2370 results about "Motion sensors" patented technology

A real-time system for the multimodal generation of virtual reality scenes based on artificial intelligence for the creation of immersive three-dimensional environments from natural language narratives, consisting of: a speech capture module configured to continuously record a user's spoken narrative via one or more directional microphones, preprocesses the captured signal by noise reduction and temporal alignment, and outputs a digital speech stream; A speech-to-text processing unit that is operationally coupled to the speech capture module and configured for real-time speech recognition using a continuous neural transformer model. The unit is trained to transcribe natural language utterances into structured text data while maintaining contextual continuity throughout the evolving narrative. a semantic interpretation processing unit that is communicatively linked to the speech recognition unit and configured to perform natural language understanding techniques to extract contextual entities, spatial references, temporal relationships, and object attributes from the transcribed narrative; the engine includes a large language model that is fine-tuned for spatial reasoning tasks; a scene graph generation module configured to transform the interpreted semantic data into a structured, hierarchical representation that defines nodes for identified entities and edges for corresponding relationships, with each node associated with metadata describing geometry, position, orientation, texture, and linking attributes between objects; a multimodal image-language model processor coupled with the scene graph generation module, wherein the processor is configured to retrieve, adapt, or synthesize appropriate three-dimensional elements from a pre-trained visual-lexical embedding space and align these elements with their semantic and spatial definitions derived from the scene graph; a scene assembly and rendering controller configured to create a cohesive virtual scene from the aligned assets, perform real-time rendering using a GPU-accelerated ray tracing pipeline, and produce a stereoscopic visual output that corresponds to the evolving narrative; A head-mounted virtual reality visualization device connected to the rendering engine and configured to display the generated immersive environment to the user in real time. The device features motion sensors and inside-out tracking cameras to detect head and body movements, dynamically updating viewing angles and perspective within the rendered scene; and a bidirectional feedback module integrated into the head-mounted device and connected to the semantic interpretation processing unit; the module is configured to interpret corrective commands, gestures, or supplementary comments from the user to refine or modify specific scene elements without interrupting the real-time visualization; The system continuously updates the virtual scene as the narrative develops, ensuring temporal synchronization between speech input and rendered output below a defined latency threshold, thus enabling a natural, dialogic construction of complex three-dimensional virtual environments.

A detection and deterrent method and system for detecting and deterring a pet from attacking another animal including a collar configured to be affixed to a pet, the collar having a motion sensor. The collar is configured to determine when a pet is about to attack another animal and to deter the pet from attacking the animal.

The invention provides an industrial robot control system and method. The system comprises a trajectory planning module, a dynamic compensation module, a feedback control module, an execution module, a sensor module and a data storage module. The trajectory planning module generates an expected trajectory of an end effector of the robot; the dynamic compensation module calculates a dynamic compensation torque based on the expected trajectory and a robot dynamics model; the feedback control module generates feedback control torque according to the deviation between the real-time pose data acquired by the sensor module and the expected trajectory; the execution module drives robot joints to move according to the synthesized moment of the dynamic compensation moment and the feedback control moment; the sensor module collects robot joint angle, angular velocity and torque data in real time; the data storage module stores robot dynamic model parameters, control algorithm parameters and historical motion data. High-precision and smooth robot motion control can be achieved, the dynamic response and stability of the system are improved, and energy consumption and motion errors are reduced.

The invention discloses a rescue underwater robot based on multi-mode drowning recognition and a rescue method. The rescue underwater robot is provided with a multi-mode recognition module, a rescue launching device module and a diving mask auxiliary rescue module. The multi-modal recognition module obtains images through a high-definition camera and a sonar, the recognition precision is improved through the combined calibration of direction angles and the feature fusion technology, and accurate detection of a drowning target is achieved; the rescue launching device module adopts a spring launching device to quickly release a rescue device, the rescue device is provided with a high-elasticity silica gel strip and a built-in inflatable bag, the silica gel strip is provided with an anti-skid texture structure and can quickly surround and fix the waist of a drowning person, and the inflatable bag is automatically started to be inflated after it is confirmed that the inflatable bag is stably contacted with the drowning person through a magnetic delay mechanism; the diving mask auxiliary rescue module is provided with a heart rate sensor, a motion sensor, an emergency help button and a black box module, and the state of a diver is monitored in real time.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes such as agent autonomous decision making, financial science and technology and medical health, and discloses a multi-modal sequence data processing method, device and equipment and a medium. Extracting multi-scale feature hierarchies, combining the multi-scale feature hierarchies into a multi-scale feature pyramid set, performing cross-modal feature alignment to generate a multi-scale alignment feature sequence, executing local and global attention processing to generate long-distance dependency features, performing cross-layer information interaction to generate comprehensive multi-scale features, and performing multi-scale feature extraction; and dynamically fusing the multi-modal information and inputting the multi-modal information into a task decision network to obtain a target task result. According to the method, through the multi-scale feature pyramid, cross-modal alignment, attention processing and cross-layer information interaction, the problem of insufficient relevance between different modals and different scales in multi-modal long sequence data is solved, and fine modeling and dynamic fusion of multi-modal and multi-scale features are realized.

