Patent classifications
G16H50/00
Peristaltic pump
A peristaltic pump having at least first, second, and third stages is provided. The peristaltic pump includes a plunger, inlet and outlet valves, a spring, and an actuator. The plunger actuates toward and away from a tube, the inlet valve is upstream of the plunger, the outlet valve is downstream of the plunger, the spring biases the plunger toward the tube, and the actuator mechanically engages and disengages from the plunger. In the first stage, the inlet valve is opened and the plunger is actuated from the tube, in the second stage, the inlet valve is closed, the plunger is actuated toward the tube, and the actuator is mechanically disengaged from the plunger, and in the third stage, the outlet valve is opened. In the third stage or in a fourth stage, the actuator actuates the plunger toward the tube to discharge fluid downstream past the outlet valve.
Peristaltic pump
A peristaltic pump having at least first, second, and third stages is provided. The peristaltic pump includes a plunger, inlet and outlet valves, a spring, and an actuator. The plunger actuates toward and away from a tube, the inlet valve is upstream of the plunger, the outlet valve is downstream of the plunger, the spring biases the plunger toward the tube, and the actuator mechanically engages and disengages from the plunger. In the first stage, the inlet valve is opened and the plunger is actuated from the tube, in the second stage, the inlet valve is closed, the plunger is actuated toward the tube, and the actuator is mechanically disengaged from the plunger, and in the third stage, the outlet valve is opened. In the third stage or in a fourth stage, the actuator actuates the plunger toward the tube to discharge fluid downstream past the outlet valve.
IMAGE-BASED CIRCULAR PLOT RECOGNITION AND INTERPRETATION
A device includes software instructions for a circular plot analysis agent and at least one circular plot definition. The circular plot analysis agent obtains a digital image of a circular plot, detects a perimeter of the circular plot within the digital image, detects a plurality of edges within the perimeter, identifies a set of endpoints on the perimeter as a function of the plurality of edges, generates a plot descriptor from the set of endpoints, and initiates a transaction with a second device as a function of the plot descriptor.
Generation and delivery of customized content programs
Provided are systems, methods, and computer-readable medium for obtaining specific information about one or more psychological conditions. The information is obtained in a manner such that a second response is based on a first response. Once the information has been obtained, a customized content program for responding to the one or more psychological conditions may be generated.
Generation and delivery of customized content programs
Provided are systems, methods, and computer-readable medium for obtaining specific information about one or more psychological conditions. The information is obtained in a manner such that a second response is based on a first response. Once the information has been obtained, a customized content program for responding to the one or more psychological conditions may be generated.
Algorithm-based optimization for knee arthroplasty procedures
A method for optimizing a knee arthroplasty surgical procedure includes receiving pre-operative data comprising (i) anatomical measurements of the patient, (ii) soft tissue measurements of the patient's anatomy, and (iii) implant parameters identifying an implant to be used in the knee arthroplasty surgical procedure. An equation set is selected from a plurality of pre-generated equation sets based on the pre-operative data. During the knee arthroplasty surgical procedure, patient-specific kinetic and kinematic response values are generated and displayed using an optimization process. The optimization process includes collecting intraoperative data from one or more surgical tools of a computer-assisted surgical system, and using the intraoperative data and the pre-operative data to solve the equation set, thereby yielding the patient-specific kinetic and kinematic response values. A visualization is then provided of the patient-specific kinetic and kinematic response values on the displays.
Machine learning for amyloid and tau pathology prediction
Method and apparatus for predicting amyloid beta (Aβ) and phosphorylated tau (p-tau) biomarker levels in the cerebrospinal fluid (CSF) of patients. Embodiments include determining current values for a plurality of easily-measurable attributes of a first patient. Embodiments include analyzing data associated with a cohort of patients having known measurements of Aβ and p-tau biomarker levels, including determined values for the plurality of easily-measureable attributes. Embodiments include generating a predicted value for Aβ and/or p-tau biomarker levels for the first patient. Embodiments include generating a risk of the first patient developing AD at a future time, generating a probability of a patient's predicted rate of decline, and/or generating a probability of a patient's age at the onset of dementia, based on the predicted values for Aβ and/or p-tau biomarker levels.
Machine learning for amyloid and tau pathology prediction
Method and apparatus for predicting amyloid beta (Aβ) and phosphorylated tau (p-tau) biomarker levels in the cerebrospinal fluid (CSF) of patients. Embodiments include determining current values for a plurality of easily-measurable attributes of a first patient. Embodiments include analyzing data associated with a cohort of patients having known measurements of Aβ and p-tau biomarker levels, including determined values for the plurality of easily-measureable attributes. Embodiments include generating a predicted value for Aβ and/or p-tau biomarker levels for the first patient. Embodiments include generating a risk of the first patient developing AD at a future time, generating a probability of a patient's predicted rate of decline, and/or generating a probability of a patient's age at the onset of dementia, based on the predicted values for Aβ and/or p-tau biomarker levels.
PLETHYSMOGRAPHIC RESPIRATION RATE DETECTION
A plethysmographic respiration processor is responsive to respiratory effects appearing on a blood volume waveform and the corresponding detected intensity waveform measured with an optical sensor at a blood perfused peripheral tissue site so as to provide a measurement of respiration rate. A preprocessor identifies a windowed pleth corresponding to a physiologically acceptable series of plethysmograph waveform pulses. Multiple processors derive different parameters responsive to particular respiratory effects on the windowed pleth. Decision logic determines a respiration rate based upon at least a portion of these parameters.
PLETHYSMOGRAPHIC RESPIRATION RATE DETECTION
A plethysmographic respiration processor is responsive to respiratory effects appearing on a blood volume waveform and the corresponding detected intensity waveform measured with an optical sensor at a blood perfused peripheral tissue site so as to provide a measurement of respiration rate. A preprocessor identifies a windowed pleth corresponding to a physiologically acceptable series of plethysmograph waveform pulses. Multiple processors derive different parameters responsive to particular respiratory effects on the windowed pleth. Decision logic determines a respiration rate based upon at least a portion of these parameters.