The invention relates to a system and a method for learning a gesture by a human learner (5), comprising the following steps: equipping said learner (5) with a plurality of motion sensors (6, 7) on a plurality of members that are predetermined in accordance with said gesture to be learned; acquiring biomechanical data provided by said plurality of sensors during a gesture performed by the learner; analyzing said acquired biomechanical data and determining a theoretical correction of the gesture by comparing said biomechanical data of the learner with biomechanical data corresponding to a target gesture; customizing the theoretical correction into a specific correction on the basis of behavior models of the learner derived from a history of biomechanical data acquired for the learner; transmitting said specific correction to the learner; updating said specific correction on the basis of information representing the sensation perceived by the learner when performing the corrected gesture.

Systems and methods for providing a customized exercise protocol to a computing device of a user. As the user performs the exercise protocol, one or more cameras of the computing device can track the user's movements. The user's movement are assessed to determine if proper form and technique is being used.

A smartphone plugin determines the heart rate and the breathing rate of a user, who is either holding the smartphone in his/her hand or who has the smartphone resting on his/her chest when lying in a supine position, using only smartphone accelerometer output data and no external sensors. The smartphone is preloaded with spectral entropy to weight mapping information for each of a plurality of use cases. The plugin performs frequency domain processing on accelerometer output data to determine an estimated heart rate EHR and an estimated breathing rate EBR. The spectral entropy of accelerometer output data is determined, and is used along with an appropriate spectral entropy to weight mapping, to determine an EHR weight for each EHR value and an EBR weight for each EBR value. The weights are used to adjust the EHR and EBR values to generate more accurate heart rate and breathing rate values.

Systems and methods of dynamic IoT device regulation and control can aid in shifting a user's emotional state from a first state of mind to a preferred second state of mind, using the user's biomarker response to device settings. Particularly, an IoT device controller may be embedded within a wearable device that is wirelessly connected to a computing device and one or more IoT devices. Initially, each wearable device can be calibrated, wherein a matrix of sensed user biomarker responses can be generated. In some embodiments, the system continuously monitors user biomarkers to detect which physiological state exists. When the user enters into the first physiological/psychological state, the system can adjust each IoT device to align with the second state. When the system detects that the user biomarker response has not shifted, the system can continuously adjust IoT settings based upon a learning algorithm having monitored user biomarkers as input.

Wearable blood pressure biosensors, systems, and methods for short-term blood pressure predictions are disclosed herein. In some embodiments, a blood pressure prediction system is configured to provide short-term predictions of average blood pressures for future time period(s) or horizon(s) (e.g., for a coming week). The system can include models trained to predict future systolic values, diastolic blood pressure values, and/or trends. The models can be trained on data related to personalized body, health, and/or physical characteristics of the user, e.g., the current or previous blood pressure, amount of sleep, heart rate, blood glucose (BG), activity, weight, etc. In some embodiments, the models can also determine whether the blood pressure of the user will change or remain relatively constant over a period/range of time.

The system and method disclosed utilizes a wearable device to collect data generated pursuant to user's kinesthetic movement. The instant innovation filters and cleans the data, then slices the data into subsets. The subsets are analyzed with a Machine Learning algorithm to identify signal patterns indicative of statistically significant behavioral metrics, and the instant innovation returns insights based in part on relationships between said behavioral metrics. These insights are returned to an observer in the form of a report.

A device for motion sickness reduction. The device operates by providing haptic feedback using transducers that convert electrical signals to a tactile sensation such as pressure, vibration, electrical stimulation, temperature, or airflow. The transducers are located at different locations on the body of a user, and actively change their operation to indicate a direction of motion or rotation to the user through haptic (tactile) feedback. This tactile feedback can be used to reduce motion sickness. In an embodiment, the feedback is improved by deducting motion associated with the user's visual frame of reference from motion associated with the user's vestibular frame of reference.

An intraoral scanner generates 2D images of a dental site and 3D intraoral scans of the dental site. The computing device receives the 2D images of the dental site and the 3D intraoral scans of the dental site from the intraoral scanner, generates a 3D model of the dental site based on the 3D intraoral scans of the dental site, and processes at least one of a) one or more of the 2D images of the dental site, b) one or more of the 3D intraoral scans of the dental site, or c) data from the 3D model of the dental site to identify one or more intraoral areas of interest (AOIs) at the dental site. The computing device determines a dental condition associated with the one or more intraoral AOIs, and determines a manner for scanning the one or more intraoral AOIs.

One aspect of this disclosure relates to a sleep position training device for reducing gastroesophageal reflux during sleep. The training device can comprise an orientation sensor, a stimulus generator and a processing system. The orientation sensor can be configured to output a signal indicative of an orientation of the torso of the person. The stimulus generator can be configured to provide a stimulus to the torso of the person when the torso of the person is in a predetermined torso orientation range in a sleeping position. The stimulus generator can be removably affixable to the torso of the person. The processing system can be configured to receive a first signal from the orientation sensor, the first signal being indicative of an orientation of the torso of the person, and to determine that the orientation is within the predetermined torso orientation range in the sleeping position.

The present invention provides a human-computer interactive rehabilitation system, which can automatically calculate rehabilitation strength suitable for the patient, so that it is not necessary to manually evaluate and adjust the parameter settings in human-computer interactive rehabilitation system when different patients use it. At the same time, the human-machine interactive rehabilitation system and the hospital end can track the rehabilitation status and intervene through the data platform at any time. The platform establishes a cloud community feedback and encouragement mechanism, and immediately transmits the rehabilitation results to the designated barriers of the patients, provides patient encouragement feedback, and strengthens the community interaction and linkage in the medical relationship.