The invention relates to a blood oxygen and heart rate monitoring system and method based on PPG signals and motion data, and relates to the technical field of medical health and intelligent wearable equipment. The system comprises a PPG signal acquisition module, a motion sensor module, a signal preprocessing module, a dynamic signal segmentation and quality evaluation module, an oxyhemoglobin saturation calculation module, a heart rate calculation module, a motion state recognition module, a self-adaptive Kalman filtering module and an output display module. The method and the device have the effects of realizing a function of identifying the motion state by the wearable equipment, improving the signal interference suppression capability and widening the application scene.

Systems and methods are provided for assisting individuals with physical and / or cognitive ailments, such as dementia, Alzheimer's disease, and / or visual impairment. The quality of life, safety, and / or autonomy of such individuals can be enhanced by utilizing a combination of sensors, actuators, augmented reality (AR) technology, and machine learning (ML) algorithms. The sensors and actuators can include (but are not limited to) cameras, global positioning system (GPS) sensors, accelerometers, motion sensors, AR visual systems, headphones, microphones, haptic feedback systems, and / or connectivity capabilities (wired and / or wireless) to provide information about the surrounding environment and events. By leveraging advanced sensor technology, AR, and real-time data processing, a comprehensive support system can be provided that addresses the unique challenges faced by these individuals on a daily basis.

Apparatuses, systems, interfaces, and methods for implementing the apparatuses, systems, and interfaces include: (a) a dynamic 3D environment generation subsystem including an eye or head tracking device, one or more motion sensors, one or more processors, and one or more display devices displaying bands superimposed on environment with bands that control movement through the environment; (b) training systems where trainer and trainee movement are superimposed and scaled to improve movement conformity and mastery of movements; and (c) eye or head tracking systems for selecting / activating / confirming icons or objects based on motion and micro duration holds.

An electronic door system includes a door frame, a door hingedly coupled to the door frame, an electronic locking mechanism coupled to the door, one or more lights coupled to the door frame, a camera system including at least two of a camera, a proximity or motion sensor, or a doorbell, and a wiring harness extending through the door frame and the door. The wiring harness is coupled to the electronic locking mechanism, the one or more lights, and the camera system. The wiring harness is configured to electrically couple to an external power source.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes such as financial science and technology and medical health, and discloses a task instruction generation method, device and equipment based on cross-modal fusion, and a medium, and the method comprises the steps: carrying out the decoding and noise reduction of an input video, generating a frame sequence, and recognizing a plurality of key frames based on the inter-frame similarity; extracting spatial features of the key frames to form a sequence, and generating video spatial-temporal features in combination with time features; performing semantic preprocessing on the input text to obtain text semantic features, and acquiring motion sensor signals to obtain motion features; fusing the video spatio-temporal features, the text semantic features and the action features to generate fused features; and generating a perception vector based on the fusion feature and outputting a task instruction. According to the method, multi-modal fusion is realized through key frame extraction and space-time fusion mechanisms in combination with text semantic features and action features, and the perception expression ability and the task instruction generation accuracy are improved by using time sequence information and multi-source perception input of the video.

This invention is an article of clothing with electromyographic (EMG) sensors which measures body motion and / or muscle activity. This clothing can be a short-sleeve shirt or a pair of shorts, wherein the electromyographic (EMG) sensors are on the cuffs. The electromyographic (EMG) sensors can be modular; they can be removably attached to different locations in order to create a customized article of electromyographic clothing which optimally measures the muscle activity of a particular person or muscle activity during a particular sport. This clothing can also include bending-based motion sensors.

