one or more parameters of chest wall oscillation therapy for a subject. The system includes a wearable garment configured to provide percussion to one or more parts of a lung of a subject. The wearable garment includes: percussion excitation elements configured to produce the percussion; and sensors configured to generate output signals conveying information related to a response of the one or more parts of the lung to the percussion. The system includes a control unit configured to determine frequency and energy density information for the sounds made by the one or more parts of the lungs caused by the percussion, the frequency and energy density information determined based on the output signals; and determine the one or more parameters of the chest wall oscillation therapy based on the frequency and energy density information.

One aspect of the present invention is different embodiments of legs and feet massage apparatuses for providing massage effects to the legs and/or feet of a user. Another aspect is different embodiments of hands and arms massage apparatuses for providing massage effects to the arms and/or hands of the user. Additional aspect is an armrest sliding adjustment apparatus. A further aspect is a bicep and tricep panel sliding adjustment apparatus. Additional aspect is a chair that includes at least one of the following devices, apparatuses and systems described in this application: any of the legs and feet massage apparatuses; any of the hands and arms massage apparatuses; an armrest sliding adjustment apparatus; a bicep and tricep panel sliding adjustment apparatus; a neck and shoulder massage system; an integrated smart medical device; at least one health monitoring device or system; a virtual reality device; and a touchscreen-based control system.

A medical monitoring and treatment device that includes a therapy delivery interface, a plurality of therapy electrodes coupled to the therapy delivery interface, a plurality of electrocardiogram sensing electrodes to sense electrocardiogram signals of a patient, a sensor interface to receive the electrocardiogram signals and digitize the electrocardiogram signals, and at least one processor coupled to the sensor interface and the therapy delivery interface to analyze the digitized electrocardiogram signals, to detect a cardiac arrhythmia based on the digitized electrocardiogram signals, and to control the therapy delivery interface to apply electrical therapy to the patient based upon the detected cardiac arrhythmia. The at least one processor is further configured to analyze a frequency domain transform of the digitized electrocardiogram signals, to determine a metric indicative of a metabolic state of a heart of the patient, and to accelerate or delay application of the electrical therapy based upon the metric.

A computer-implemented system may include a stair-climbing machine configured to be manipulated by a user while the user performs a treatment plan, an interface comprising a display configured to present information associated with the treatment plan, and processing structure configured to receive, from one or more data sources, information associated with the user, wherein the information comprises one or more risk factors associated with a cardiac-related event; generate, using one or more trained machine learning models, the treatment plan for the user, wherein the treatment plan is generated based on the information associated with the user, and the treatment plan comprises one or more exercises associated with managing the one or more risk factors in order to reduce a probability that a cardiac intervention will occur; and transmit the treatment plan to cause the stair-climbing machine to implement the one or more exercises.

Methods and apparatus for increasing cerebral blood flow for improving the flow of blood to the brain during and/or following an ischaemic stroke. The apparatus includes a plurality of inflatable cuffs to be positioned, in use, around a respective limb of a patient. Once inflated, the cuffs exert a pressure upon the limb to reduce blood flow to the limb below the point at which the cuff is positioned. Reducing blood flow to the limbs causes an increase in blood flow to the brain, and will therefore improve stroke outcomes. The apparatus includes a control module for controlling inflation and deflation of each cuff. The control module detects, measures and monitors cerebral blood flow and uses these measurements to control inflation and deflation of each cuff such that cerebral blood flow is maintained above a baseline level during treatment of the patient.

A system for assisting with a cardiopulmonary resuscitation (CPR) treatment being administered to a patient. In one aspect, a system for assisting with a cardiopulmonary resuscitation (CPR) treatment being administered to a patient includes a sensor for determining a parameter of the patient (e.g., indicative of a blood flow or pressure waveform), and one or more processors configured for receiving input from the sensor, determining, based on the input from the sensor, whether a rate of chest compressions administered in the CPR treatment should be changed, and providing an indication to a user that the rate of chest compressions should be changed.

A computer-implemented method is disclosed. The method includes receiving, at a computing device, a first treatment plan designed to treat an invasive surgical-related health issue of a user. The first treatment plan comprises at least two exercise sessions that, based on the invasive surgical-related health issue, enable the user to perform an exercise at different exertion levels. Next, while the user uses the electromechanical machine to perform the first treatment plan, receiving, at the computing device, data from sensors configured to measure the data associated with the invasive surgical-related health issue and transmitting the data. One or more machine learning models are used to generate a second treatment plan. The second treatment plan modifies at least one exertion level, and the modification is based on a standardized measure comprising perceived exertion, the data, and the invasive surgical-related health issue. The method additionally includes receiving the second treatment plan.

Systems including an elliptical machine and a processing device. The processing device may be configured to receive, before or while a user operates the elliptical machine, one or more messages pertaining to the user or a use of the elliptical machine by the user. The processing device may be also configured to determine whether the one or more messages were received by the processing device. In response to determining that the one or more messages were not received by the processing device, the processing device may be configured to determine, via one or more machine learning models, one or more actions to perform. The one or more actions may include at least one of initiating a telecommunications transmission, stopping operation of the elliptical machine, and modifying one or more parameters associated with the operation of the elliptical machine.