A tissue ablation system may be configured to receive location information indicating locations of at least part of a transducer-based device in a bodily cavity; cause delivery of first tissue-ablative energy during a duration of a first particular time period in accordance with a first energy waveform parameter set at least in response to a first state in which at least part of the location information indicates at least a first rate of movement of the part of the transducer-based device in the bodily cavity; and cause delivery of second tissue-ablative energy during a duration of a second particular time period in accordance with a second energy waveform parameter set at least in response to a second state in which the at least part of the location information indicates at least a second rate of movement of the part of the transducer-based device in the bodily cavity.

Systems and methods are described for determining an intraocular pressure (IOP) of an eye using a contact lens with a magnet placed on the cornea of the eye. A magnetic field is exerted on the magnet of the contact lens, and the magnet is displaced by the magnetic field. The system of the present disclosure determines the deflection of the cornea based on the magnetic displacement of the magnet and determines the IOP of the eye.

A method and system for indicating a breathing pattern is disclosed. The system comprises a plurality of smart wearable devices, a plurality of mobile devices, an application server and a database. The plurality of smart wearable devices is either connected to the mobile devices or directly connected to the application server. The database is connected to the application server to store the data transmitted by the smart wearable device or the mobile device. The plurality of smart wearable devices is capable of monitoring the health index and identifying the variation in health conditions of the user, in real-time and activating a suitable breathing pattern to normalize such medical condition.

An adapter assembly includes a knob housing, an outer tube, an end effector, an articulation link, and a sensor assembly. The end effector is movable from a first position where the end effector is aligned with a longitudinal axis defined by the outer tube, to a second position where the end effector is disposed at an angle relative to the longitudinal axis. Longitudinal translation of the articulation link relative to the outer tube causes the end effector to move from its first position to its second position. The sensor assembly includes a first portion disposed in mechanical cooperation with the articulation link, and a second portion disposed at least partially within the outer tube. The sensor assembly is configured to determine an actual amount of articulation of the end effector based on a distance the articulation link move longitudinally relative to the outer tube.

A medical device comprises a control system, processing modules, and a wire bundle connecting the control system to the processing modules, the wire bundle comprising control lines and data lines. Each processing module is coupled to a respective set of sensors arranged to interface with a biological tissue site, the sensors being configured to capture analog physiological signals generated from the biological tissue site. The control system is configured to generate a control signal on the control lines to initiate a data collection cycle by the processing modules. In response to the control signal, each processing module is configured to perform a respective data collection process which comprises (i) capturing and processing an analog physiological signal on each enabled sensor to generate a data sample for each analog physiological signal captured on each enabled sensor, and (ii) outputting data samples to the control system on the data lines.

A rider state modification system for improving a state of a rider in a vehicle includes a first neural network that operates to classify a state of the vehicle through analysis of information about the vehicle captured by an Internet-of-things device during operation of the vehicle. The rider state modification system further includes a second neural network that operates to optimize at least one operating parameter of the vehicle based on the classified state of the vehicle, information about a state of a rider occupying the vehicle, and information that correlates vehicle operation with an effect on rider state.

A system for measuring an angle of a joint of a user includes a center hub, a first arm, a second arm, a magnet, and a sensor. The center hub includes a first hub and a second hub. The first arm is configured for attachment to a first limb portion of the user at a first outer end and to the first hub at a first inner end. The second arm is configured for attachment to a second limb portion of the user at a second outer end and to the second hub at a second inner end, wherein the first hub is pivotally coupled to the second hub. The magnet is coupled to the second hub. The sensor is disposed in the center hub and configured to detect a rotation of the magnet.

A measurement apparatus is provided, which includes a magnetic sensor array formed by three-dimensionally arranging a plurality of magnetic sensor cells each including a magnetic sensor, and capable of detecting an input magnetic field in three axial directions; a measurement data acquiring section that acquires a plurality of measurement values based on the input magnetic field detected by the magnetic sensor array; a magnetic field calculating section that calculates the input magnetic field based on the measurement values; an error calculating section that calculates a detection error of the input magnetic field, based on the plurality of measurement values and a calculation result obtained by calculating the input magnetic field; and a measurement data selecting section that selects a plurality of measurement values to be used for calculating the input magnetic field by the magnetic field calculating section, from among the plurality of measurement values, based on the detection error.

System and method of deploying a trained machine learning neural network (MLNN) in generating a fall injury condition of a subject. The method comprises receiving, at input layers of the trained MLNN, millimeter wave (mmWave) radar point cloud data representing fall attributes from monitoring the subject via mmWave radar sensing device, the input layers associated with the fall attributes, receiving, at a second set of input layers, personal attributes of the subject associated with ones of the second set of input layers, the first and second sets of input layers interconnected with an output layer of the trained MLNN via intermediate layers, the trained MLNN produced by establishing a correlation between an injury condition of prior subjects and mmWave point cloud data and personal attributes associated with the prior subjects, and generating, at the output layer, the fall injury condition attributable to the subject.

Provided is a sensor device-equipped golf shoes comprising: golf shoes including a left and right pair of a first shoe and a second shoe to be worn by a golfer; and a sensor device attached to the golf shoes. The sensor device includes one or more sensor modules configured to measure sensor data pertaining to at least one of an orientation of the first shoe when the golfer takes a shot, an orientation of the second shoe when the golfer takes a shot, and a positional relationship between the first shoe and the second shoe when the golfer takes a shot.