The present disclosure relates to virtual reality interaction methods, devices, and systems. In one example method, a head-mounted display device displays a virtual keyboard. In response to an input trigger operation of a user, a hand-mounted device sends hand detection data to the head-mounted display device based on a detected target sensor signal. After determining, based on the hand detection data, that an input trigger event occurs, the head-mounted display device determines an initial tap position of a target finger relative to the virtual keyboard based on a hand image captured by a camera. The hand-mounted device sends hand motion tracking data collected by a motion sensor to the head-mounted display device. In response to an input completion operation of the user, the head-mounted display device determines and displays an input result based on the initial tap position and the hand motion tracking data.

Systems, interfaces, and methods for implementing the systems and interfaces include a dynamic environment generation subsystem that changes objects and subobjects based on locations of the motion sensors and / or the nature, time and / or location of sensed motion and include selection attractive movement as the selection protocol, where a selection object is used to discriminate between selectable objects and attract a target object toward the selection objects, where the direction and speed of the motion controls, discriminates, attracts, and activates the selected objects.

The specification describes apparatus comprising means for: estimating a propagation delay of transmissions between a base station and a mobile device based on data from at least one motion sensor that is local to the mobile device; determining updated timing information based on the estimated propagation delay; and updating an internal clock of the mobile device in accordance with the updated timing information.

A device for time-based tracking and cost optimization in construction projects, the device comprising the following: a robust housing suitable for use on construction sites; a processing unit located inside the housing, configured to perform real-time time-stamping, data acquisition and preprocessing tasks; a multimodal sensor unit that is operationally coupled with the processing unit, wherein the sensor unit comprises at least a motion sensor, an RFID reader, sensors for environmental conditions and a vision module with optical character recognition; a real-time clock module that is operationally connected to the processing unit to provide time synchronization for all sensor data streams; a wireless communication module that supports the Wi-Fi, LoRa and LTE protocols and is configured for transmitting time-stamped data to a central project server; a storage module that is operationally coupled with the processing unit to locally buffer time series data of construction activity during offline operation; a housing-mounted, touchscreen-based human-machine interface configured to allow site personnel to enter activity updates and confirm the status of construction tasks; a cost optimization engine running on the central server, the engine being configured to receive time-synchronized sensor data from multiple such devices and dynamically calculate time-cost trade-offs using a predictive planning technique that incorporates the principles of the critical path and the power value; furthermore, the device is configured to be integrated into a digital twin environment of the building under construction in order to provide real-time visualization of progress and to generate suggestions for resource reallocation based on a time-cost-benefit analysis.

The invention relates to a dynamic calibration method and device of a motion sensor for intelligent terminal equipment, and the method comprises the following steps: carrying out the real-time collection of a multi-source sensor on target intelligent terminal equipment through a multi-sensor fusion technology, and obtaining a multi-mode sensor data set; performing feature extraction on the multi-modal sensor data set to obtain a feature vector set; performing error mode recognition on the feature vector set to obtain a sensor error mode; performing error compensation on the multi-modal sensor data set based on the sensor error mode to obtain a compensated multi-modal sensor data set; and performing data reconstruction based on the compensated multi-modal sensor data set to obtain a reconstructed motion trail, thereby solving the technical problem that the measurement precision is reduced due to the fact that the traditional sensor data is often influenced by external interference and self-drift.

The invention discloses a Pick ball motion trail tracking and tactical decision-making auxiliary system which adopts a high-frame-rate camera, a micro sensor installed on a ball and an athlete motion sensor worn on an athlete to respectively collect video image data, ball motion data and athlete motion data in the Pick ball motion process. Precise positioning and trajectory tracking are carried out on a ball through a target detection and tracking algorithm based on deep learning, a tactical knowledge model is constructed by combining site information and athlete action data and applying a decision tree and a neural network algorithm, real-time tactical decision suggestions are provided for athletes and coaches, and the training efficiency is improved. And the competitive level and the training effect of the Pick ball sports can be improved.

