An artificial tongue is provided. The artificial tongue includes tongue tissue formed by a bioprinting process, an antenna embedded within the tongue tissue and configured to wirelessly receive power from an external device, a processor embedded within the tongue tissue and operatively coupled to the antenna, and a piezoelectric element embedded within the tongue tissue and operatively coupled to the processor. The piezoelectric element is configured to deform in response to an applied electric bias, and the processor is configured to cause the electric bias to be applied to the piezoelectric element based on the power received by the antenna.

Automated detection of head and/or head-affecting body impact events in data collected is performed using instrumented mouthguard devices. For example, in some embodiments the present disclosure relates to training and operation of an impact classifier system, which is configured to identify head affecting impacts from time-series data collected by an instrumented mouthguard device. Some embodiments relate to a two-stage method for processing impacts, including a first stage in which a set of data is classified by such an impact classifier system, and a second stage whereby impacts classified as head affecting impacts are designated a numerical value based on a predefined scale.

A system for ambient mobility testing including: at least one sensor configured to collect data associated with an individual's movement; an analysis module configured to analyze the collected data to determine a set of timed-up-and-go (TUG) actions of a TUG test and determine results of a complete TUG test; and a reporting module configured to provide the results of the TUG test. A method for ambient mobility testing, the method including: collecting data associated with an individual's movement, via at least one sensor; analyzing the collected data to determine timed-up-and-go (TUG) actions of a TUG test; determining results of a complete TUG test; and providing the results of the TUG test.

A method for evaluating a posture of a rider riding a bicycle includes: continuously receiving a plurality of sensor datasets, each of the sensor datasets being associated with a specific time instance and includes data generated by an inertial measurement set and an electrical signal sensor set; determining a plurality of top time instances and a plurality of bottom time instances, and establishing a number of riding periods based on the plurality of top time instances and the plurality of bottom time instances; and for each of the riding periods, generating an evaluation result with respect to a number of sensor datasets received within the riding period.

A wearable mechano-acoustic sensor for continuous cardiorespiratory monitoring, and methods of making and using the same. The sensor includes a diaphragm with a chamber and a channel connected to the chamber, a plurality of electrodes including at least an anode and a cathode extending into the channel, and a liquid electrolyte solution that fills the chamber and channel. When the diaphragm is attached to a user's chest, mechano-acoustic movement from the chest cause the diaphragm to move, pushing the electrolyte solution across the electrodes. A voltage is applied to the anode and an electrochemical current is determined by the flux from the anode to the cathode by the modulation of the electrolyte solution across the electrodes and cardiorespiratory signals are measured from the electrochemical currents.

The disclosure provides a dynamic interaction-oriented subject's limb time-varying stiffness identification method and device. The method includes: the combination of subject's limb displacement and measured force data or the combination of angle and measured torque data is collected; based on the time-varying dynamic system constructed based on a second-order impedance model, the linear parameter varying method is utilized to substitute the time-varying impedance parameters and reconstruct the restoring force/torque expression; iterative identification is performed on variable weights, dynamic interaction force/torque, and restoring force/torque by using time-varying dynamic parameters based on the dynamic interaction force/torque expression expanded from basis function; the time-varying stiffness is solved by using variable weights and dynamic interaction force/torque according to expression with substituted the time-varying impedance parameters. The disclosure not only improves the accuracy of the time-varying stiffness identification technology but also expands the application scenarios of the time-varying stiffness identification technology.

A mouth guard senses impact forces and determines if the forces exceed an impact threshold. If so, the mouth guard notifies the user of the risk for injury by haptic feedback, vibratory feedback, and/or audible feedback. The mouth guard system may also remotely communicate the status of risk and the potential injury. The mouth guard uses a local memory device to store impact thresholds based on personal biometric information obtained from the user and compares the sensed forces relative to those threshold values. The mouth guard and its electrical components on the printed circuit board are custom manufactured for the user such that the mouth guard provides a comfortable and reliable fit, while ensuring exceptional performance.

A method and system of training a machine learning neural network (MLNN) in anatomical degenerative conditions in accordance with anatomical dynamics. The method comprises receiving, in a first input layer of the MLNN, from a millimeter wave (mmWave) radar sensing device, a first set of mmWave radar point cloud data representing a first gait characteristic of a subject in motion, comprising an arm swing velocity, receiving, in a second layer, a second set of mmWave radar point cloud data representing a second gait characteristic comprising a measure of dynamic postural stability, the input layers being interconnected with an output layer of the MLNN via an intermediate layer, and training a MLNN classifier in accordance with a classification that increases a correlation between a degenerative condition of the subject as generated at the output layer and the sets of mmWave point cloud data.

An intelligent dog collar for monitoring physiological parameters of a dog, comprising: a movement sensor unit comprising an accelerometer and/or a gyrometer, wherein the movement sensor unit is configured to detect raw movement signals of the dog collar, a storage module storing a trained neural network, the neural network being configured to determine a physiologic information into raw movement signals detected by the movement sensor unit, a processing unit connected to the movement sensor unit and configured to operate the trained neural network, a memory configured to store the identified physiologic information, an interface for transmitting to a communication device the identified physiologic information.

Improvements in ingestible electronics with the capacity to sense physiologic and pathophysiologic states have transformed the standard of care for patients. Yet despite advances in device development, significant risks associated with solid, non-flexible gastrointestinal transiting systems remain. Here, we disclose an ingestible, flexible piezoelectric device that senses mechanical deformation within the gastric cavity. We demonstrate the capabilities of the sensor in both in vitro and ex vivo simulated gastric models, quantified its key behaviors in the GI tract by using computational modeling, and validated its functionality in awake and ambulating swine. Our piezoelectric devices can safely sense mechanical variations and harvest mechanical energy inside the gastrointestinal tract for diagnosing and treating motility disorders and for monitoring ingestion in bariatric applications.