1713results about "Inertial sensors" patented technology

A non-invasive physiological sensor system implemented as a smart watch or other wearable device includes an emitter for emitting light and a detector for collecting light after the light interacts with a tissue of a wearer. The system can measure light intensity at one or more radial distances to estimate attenuation parameter values that can be used to correct physiological parameter bias across subjects. In addition or alternatively, the system can determine whether a detector is positioned sufficiently proximate to an obstructing tissue (for example, an artery or vein) so as to cause inaccurate measurements by the detector.

A robotic surgical system includes a robotic manipulator configured to perform surgical procedures under direct surgeon control. A surgical camera system captures real-time intraoperative video. An external imaging interface receives multimodal imaging data, including preoperative and intraoperative data from at least one of magnetic resonance imaging (MRI), computed tomography (CT), ultrasound, and fluoroscopy. An artificial intelligence (AI module has a trained neural network and a deep learning model trained on multi-institutional annotated surgical datasets, The AI module is configured to execute one or more of: fuse acquired video and imaging data into temporally and spatially coherent anatomical visualizations; generate continuously updating overlays aligned with the surgical field, with segmented anatomical features; projected tissue boundaries, proximity indicators for instruments, and predictive deformation trends; provide dynamic predictive trend visualization indicating zones of future anatomical complexity or risk; register and align preoperative imaging data with intraoperative imaging data in real time; adapt overlay presentation in response to tissue deformation without actuating the robotic manipulate or; and passively augment visual feedback without initiating any autonomous actuation of surgical instruments.

The invention provides a falling detection method and system based on millimeter wave radar fusion human body postures, and relates to the technical field of falling detection, and the method comprises the steps: collecting human body echo signals, and constructing three-dimensional point cloud data through distance, angle and speed estimation; the point cloud is processed to generate a dynamic sequence, and skeleton features and key point coordinates are extracted in combination with a graph convolutional network. A skeleton connection relation is constructed based on the key points, attitude features are calculated, key change features are extracted through a self-attention mechanism, and the key change features are fused with a point cloud sequence to construct multi-modal features. And the double-branch state recognition network processes the fusion features, and when abnormality is detected, further analysis is carried out through residual attention and space-time diagram convolution, and finally, the falling state is recognized and the risk level is evaluated.

An automated urinary output (“UO”)-measuring system includes a fluid container including a console coupled to one or more ultrasonic sensors and one or more accelerometers. The console includes a processor configured to receive accelerometer values from the one or more accelerometers, to determine an acceleration state of the fluid container, to activate the one or more ultrasonic sensors when the acceleration state of the fluid container is below a threshold, and to measure a volume of fluid in the fluid container when the acceleration state of the fluid container is zero. The system can further include a removable valve coupled to the fluid container and an attachment device including a pocket configured to accommodate the fluid container.

A mobility augmentation system monitors a user's motor intent data and augments the user's mobility based on the monitored motor intent data. A machine-learned model is trained to identify an intended movement based on the monitored motor intent data. The machine-learned model may be trained based on generalized or specific motor intent data (e.g., user-specific motor intent data). A machine-learned model initially trained on generalized motor intent data may be re-trained on user-specific motor intent data such that the machine-learned model is optimized to the movements of the user. The system uses the machine-learned model to identify a difference between the user's monitored movement and target movement signals. Based on the identified difference, the system determines actuation signals to augment the user's movement. The actuation signals determined can be an adjustment to a currently applied actuation such that the system optimizes the actuation strategy during application.