Systems, interfaces, and methods for implementing the systems and interfaces include a dynamic environment generation subsystem that changes objects and subobjects based on locations of the motion sensors and / or the nature, time and / or location of sensed motion and include selection attractive movement as the selection protocol, where a selection object is used to discriminate between selectable objects and attract a target object toward the selection objects, where the direction and speed of the motion controls, discriminates, attracts, and activates the selected objects.

A system and method for generating patient care instructions based on real-time sensor and medical data. The method includes receiving time-stamped sensor data from a sensor network comprising motion, occupancy, and environmental sensors, and receiving medical data associated with a patient, including medical conditions, treatment history, medication data, and biometric data. The sensor data is enriched with room-specific information, and activity pattern data is generated in real time using a pattern recognition model. The activity pattern data includes mobility, sleep patterns, medication adherence, statistical measures, temporal patterns, and correlations with medical data. Anomalies indicating potential health risks are detected by comparing current activity patterns with baseline data. A prediction model, trained on historical patient data, assesses the patient's health and generates care instructions accordingly. The care instructions are securely delivered to patient devices, caregiver applications, or automated medication dispensing systems, enabling timely interventions and continuous patient monitoring.

A safety and alarm system for vehicles which is designed to enhance safety by preventing risks associated with leaving vulnerable individuals or pets in locked vehicles is disclosed. The system comprises a durable, weatherproof housing attachable to the ceiling of the vehicle including one or more of a 360-degree infra-red camera, a motion sensor in the vehicle seats, CO2, CO, and temperature sensors. Each sensor is associated with a predetermined threshold. The system includes a processor and a wireless chip, and communicates with a handheld electronic device, enabling real-time alerts and remote monitoring. The smartphone application associated with the system enables for customization of alerts, holographic display controls on the windows, viewing of vehicle interior and communication with vehicle occupants via camera, and system activation / deactivation. Additionally, the system includes GPS functionality for emergency location tracking, enhancing the safety and response measures in critical situations.

Provided herein are methods for Inertial Measurement Unit (IMU) sensor compensation utilizing deep neural network (DNN) technology. In some embodiments, the methods involve receiving sensor values from gyroscopes, accelerometers, and non-motion sensors, then loading these values into a deep learning algorithm alongside true sensor values. Unlike traditional calibration techniques that rely on complex parametric models, this approach may utilize a neural network to directly process raw sensor data and enhance output signal accuracy. In some embodiments, the training process employs ground truth data for approximately 70% of inputs, with the remaining 30% dedicated to sensor compensation. The technique may be implemented in standalone IMUs, augmented IMUs, and navigation systems, offering improved accuracy and reduced computational overhead across various technological applications.

The invention discloses an intelligent voice recognition and analysis system based on a universal smart phone chip, and the system comprises a data collection module which synchronously captures a voice signal and motion sensor data through a built-in microphone array and a motion sensor, and generates an original multi-mode data package with a time sequence stamp; and the noise reduction processing module is used for receiving the original multi-mode data packet, executing environmental noise spectrum analysis, generating an anti-phase sound wave, performing signal enhancement and outputting a pure voice stream. According to the method, through multi-modal noise separation, nonlinear signal enhancement and hierarchical privacy protection, the contradiction between speech recognition precision and privacy security in a complex environment is solved, and meanwhile, by means of dynamic resource scheduling and a lightweight model, the finite computing power of a mobile phone chip is utilized to the maximum extent in the aspect of speech recognition.

Embodiments of a method for improving movement characteristic determination using a plurality of mobile devices associated with a vehicle can include: collecting a first movement dataset corresponding to at least one of a first location sensor and a first motion sensor of a first mobile device of the plurality of mobile devices; collecting a second movement dataset corresponding to at least one of a second location sensor and a second motion sensor of a second mobile device of the plurality of mobile devices; determining satisfaction of a device association condition indicative of the first and the second mobile devices as associated with the vehicle, based on the first and the second movement datasets; and after determining the satisfaction of the device association condition, determining a vehicle movement characteristic based on the first and the second movement datasets.

