A method for storing data of photoelectrically synchronous brain activity recording, said method comprising: generating data when a photoelectrically synchronous brain activity detection system is operating; generating from said data a data storage file comprising a basic information data segment, a near-infrared spectrum data segment and a brain electrical activity data segment, and sequentially storing said data segments into a .neg file in binary form according to the above order. The method can store comprehensive test information, flexibly configure the near-infrared and brain electrical measurement information, and realize synchronous storage of near-infrared data and brain electrical data and maintain file version compatibility.

With the pharmaceutical injection system in an embodiment, pharmaceutical injection amount setting conditions for setting a pharmaceutical injection amount are inputted to a portable terminal (300), and the pharmaceutical injection amount setting conditions are transmitted to a health care worker-use information terminal (500). The portable terminal (300) receives the pharmaceutical injection amount set on the basis of the pharmaceutical injection amount setting conditions transmitted from the health care worker-use information terminal (500), and transmits the pharmaceutical injection amount to a pharmaceutical injection device (100). The health care worker-use information terminal (500) receives the pharmaceutical injection amount setting conditions transmitted from the portable terminal (300), and the pharmaceutical injection amount set on the basis of the pharmaceutical injection amount setting conditions is inputted. The health care worker-use information terminal (500) transmits the inputted pharmaceutical injection amount to the portable terminal (300). The pharmaceutical injection device (100) receives the pharmaceutical injection amount transmitted from the portable terminal (300), and a piston drive mechanism (101) is driven on the basis of the pharmaceutical injection amount.

Medical software tools platforms utilize a surgical display to provide access to specific medical software tools, such as medically-oriented applications or widgets, that can assist surgeons or surgical team in performing various procedures. In particular, an endoscopic camera may register the momentary rise in the optical signature reflected from a tissue surface and in turn transmit it to a medical image processing system which can also receive patient heart rate data and display relevant anomalies. Changes in various spectral components and the speed at which they change in relation to a source of stimulus (heartbeat, breathing, light source modulation, etc.) may indicate the arrival of blood, contrast agents or oxygen absorption. Combinations of these may indicate various states of differing disease or margins of tumors, and so forth. Also, changes in temperatures, physical dimensions, pressures, photoacoustic pressures and the rate of change may indicate tissue anomalies in comparison to historic values.

Medical software tools platforms utilize a surgical display to provide access to specific medical software tools, such as medically-oriented applications or widgets, that can assist surgeons or surgical team in performing various procedures. In particular, an endoscopic camera may register the momentary rise in the optical signature reflected from a tissue surface and in turn transmit it to a medical image processing system which can also receive patient heart rate data and display relevant anomalies. Changes in various spectral components and the speed at which they change in relation to a source of stimulus (heartbeat, breathing, light source modulation, etc.) may indicate the arrival of blood, contrast agents or oxygen absorption. Combinations of these may indicate various states of differing disease or margins of tumors, and so forth. Also, changes in temperatures, physical dimensions, pressures, photoacoustic pressures and the rate of change may indicate tissue anomalies in comparison to historic values.

Systems and methods of the present disclosure enable injury prediction using one or more processors for receiving a time-varying signal of sensor measurements from a sensor device associated with a user. The processor(s) generate time windows of the time-varying signal, including a series of the sensor measurements across a predetermined time period, and generate motion features based at least in part on the series of the sensor measurements of the time windows. The processor(s) utilize an injury risk classification machine learning model to predict an injury risk during each time window based at least in part on the motion features. An injury alert message is generated based at least in part on the injury risk being predicted; and transmitting the injury alert message to at least one user computing device.

An apparatus for estimating cardiovascular information includes: a main body; and a strap connected to the main body and formed to be flexible to be wrapped around an object, wherein the main body may include: a pulse wave measurer configured to measure, from the subject, a first pulse wave signal by using a first light of a first wavelength, and a second pulse wave signal by using a second light of a second wavelength, the first wavelength being different from the second wavelength; a contact pressure measurer configured to measure a contact pressure between the object and the pulse wave measurer; and a processor configured to extract a cardiovascular characteristic value based on the first pulse wave signal, the second pulse wave signal, and change in the contact pressure, and estimate cardiovascular information based on the extracted cardiovascular characteristic value.

An apparatus for estimating cardiovascular information includes: a main body; and a strap connected to the main body and formed to be flexible to be wrapped around an object, wherein the main body may include: a pulse wave measurer configured to measure, from the subject, a first pulse wave signal by using a first light of a first wavelength, and a second pulse wave signal by using a second light of a second wavelength, the first wavelength being different from the second wavelength; a contact pressure measurer configured to measure a contact pressure between the object and the pulse wave measurer; and a processor configured to extract a cardiovascular characteristic value based on the first pulse wave signal, the second pulse wave signal, and change in the contact pressure, and estimate cardiovascular information based on the extracted cardiovascular characteristic value.

A method of installing a cable finger accessory in a cable manager includes: positioning a first semi-arcuate clamp section against a section of an extruded support member of the cable manager; positioning a second semi-arcuate clamp section, which is hingedly connected to the first semi-arcuate clamp section, against an opposite side of the section of the extruded support member such that, together, the first and second semi-arcuate generally surround the section of the extruded support member; and fastening a clamp mechanism connected to a distal end of the first semi-arcuate clamp section and adapted to secure the distal end of the first semi-arcuate clamp section against a distal end of the second semi-arcuate clamp section such that the first and second semi-arcuate clamp sections are secured against the section of the extruded support member. The cable finger extends outwardly from at least one of the first and second semi-arcuate clamp sections.

Systems and methods are provided for the non-invasive computation of Pulmonary Artery Pressure (and its components of mean, systolic and diastolic) (PAP) as well as Pulmonary Capillary Wedge Pressure (and its components of mean, A-Wave and V-Wave) (PCWP) using a wearable sensor device. Cardiac acoustic and electrocardiogram sensor signals are obtained and multiple temporal, amplitude-based, and spectral features are extracted from the signals. Extracted features from a subject are used as inputs for pre-trained classification, regression, or advanced machine learning models to provide an accurate computation of PAP and PCWP and their associated component values without surgery.

Immune context scores are calculated for tumor tissue samples using continuous scoring functions. Feature metrics for at least one immune cell marker are calculated for a region or regions of interest, the feature metrics including at least a quantitative measure of human CD3 or total lymphocyte counts. A continuous scoring function is then applied to a feature vector including the feature metric and at least one additional metric related to an immunological biomarker, the output of which is an immune context score. The immune context score may then be plotted as a function of a diagnostic or treatment metric, such as a prognostic metric (e.g. overall survival, disease-specific survival, progression-free survival) or a predictive metric (e.g. likelihood of response to a particular treatment course). The immune context score may then be incorporated into diagnostic and/or treatment decisions.