Abstract: Systems, apparatus, and method(s) are provided for automatically detecting and/or estimating one or more clinical biomarker(s) of a subject. Sensor data is received from one or more sensor(s) associated with the subject. The sensor data is using a first set of machine learning (ML) model(s) configured to extract portions of sensor data relevant for estimating one or more clinical biomarker(s) of the subject. The extracted portions of sensor data from sensors associated with the subject are processed to determine one or more clinical biomarkers. The processing of the received extracted portions of sensor data includes using a second set of ML model(s) configured to estimate the one or more clinical biomarker(s) of the subject. The portions of sensor data may comprise the first set of ML model(s) being configured to extract segments of sensor data and classify the segments of sensor data based on one or more clinical biomarker components for use in the estimation of one or more clinical biomarkers of interest.

Abstract: A system for controlling a cardiac system of a subject comprising: a plurality of sensors arranged to detect physiological activity in a subject and produce physiological signals corresponding to the detected physiological activity; at least one controller arranged to receive the physiological signals and to process the physiological signals using at least one model to determine at least one output signal; and a plurality of neural stimulators arranged to receive the at least one output signal and to provide neural stimulation to the nervous system of the subject based on the at least one output signal.

Abstract: A neural control system comprises an input controller arranged to receive neural data regarding neural signals relating to a bodily state of a subject from at least one neural sensor, at least one machine learning means using at least one machine learning model to process the received neural data to determine at least one output signal required to achieve a desired value of the bodily state, and means arranged to send the determined output signal to at least one output device, whereby the neural control system forms a first control loop providing closed loop control of the bodily state.

Abstract: An active-constraint robot (4), particularly for surgical use, is controlled by means of a series of motorized joints to provide a surgeon with real-time tactile feedback of an operation in progress. The robot system holds, in memory, a series of constraint surfaces beyond which it would be dangerous for the surgeon to go, and a resistive force is applied to the cutting tool (14) as the surgeon approaches any such surface. To improve the overall quality of feedback experienced by the surgeon, the resistive force applied to the cutting tool (14) depends upon the point of intersection (I) between the force factor applied by the surgeon and the surface. Further improvements are achieved by adjusting the tangential resistive force, when the tool is adjacent to the surface, in dependence upon the surrounding shape of the surface.

Abstract: An apparatus and method for the position of a surgical robot, prior to its use for computer-aided surgery, has a probe (37) on the end of a robot, allowing the location of the robot to be registered with respect to a bone (33) to be cut. Prior to the procedure, the location of the robot is also registered with respect to a bone clamp (10) by touching the end of the probe (37) into depressions (15) formed in the clamp surface. If the robot needs to be moved, with respect to the bone, its position can be accurately re-registered by touching the probe tip back into some of the depressions (15) in the clamp. The system allows easy re-registration without the need either for expensive tracking systems or the use of fiducial markers inserted into the patient's bone.

Abstract: An active-constraint robot (4), particularly for surgical use, is controlled by means of a series of motorised joints to provide a surgeon with real-time tactile feedback of an operation in progress. The robot system holds, in memory, a series of constraint surfaces beyond which it would be dangerous for the surgeon to go, and a resistive force is applied to the cutting tool (14) as the surgeon approaches any such surface. To improve the overall quality of feedback experienced by the surgeon, the resistive force applied to the cutting tool (14) depends upon the point of intersection (I) between the force factor applied by the surgeon and the surface. Further improvements are achieved by adjusting the tangential resistive force, when the tool is adjacent to the surface, in dependence upon the surrounding shape of the surface.