A device including a visual or audible signaling means and a motion sensor, and logic for activating or controlling the signaling means in response to a sensed motion according to an embedded logic is disclosed. The device may be used as a toy for amusement, and may be shaped like a play ball or as a handheld unit. The device may be powered from a battery, which may be charged from an AC power directly or contactless by using induction or by converting electrical energy from harvested kinetic energy. The embedded logic may activate or control the signaling means, predictably or randomly, in response to the sensed acceleration magnitude or direction, such as sensing the crossing a preset threshold or sensing the peak value. The visual means may be a numeric display for displaying a value associated with the count of the number of times the threshold has been exceeded or the peak magnitude of the acceleration sensed.

Techniques are disclosed to build a map for an area around at least a route traversed by a moving platform. An integrated navigation solution for a device within the moving platform is generated using motion sensor data obtained from a sensor assembly of the device and absolute navigational information for the platform. The integrated navigation solution is then used to project radar measurements obtained from at least one radar of the platform onto the area so that the map is built using the projected radar measurements.

A system and methods are provided for authenticating handwriting of a user, including a pen that includes a motion sensor, a biometric sensor, a communications interface, a real-time clock and a processor with associated memory to perform: receiving a biometric signal of a user from the biometric unit; generating a user authentication indicator, wherein a match between the biometric signal and stored biometric information is a factor of the user authentication indicator; receiving a motion signal indicative of a user signature from the motion sensor and responsively determining a handwriting pattern instance; storing in the memory a signature record including the user authentication indicator, the handwriting pattern instance, and a time and date; and communicating the signature record by the communications interface to an external application.

The invention relates to the technical field of geotechnical engineering testing, in particular to a gravity type multifunctional in-situ testing device and a gravity type multifunctional in-situ testing method, and the testing device consists of three parts, namely a penetration system, a shearing system and a power system. The penetration system comprises a spindle-shaped full-flow penetration probe and a motion sensor. The shearing system comprises two arc-shaped shearing plates, a power shaft and a torque sensor. The power system comprises a moving empennage, a motor, a storage battery and an information collector. The device is hoisted to a test point to be thrown, so that the device freely falls and can penetrate into a soft soil layer, motion parameters of the device in the process of penetrating into the soft soil are tested and recorded, and the characteristics of the soil layer are obtained; and the arc-shaped shear plate is opened and rotated, so that the undrained shear strength of the soft soil layer is obtained. The test platform is convenient to arrange and high in test efficiency and accuracy. Excessive manpower is not needed, and the automation degree is high. Therefore, the technical problems that a test platform of an existing in-situ test device is difficult to arrange and low in automation degree can be solved.

The invention relates to a system and method for detection on a stationary surface. A method includes generating, by a first motion sensor of an electronic device, first sensor data over an acquisition time window, and determining whether the electronic device is located on a stationary surface, a semi-stationary surface, or in motion by processing the first sensor data to generate first process data. The method further includes determining whether the electronic device is in a steady state based on the first process data, the steady state indicating whether the electronic device is held on the stationary surface for a first predefined time. And, the method further includes stopping processing the first sensor data in response to determining that the electronic device has been in the steady state for a second predefined time.

The present invention relates to a method implementing measurements obtained with a LiDAR sensor (2) mounted on a wind turbine (1), and measurements obtained with at least one motion sensor (CAM), as well as a LiDAR measurement model (MOD M) and a wind model (MOD V). The method then implements an informative adaptive Kalman filter (KAL) to determine the wind speed (v) at some estimation points. At least one wind speed characteristic (CAR) can then be possibly deduced therefrom, in the rotor plane for example.

The invention relates to a sign language translation method based on a motion sensor of an intelligent wrist-worn device, and belongs to the technical field of artificial intelligence and mobile computing application. According to the method, the motion data of the non-habitual hand is generated by establishing the mapping relation of the motion of the two hands and taking the motion data of the habitual hand of the user as the reference, and the motion data of the non-habitual hand is intelligently estimated and combined with the neural network and the large language model, so that the motion data of the non-habitual hand can be intelligently estimated on the premise of not increasing extra equipment burden. Precise recognition and translation of sign languages of both hands are realized. According to the method, seamless integration with the daily life of the user is realized, the design is simple, the method is easy to use, the user can quickly integrate the sign language into a daily use scene without purchasing extra special equipment, the threshold of technology application is reduced, the universality of the sign language translation technology is improved, the translation accuracy is improved, the response real-time performance is ensured, and the user experience is improved. And the communication between the sign language person and the non-sign language person is smoother and barrier-free.