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.

In one embodiment of the invention, an interactive vital signs scanning method is disclosed including concurrently scanning for a plurality of vital signs with a portable vital signs scanner; detecting movement of the portable vital signs scanner during the scanning for the plurality of vital signs; and determining a measure of quality of the scanning for the plurality of vital signs with the portable vital signs scanner. In another embodiment, a method of improving the quality of vital signs data is disclosed including concurrently sensing data from a plurality of vital signs sensors over a period of time with a portable vital signs scanner; determining a plurality of vital sign values for a respective plurality of vital signs in response to the sensed data from the plurality of vital signs sensors over the period of time; and fusing at least two vital sign values of the plurality of vital sign values for the respective plurality of vital signs.

Examples described herein include defibrillators or other medical equipment that may employ hidden Markov models to classify cardiac rhythms in ECG signals. Hidden Markov models may additionally or instead be used to determine presence of a chest compression from the thoracic impedance signal. Classification of cardiac rhythms may be used to determine when to deliver a shock to a patient. Other applications are also described.

The system and method of the present application includes a physiological sensor and a motion sensor connected to a patient. The physiological sensor detects patient connection to the sensor and collects a physiological signal while connected. When the physiological sensor is disconnected, the motion sensor data is analyzed. Patterns of sensor connection and patient movement typical for nurse initiated removal compared to accidental or patient initiated removals are created. The alarm protocol may be modified if the disconnection is due to patient movement. The detected movement patterns may include movement measurements that are close to the sensor that detects how the actual disconnection happens, or general movement information for the patient such as whether the patient has been still or has moved before the sensor gets disconnected. By using this information to classify the reason of the sensor removal, a more relevant alarm may be generated.

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.

Ruptured Abdominal Aortic Aneurysms (AAA) cause a large number of deaths annually. Ruptures occur even in people who are already diagnosed with AAA and are being monitored. The reason is that the interval between tests is too long because of the need to visit a pathological facility with imaging equipment. It is preferable to estimate the progress of AAA frequently, once detected, in a non-invasive manner, preferably at the subject's home, without the need for the subject to visit a pathological facility. A device is disclosed for detecting a state of a vascular pathology of a subject, comprising a sensor signal unit (103) for providing a signal representative of a blood volume in a body part of a subject, a comparator (107) for comparing the sensor signal with a reference signal, and a user interface (109) for conveying a result based on the comparison to a user of the device.

A heart monitoring system for a person includes one or more wireless nodes; and wearable appliance in communication with the one or more wireless nodes, the appliance monitoring vital signs.

A physiological signal processing system for a physiological waveform that includes a cardiovascular signal component provides a variable high pass filter that is responsive to the physiological waveform, and that is configured to high pass filter the physiological waveform in response to a corner frequency that is applied. A heart rate metric extractor is responsive to the variable high pass filter and is configured to extract a heart rate metric from the physiological waveform that is high pass filtered. A corner frequency adjuster is responsive to the heart rate metric extractor and is configured to determine the corner frequency that is applied to the variable high pass filter, based on the heart rate metric that was extracted. Analogous methods may also be provided.

A system and method for determining physiological parameters based on electrical impedance measurements is provided. One method includes obtaining electrical measurement signals acquired from a plurality of transducers coupled to a surface of an object and spatially pre-conditioning the obtained electrical measurement signals. The method also includes performing multiple-input-multiple-output (MIMO) analog to information conversion (AIC) of the spatially pre-conditioned electrical measurement signals to correlate the spatially pre-conditioned electrical measurement signals to separate the electrical measurement signals.

An arousal-level determining apparatus includes a generating unit, a calculating unit, an identifying unit, and a determining unit. The generating unit generates heartbeat-interval variation data, which indicates changes in heartbeat interval, on the basis of heartbeat signals indicating subject's heartbeats. The calculating unit applies a band-pass filter, which allows passage of a certain range of frequencies, to each frequency band in the heartbeat-interval variation data while changing the frequency band, and calculates spectral density with respect to each frequency band applied with the band-pass filter. The identifying unit identifies a feature point corresponding to a spectral density peak in the calculated spectral densities in the frequency bands. The determining unit determines subject's arousal level on the basis of the identified feature point.