The invention provides a self-supervised continuous 3D hand posture tracking method based on a lightweight inertial measurement unit, and the method specifically comprises the following steps: firstly, obtaining a hand motion signal by using a lightweight consumer-level IMU, and extracting features by using a multi-stage neural network containing a bidirectional long-short term memory (Bi-LSTM) module; the global displacement and orientation of the hand are processed through a wrist posture estimation module, and three-dimensional coordinates of hand skeleton points are solved and calculated by means of a finger motion chain; secondly, a self-supervised completion network is realized by combining a random mask and dual loss, and the features are processed to complete complete attitude reconstruction; and finally, combining network output with kinematics prior, generating a three-dimensional grid with reasonable anatomy, solving the problem that joint deformation is inconsistent with torque, and realizing accurate acquisition of a three-dimensional hand posture. For a dynamic hand motion tracking task, the method can adapt to a sparse IMU deployment scene and realize continuous high-precision tracking; the real-time performance demand of daily interaction can be met, and a low-cost solution can be provided for the fields such as medical rehabilitation and virtual interaction which have strict requirements on attitude precision.

Systems and methods for predicting problem behavior in individuals with developmental and behavior disabilities. A plurality of sensors are configured to collect multimodal data signals of a subject individual including a wearable upper body motion sensing device with a plurality of inertial measurement units (IMUs). An electronic controller is configured to receive output signals from each of IMUs and to model an upper body position of the subject individual based on the output signals from the IMUs. A trained machine-learning model is then applied by providing an input data set that includes multimodal signal data (e.g., including signal data from at least one IMU) and / or features extracted from the multimodal signal data. The machine-learning model is trained to produce as output an indication of whether a precursor to the problem behavior is detected and, in response to detecting the precursor, a notification (or alarm) is generated.

The invention relates to a blood pressure compensation measurement method and device for a motion scene and a smart watch, and the method comprises the steps: generating a lactic acid accumulation mark through fusing a motion acceleration signal and a heart rate signal, extracting a specific frequency band component of a pressure shock wave signal based on the mark, and generating a time domain envelope line; quantifying envelope line main peak width change and frequency domain energy distribution abnormity to generate tension abnormity, and mapping the tension abnormity into a blood vessel volume dynamic change curve; constructing a biomechanical compensation curve in combination with a static blood vessel volume reference, and reconstructing an envelope line for eliminating blood vessel tension interference through frequency domain convolution; and finally, positioning a blood pressure feature point based on the reconstructed envelope differential feature and outputting a compensated blood pressure value. According to the method, a lactic acid metabolism and vascular biomechanical characteristic compensation model is established for the first time, the problem of systematic offset of feature points caused by abnormal vascular tension after movement is solved, and the blood pressure measurement error is remarkably reduced.

The invention relates to a gait monitoring algorithm which is used for a traditional four-foot walking aid, a six-axis gyroscope is installed in the middle of four feet of the walking aid, pressure sensor data is installed at the bottom of a handle and the bottoms of the four feet, and the gait monitoring algorithm comprises a sensor data acquisition module used for acquiring original sensor data related to gaits of a user, acquiring three-axis acceleration information; acquiring triaxial angular velocity information according to gyroscope sensor data; acquiring handle pressure and ground contact pressure information according to data of a pressure sensor; the gait feature extraction system is used for extracting gait features from original sensor data and comprises a gait feature extraction module used for extracting gait feature values from the original sensor data, the gait feature values comprise stride frequency, and the number of steps per second is calculated by analyzing periodic fluctuation of pressure or acceleration; and calculating the distance between the two steps through acceleration integration or the ground contact interval between the left foot and the right foot.

The present invention relates to a method for creating an individualized machine learning inference model. In accordance with the invention, the aforementioned method and a related system involve a motion capture device adapted to be worn by a user. The motion capture device is adapted to acquire measurements, which are used to compute a first estimate of one or more gait parameters. Next, a database is accessed that contains previously collected observations of gait data and the first estimate is compared to the previously collected observations of gait data. Finally, the subset of previously collected observations of gait data that is most informative for the particular user is identified and the individualized machine learning inference model can be developed using the identified subset of previously collected observations of gait data.

