A robotic surgical system in which the system applies a scaling factor between user input from a user input device and corresponding movements of the robotic manipulator. Scaling factors may be applied or adjusted based on detected conditions such as the type of instrument being manipulated, detected distance between multiple instruments being manipulated, user biometric parameters.

The present invention relates to a wearable sensor device and method for a thermogenic and/or psychogenic stress response of a subject. The wearable sensor device comprises a first electrode (11) configured to contact a first non-glabrous skin part of the subject, a second electrode (12) configured to contact a second non-glabrous skin part of the subject, a third electrode (13) configured to contact a glabrous skin part of the subject, and a skin conductance sensor (14) configured to provide a voltage signal between a positive input terminal (141) and a negative input terminal (142) and to measure a skin conductance signal at an output terminal (143). A switching arrangement (15) switches connections between the first to third electrodes and the positive and negative input terminals of the skin conductance sensor between at least three switching states. A processing unit (15) determines a 10 thermogenic and/or sychogenic stress response from the measured skin conductance signals during the at least three switching states.

The present invention relates to a wearable sensor device and method for a thermogenic and/or psychogenic stress response of a subject. The wearable sensor device comprises a first electrode (11) configured to contact a first non-glabrous skin part of the subject, a second electrode (12) configured to contact a second non-glabrous skin part of the subject, a third electrode (13) configured to contact a glabrous skin part of the subject, and a skin conductance sensor (14) configured to provide a voltage signal between a positive input terminal (141) and a negative input terminal (142) and to measure a skin conductance signal at an output terminal (143). A switching arrangement (15) switches connections between the first to third electrodes and the positive and negative input terminals of the skin conductance sensor between at least three switching states. A processing unit (15) determines a 10 thermogenic and/or sychogenic stress response from the measured skin conductance signals during the at least three switching states.

Provided are a brain stimulation system, method, apparatus and storage medium based on artificial intelligence. The system includes: a plurality of brain stimulation terminals and a cloud platform. The cloud platform is configured to, with artificial intelligence algorithm especially machine learning and deep learning, generate multi-dimensional psychological big data using physiological data and psychological state evaluation parameters gotten from the plurality of brain stimulation terminals and established models of algorithm for disease diagnosis. The brain stimulation terminal is configured to analyze the physiological data and psychological state evaluation parameters of a target subject, measure a mental state of the target subject, obtain brain stimulation parameters required for the target subject according to the mental state, and generate corresponding non-invasive brain stimulation for the target subject according to the brain stimulation parameters based on the multi-dimensional big data through the artificial intelligence algorithm.

Provided are a brain stimulation system, method, apparatus and storage medium based on artificial intelligence. The system includes: a plurality of brain stimulation terminals and a cloud platform. The cloud platform is configured to, with artificial intelligence algorithm especially machine learning and deep learning, generate multi-dimensional psychological big data using physiological data and psychological state evaluation parameters gotten from the plurality of brain stimulation terminals and established models of algorithm for disease diagnosis. The brain stimulation terminal is configured to analyze the physiological data and psychological state evaluation parameters of a target subject, measure a mental state of the target subject, obtain brain stimulation parameters required for the target subject according to the mental state, and generate corresponding non-invasive brain stimulation for the target subject according to the brain stimulation parameters based on the multi-dimensional big data through the artificial intelligence algorithm.

A method for uterine activity monitoring may include: acquiring a plurality of signals from a plurality of sensors during uterine activity; processing the plurality of signals to extract a plurality of uterine electrical activity characteristics; analyzing the plurality of uterine electrical activity characteristics; and classifying the uterine activity as one of: a preterm labor contraction, a labor contraction, a Braxton-Hicks contraction, and a state of no contraction. A method of assessing over time a pre-term birth risk of a pregnant female may include: calculating a baseline pre-term birth risk score based on a user input; acquiring, over time, a signal from a sensor; analyzing the signal to extract a parameter of interest, such that the parameter of interest comprises a physiological parameter; and calculating an instant pre-term birth risk score based, at least in part, on the parameter of interest and the user input.

The wearable article (200) comprises a sensing component. The electronics module (100) is removably coupled to the wearable article (200). The electronics module comprises a housing and a processor disposed within the housing (101). An interface element (121, 123) interfaces with the sensing component so as to receive signals from the sensing component and provide the same to the processor. A sensor (105) is disposed within the housing (101). The sensor (105) monitors a property of the environment external the electronics module (100) through the housing (101). The housing (101) is constructed such that the sensor (105) has line of sight through the housing (101).

Treating kenosis may comprise the following steps: measuring the electrodermal activity of a person by means of an EDA sensor; assessing, on the basis of the electrodermal activity measured, whether the person is currently affected by kinetosis; generating electrical pulses at an electrode in contact with the person's skin in order to treat the kinetosis on the basis of the assessment as to whether the person is currently affected by kinetosis. The sensor and the electrode may be integrated in a device that can be worn on the person's body.

A mobility augmentation system assists a user's movement by determining a corresponding electrical stimulation for the movement. A wearable stimulation array includes sensors, electrodes, an electrode multiplexer, and a controller that executes the mobility augmentation system. The sensors measure movement data, and the mobility augmentation system applies a movement model to the measured movement data. The model can determine different electrical actuation instructions depending on the movement stimulated. For example, to stimulate a knee flexion, the movement model output enables a first set of the electrodes to operate as cathodes and a second set of electrodes to operate as anodes. To stimulate a knee extension, the first set of electrodes can be enabled to operate as anodes and a third set of electrodes as cathodes. The user can provide feedback of the applied stimulation, which the system can use to retrain the model and optimize the stimulation to the user.

A mobility augmentation system assists a user's movement by determining a corresponding electrical stimulation for the movement. A wearable stimulation array includes sensors, electrodes, an electrode multiplexer, and a controller that executes the mobility augmentation system. The sensors measure movement data, and the mobility augmentation system applies a movement model to the measured movement data. The model can determine different electrical actuation instructions depending on the movement stimulated. For example, to stimulate a knee flexion, the movement model output enables a first set of the electrodes to operate as cathodes and a second set of electrodes to operate as anodes. To stimulate a knee extension, the first set of electrodes can be enabled to operate as anodes and a third set of electrodes as cathodes. The user can provide feedback of the applied stimulation, which the system can use to retrain the model and optimize the stimulation to the user.