Abstract: Methods, apparatus, and processor-readable storage media for determining candidates for circuit breaker patterns in cloud applications using machine learning techniques are provided herein.

Abstract: A monitoring system (1) comprises sensors (102) adapted to be worn by a user, and, a processor (101, 302) linked with the sensor. The processor receives sensor data and processes this data to determine user posture data including data indicative of vertical distance between level of the user's heart and ankle (?h, Vd 1, Vd2, Vd3). Based on the posture data together with a value for degree of user chronic venous insufficiency and/or blood density, generate an estimate of user static venous pressure while the user is static, without calf muscle pump activity. The processor (101, 302) also processes the sensor data to determine if there is calf muscle pump activity, and generates an estimate of user active venous pressure according to the static venous pressure estimate, rate of calf muscle activity, and a value for degree of user chronic venous insufficiency. The processor (101, 302) may generate the venous pressure estimate in real time, and may control an NMES device accordingly.

Abstract: A monitoring system comprises sensors adapted to be worn by a user, and, a processor linked with the sensor. The processor receives sensor data and processes this data to determine user posture data including data indicative of vertical distance between level of the user's heart and ankle. Based on the posture data together with a value for degree of user chronic venous insufficiency and/or blood density, generate an estimate of user static venous pressure while the user is static, without calf muscle pump activity. The processor also processes the sensor data to determine if there is calf muscle pump activity, and generates an estimate of user active venous pressure according to the static venous pressure estimate, rate of calf muscle activity, and a value for degree of user chronic venous insufficiency. The processor may generate the venous pressure estimate in real time, and may control an NMES device accordingly.

Abstract: A surface NMES stimulation device the device has a control processor, a voltage regulator connected to a high voltage supply, a bridge of control switches, and electrodes or electrode terminals connected in the bridge to receive drive signals via the control switches. The processor directs a drive scheme for the bridge control switches in which charge build-up on a user's skin is allowed to dissipate to ground in an inter-pulse interval. An isolation capacitor isolates the high voltage supply from the electrodes, and charging of the capacitor only occurs if a switch allows it. Sensors senses supply voltage and bridge current and the processor monitors there to determine further stimulation and faults.

Abstract: A surface NMES stimulation device the device has a control processor, a voltage regulator connected to a high voltage supply, a bridge of control switches, and electrodes or electrode terminals connected in the bridge to receive drive signals via the control switches. The processor directs a drive scheme for the bridge control switches in which charge build-up on a user's skin is allowed to dissipate to ground in an inter-pulse interval. An isolation capacitor isolates the high voltage supply from the electrodes, and charging of the capacitor only occurs if a switch allows it. Sensors senses supply voltage and bridge current and the processor monitors there to determine further stimulation and faults.

Abstract: A monitoring system comprises sensors adapted to be worn by a user, and, a processor linked with the sensor. The processor receives sensor data and processes this data to determine user posture data including data indicative of vertical distance between level of the user's heart and ankle. Based on the posture data together with a value for degree of user chronic venous insufficiency and/or blood density, generate an estimate of user static venous pressure while the user is static, without calf muscle pump activity. The processor also processes the sensor data to determine if there is calf muscle pump activity, and generates an estimate of user active venous pressure according to the static venous pressure estimate, rate of calf muscle activity, and a value for degree of user chronic venous insufficiency. The processor may generate the venous pressure estimate in real time, and may control an NMES device accordingly.