A haptic healthcare patient garment. The garment comprises a cloth an inflatable bladder, a radio transceiver, a plurality of sensors, a display, a processor communicatively coupled to the inflatable bladder, to the radio transceiver, to the sensors, and to the display, a memory communicatively coupled to the processor, and an application stored in the non-transitory memory that, when executed by the processor receives vital signs indications from the sensors, presents a plurality of vital signs values on the display, receives a command via the radio transceiver to provide a hug to a patient wearing the haptic healthcare patient garment, in response to receiving the command to provide a hug, causes the inflatable bladder to inflate, and causes the inflatable bladder to deflate, whereby the patient is given a hug by the garment from a remote well-wisher.

A system for acquiring, standardizing and evaluating biomechanical kinematics data of a joint includes a device for acquiring biomechanical kinematics of the joint and processing it in a computer-readable manner. A display device displays visual output to a user. A control unit processes the kinematics and determines a progression of kinematics parameters of the joint with respect to a first joint element with a first joint coordinate system and with respect to a second joint element with a second joint coordinate system. A standardization unit determines, via a predetermined target optimization, a transformation for adjusting an orientation and / or position of the first and / or second joint coordinate system to carry out a standardization, and output an evaluation via the display device. A method is used to acquire, standardize and evaluate biomechanical kinematics data of a joint, and can be executed by a computer with a computer-readable storage medium.

A system obtains high-resolution data of actual over-ground gait rehabilitation interactions through a custom-made wearable system for identifying abnormal gait patterns and the therapists' assistance strategies. The system implements an impedance learning algorithm with feature selection and a goal-directed attractor definition reproduces therapist assistance in a way that integrates clinical insights into the control of lower-limb exoskeletons.

Detection method (50) of an activity of a first eye (30) of a user of a pair of glasses (10) comprising a first (22a) and a second (22b) electrode facing the first eye (30) and configured to detect respective electrostatic charge variations generated by a movement of the first eye (30). The detection method, performed by a control unit (21, 15) of the glasses (10), comprises the steps of: acquiring (S10-S14), through the first (22a) and second (22b) electrodes, a first electrostatic charge variation signal (SQ,1a) indicative of a difference between the electrostatic charge variations; verifying (S16, S18) the presence of one or more blink patterns in the first electrostatic charge variation signal (SQ,1a), each blink pattern being indicative of a respective voluntary blink or involuntary blink; if the first electrostatic charge variation signal (SQ,1a) has said one or more blink patterns, for each blink pattern determining (S20, S22; S20', S22) by the control unit (21, 15) whether said blink pattern is indicative of a respective voluntary blink and detecting a involuntary blink or a voluntary blink.

The invention discloses an intelligent stoma base plate intelligent monitoring system which dynamically predicts the remaining time of leakage by performing multi-dimensional dynamic fusion on humidity, pH, temperature and leakage sensor signals reflecting the state of a microenvironment below a stoma base plate and real-time activity state data reflecting the physical stress of a user. And further determining whether to trigger an alarm or not based on comparison between the predicted residual time of leakage occurrence and a preset alarm threshold value. By means of the mode, the system can accurately recognize and quantify complex early comprehensive symptoms of leakage which vary from person to person, precious processing time is won for patients, and nursing experience and life quality of the patients are remarkably improved.

A handheld device for sonifying electrical signals obtained from a subject is provided. The device can utilize at least one of several operations including (but not limited) digitizing signals from electrodes, adjusting the signals based on accelerometer input, filtering the signals, conditioning the signals according to conditioning parameters, modulating the signal according to sound synthesis parameters, and generating sound from the representations of the signals to accomplish sonification. The device can include an analog-to-digital (A / D) converter to digitize the one or more electrical signals and a processor that receives the one or more digitized electrical signals and produces a representation of an acoustic signal. The device further includes a speaker system that sonifies the representation of the acoustic signal.

The present disclosure provides alert implants that comprise a medical device and an implantable reporting processor (IRP), where one example of such a medical device includes a component for a total knee arthroplasty (TKA) such as a tibial extension, a femoral component for hip replacements, a breast implant, a distal rod for arm or leg breakage repair, a scoliosis rod, a dynamic hip screw, a spinal interbody spacer, and tooling and methods that may be used to form the alert implant, and uses of such alert implants in the health maintenance of patients who receive the implant.

