Abstract: A system for assessing grip security, the system comprising: a contact surface having at least a first contact surface region and a second contact surface region, the first contact surface region being configured to resist slip less than the second contact surface region, a sensor for detecting slip in the first contact region.

Abstract: A system for estimating friction, the system including a contact surface including a plurality of protrusions extending from a base surface, the contact surface further including a first contact surface region and a second contact surface region, the first contact surface region being configured to resist slip less than the second contact surface region, and a sensor arrangement configured for detecting displacement of at least three of the plurality of protrusions in three different dimensions to measure a three-dimensional force applied to the at least three plurality of protrusions at a moment of slippage of the first contact surface region.

Abstract: Disclosed is a cardio-pulmonary health monitoring apparatus. The apparatus comprises a contactless motion sensor configured to generate one or more movement signals representing bodily movement of a patient during a monitoring session; a processor; and a memory storing program instructions configured to cause the processor to carry out a method of processing the one or more movement signals. The method comprises extracting one or more sleep disordered breathing features from the one or more movement signals, and predicting whether a clinical event is likely to occur during a predetermined prediction horizon based on the one or more sleep disordered breathing features.

Abstract: A controller or processor(s) implements detection of respiratory related conditions, such as asynchrony, associated with use of a respiratory treatment apparatus or ventilator. Based on data derived from sensor signals associated with the respiratory treatment, the detector may evaluate a feature set of detection values to determine whether or not an asynchrony occurs in a breath of the patient's respiratory cycle such as by comparing the values against a set of thresholds. Different events may also be identified based on the particular feature set and threshold(s) involved in the detection processing. Automated determination of feature sets may also be implemented to design different asynchrony event classifiers. The methodologies may be implemented by computers or by respiratory treatment apparatus.

Abstract: A sleep monitoring system includes an ECG device (2) and a respiration inductance plethysmogram (3) which monitor cardiac activity and physical (ribcage) respiration respectively and feed representative signals to a digital data processor. Operations (5-9) process the beat interval data, while in a second thread, operations (20-24) independently process the amplitude modulation of the ECG data caused by the respiratory motion of the subject. The inductance plethysmogram device (3) provides an input to the processor which represents respiration as directly monitored independently of the ECG. Operations (30-34) process this direct respiration data independently and in parallel, in a third thread. All extracted features are fed to a classifier which in step (10) combines selected combinations of features to make decisions in real time.

Abstract: A controller or processor(s) implements detection of respiratory related conditions, such as asynchrony, associated with use of a respiratory treatment apparatus or ventilator. Based on data derived from sensor signals associated with the respiratory treatment, the detector may evaluate a feature set of detection values to determine whether or not an asynchrony occurs in a breath of the patient's respiratory cycle such as by comparing the values against a set of thresholds. Different events may also be identified based on the particular feature set and threshold(s) involved in the detection processing. Automated determination of feature sets may also be implemented to design different asynchrony event classifiers. The methodologies may be implemented by computers or by respiratory treatment apparatus.

Abstract: A system for assessing grip security, the system comprising: a contact surface having at least a first contact surface region and a second contact surface region, the first contact surface region being configured to resist slip less than the second contact surface region, a sensor for detecting slip in the first contact region.

Abstract: Disclosed is a cardio-pulmonary health monitoring apparatus. The apparatus comprises a contactless motion sensor configured to generate one or more movement signals representing bodily movement of a patient during a monitoring session; a processor; and a memory storing program instructions configured to cause the processor to carry out a method of processing the one or more movement signals. The method comprises extracting one or more sleep disordered breathing features from the one or more movement signals, and predicting whether a clinical event is likely to occur during a predetermined prediction horizon based on the one or more sleep disordered breathing features.

Abstract: A sleep monitoring system includes an ECG device (2) and a respiration inductance plethysmogram (3) which monitor cardiac activity and physical (ribcage) respiration respectively and feed representative signals to a digital data processor. Operations (5-9) process the beat interval data, while in a second thread, operations (20-24) independently process the amplitude modulation of the ECG data caused by the respiratory motion of the subject. The inductance plethysmogram device (3) provides an input to the processor which represents respiration as directly monitored independently of the ECG. Operations (30-34) process this direct respiration data independently and in parallel, in a third thread. All extracted features are fed to a classifier which in step (10) combines selected combinations of features to make decisions in real time.

Abstract: Disclosed is a cardio-pulmonary health monitoring apparatus. The apparatus comprises a contactless motion sensor configured to generate one or more movement signals representing bodily movement of a patient during a monitoring session; a processor; and a memory storing program instructions configured to cause the processor to carry out a method of processing the one or more movement signals. The method comprises extracting one or more sleep disordered breathing features from the one or more movement signals, and predicting whether a clinical event is likely to occur during a predetermined prediction horizon based on the one or more sleep disordered breathing features.

Abstract: A controller or processor(s) implements detection of respiratory related conditions, such as asynchrony, associated with use of a respiratory treatment apparatus or ventilator. Based on data derived from sensor signals associated with the respiratory treatment, the detector may evaluate a feature set of detection values to determine whether or not an asynchrony occurs in a breath of the patient's respiratory cycle such as by comparing the values against a set of thresholds. Different events may also be identified based on the particular feature set and threshold(s) involved in the detection processing. Automated determination of feature sets may also be implemented to design different asynchrony event classifiers. The methodologies may be implemented by computers or by respiratory treatment apparatus.

Abstract: A controller or processor(s) (1112) implements detection of respiratory related conditions, such as asynchrony, associated with use of a respiratory treatment apparatus (1102) or ventilator. Based on data derived from sensor signals associated with the respiratory treatment, the detector may evaluate a feature set of detection values to determine whether or not an asynchrony occurs in a breath of the patient's respiratory cycle such as by comparing the values against a set of thresholds. Different events may also be identified based on the particular feature set and threshold(s) involved in the detection processing. Automated determination of feature sets may also be implemented to design different asynchrony event classifiers. The methodologies may be implemented by computers or by respiratory treatment apparatus.

Abstract: Disclosed is a cardio-pulmonary health monitoring apparatus. The apparatus comprises a contactless motion sensor configured to generate one or more movement signals representing bodily movement of a patient during a monitoring session; a processor; and a memory storing program instructions configured to cause the processor to carry out a method of processing the one or more movement signals. The method comprises extracting one or more sleep disordered breathing features from the one or more movement signals, and predicting whether a clinical event is likely to occur during a predetermined prediction horizon based on the one or more sleep disordered breathing features.

Abstract: A controller or processor(s) (1112) implements detection of respiratory related conditions, such as asynchrony, associated with use of a respiratory treatment apparatus (1102) or ventilator. Based on data derived from sensor signals associated with the respiratory treatment, the detector may evaluate a feature set of detection values to determine whether or not an asynchrony occurs in a breath of the patient's respiratory cycle such as by comparing the values against a set of thresholds. Different events may also be identified based on the particular feature set and threshold(s) involved in the detection processing. Automated determination of feature sets may also be implemented to design different asynchrony event classifiers. The methodologies may be implemented by computers or by respiratory treatment apparatus.