The present invention relates to apparatus for stabilisation of tremors, both physiological and pathological, of parts of the body, especially the hands. We describe an apparatus for reducing effects of tremors on an area of the human body, the apparatus comprising a gyroscope device and an attachment assembly for attachment of the gyroscope device to a location on the human body in the area, wherein the attachment assembly provides a substantially inelastic attachment to the location and comprises a gyroscope mount. The gyroscope mount comprises a substantially stiff first plate having a shape adapted to substantially correspond with a shape of the human body in the location. In preferred embodiments, the attachment assembly further comprises a second plate mountable to the body such that a clamping force is appliable between the first plate and the second plate.

Embodiments include a method of diagnosing gastroparesis or suspected gastroparesis, the method comprising: obtaining data representing a time series of readings from gas sensing apparatus housed within a ingestible capsule device orally ingested by a subjected; processing the readings to detect one or more gastroparesis indicator spikes in the CO2 concentration with respect to time; based on the detected one or more gastroparesis indicator spikes in the CO2 concentration with respect to time, diagnosing gastroparesis or suspected gastroparesis.

The present disclosure relates to methods, systems, and apparatus, including computer programs encoded on computer storage media, for denoising magnetic measurements. An example method includes obtaining, using a plurality of magnetometers of a magnetically unshielded device, noisy magnetic measurement data of a magnetic field at least partially caused by an organ of a subject; obtaining, using one or more inertial measurement units (IMUs) of the magnetically unshielded device, a motional measurement of the plurality of magnetometers; determining cleaned magnetic field data based at least in part on the noisy magnetic measurement data and the motional measurement data; and taking an action based at least in part on the cleaned magnetic field data.

This disclosure is directed to systems and techniques for detecting change in patient health based upon peak and non-peak patient activity data. In some examples, the peak and non-peak patient activity data correspond to one or more peak (time) periods and the one or more non-peak periods, respectively, where at least one peak period and at least one non-peak period corresponds a highest activity level and a lowest activity level, respectively, for a single day. If a change in patient health is detected, the techniques described herein may direct a medical device to generate for display output indicating the detection of the change in patient health.

A system includes one or more sensors to detect activities of a mobile object; and a processor coupled to the sensor and the wireless transceiver to classify sequences of motions into groups of similar postures each represented by a model and to apply the models to identify an activity of the object.

Systems and methods for joint replacement are provided. The systems and methods include a surgical orientation device, a reference sensor device, and at least one orthopedic fixture. The surgical orientation device, reference sensor device, and orthopedic fixtures can be used to locate the orientation of an axis in the body, to adjust an orientation of a cutting plane or planes along a bony surface, or otherwise to assist in an orthopedic procedure(s).

The present description relates generally to methods and systems for power management of a digital (e.g., electronic) stethoscope. In one example, an electronic stethoscope includes a chestpiece configured to be positioned on a patient, the chestpiece including a sensor, wherein the chestpiece further includes a computer processing unit (CPU) operatively coupled to a memory storing instructions that, when executed by the CPU, cause the CPU to automatically activate the electronic stethoscope in response to detecting a touch input via the sensor.

A system for detecting motion disorder of a user of a head-wearable device includes: at least one motion sensor, providing motion data of the user of the head-wearable device; the head wearable device, with a processing unit for processing the motion data provided by the at least motion sensor and with a memory unit for storing the processed motion data. The processing unit has a processor adapted to perform the following: i) analyzing the motion data and thereby generating analyzed motion data, including classifying the motion data to different motion classes; j) storing the analyzed motion data; k) comparing current motion data with reference motion data, wherein the reference motion data is selected from the group of previously stored motion data of the same motion class and external reference motion data; and l) submitting a warning signal if the current motion data inadmissible differs from the reference motion data.

A method of monitoring a subject with an opto-physiological sensor system is described herein. The method comprises obtaining a model of the optical properties of at least one body tissue type to be monitored, wherein the model of the optical properties comprises a definition of static (DC) and dynamic (AC) components of transmitted optical power and a definition of a source-detector separation related to a normalised path length for an illumination source of the opto-physiological sensor: obtaining an indication of at least one physiological property of the subject from a wearable device worn by the subject: obtaining an indication of at least one physical variable from the wearable device worn by the subject; determining, using the opto-physiological model, how the at least one physical variable affects the at least one physiological property; and determining a corrected value for the physiological property based on the determination of how the at least one physical variable affects the at least one physiological property.

A new wearable device for monitoring the cardiovascular fitness which adapts in a customized way to the fitness profile of the user. This wearable device, provided with a microprocessor with accelerometer and gyroscope, and with a sensor for the heart rate, can be configured as smartwatch, heart rate strap, or smart shirt. The distinctive feature of the device is its capability to adapt to any physical activity, by offering a precise monitoring and feedback in real time of the cardiovascular response, by identifying four response distinct dynamic models called "Efficient adaptation", "Balance Below", "Pressure", "Active Effort" and "Proactive Preparation", by using an algorithm based on the Clustering K-Means. The system allows an accurate monitoring and feedback for training and optimizing the physical performance. This approach offers significant advantages for a wide range of users, from general health and well-being to professional sport training. Moreover, the system uses an advanced algorithm which combines Dynamic Time Warping and Clustering to evaluate the general fitness level of the user and it is capable of estimating the VO2max value through YMCA 3 minute step test, thus providing a complete analysis of the physical capabilities of the user.

This application provides a blood pressure measurement method for a wearable device and a wearable device, so as to improve measurement accuracy of a blood pressure value. The wearable device includes a pressure transducer PT and a photoplethysmography PPG module. The PT and the PPG module are deployed at a target position of the wearable device. The target position includes a position for contacting a wearer when the wearable device is in a worn state. The method includes: obtaining a pressure value collected by the PT; obtaining a PPG signal collected by the PPG module; and determining a blood pressure value of the wearer based on the pressure value and the PPG signal. If the wearer wears the wearable device on a wrist, a PPG signal of the wrist of the wearer may be collected, and a pressure value between the wearable device and the wrist may be collected according to this application. Adding pressure value information based on the PPG signal may improve measurement accuracy of the blood pressure value.

An embodiment of a spectrophotometric device for measuring blood oxygen saturation includes a light source, a detector for detecting a light signal reflected by a subject's tissue, an electronic data processing unit to calculate blood oxygen saturation based on the light signal, and a motion sensor for detecting a motion signal indicative of motion of the light source and / or the light detector. The electronic data processing unit may discard a light signal measured by the detector if the motion signal detected by the motion sensor exceeds a predetermined threshold value. An embodiment of a method for measuring blood oxygen saturation includes emitting a light signal into a subject's tissue, detecting the light signal reflected by the tissue, calculating blood oxygen saturation using the light signal measured by the detector, and detecting a contact signal indicative of contact between a subject's skin and the light source and / or the detector.

Systems are described for measuring, recording, transmitting, accessing, and using an array of physical and physiological measurements that quantify states of complex adaptive systems, such as biological systems, more particularly the state is reflected in a metric designated health capacity. These measurements may be used, for example, for the pre-symptomatic detection and interception of disease states in a biological system. In one aspect, the system comprises a wearable device configured to measure, substantially simultaneously, an array of water-associated metrics, preferably at multiple loci on the biological system and as a function of time. The systems may further comprise using a scalable technology platform to identify, from the array of data across multiple systems, preferably compared to a training data set, utilizing machine readable instructions, to determine and / or predict health states, including the health capacity, of the biological system and to generate recommendations, including modification or nutrition, sleep, physical or mental inputs for the improvement of health for the biological system.