1225 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.

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 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 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.

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.

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.

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.

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 sink system and methods of making and using sink systems including a sink basin and a discharge conduit coupled to the sink basin, which is positioned to expel fluid into the sink basin via an outlet thereof. The flow of fluid through the discharge conduit may be activated via a motion sensor or switch coupled directly to the basin or a spacer ring positioned between the basin and the countertop. Controls for adjusting the temperature and / or flow rate of fluid through the discharge conduit may also be coupled to the basin or a spacer ring positioned between the basin and the countertop, or alternatively, to a surface of the countertop.

The invention discloses a motion state monitoring and feedback system and method based on electroencephalogram signals, in the system, an electrode array is arranged corresponding to a forehead part and used for collecting the electroencephalogram signals, a motion sensor is used for collecting motion data signals, and a vibration motor is used for sending out vibration feedback; the electroencephalogram collection hair band sends the electroencephalogram signals and the motion data signals to the mobile terminal, and the mobile terminal sends the electroencephalogram signals and the motion data signals to the cloud processing platform; the cloud processing platform is used for predicting input electroencephalogram signals and motion data signals by adopting a physical and mental state prediction model based on a deep neural network to obtain physical and mental state indexes; triggering a self-adaptive feedback intervention strategy according to the physical and mental state indexes, and controlling a vibration motor to send vibration feedback in a preset mode. According to the technical scheme of the invention, the recognition accuracy of the physical and mental state characteristics of the movement of the user is improved, and the wearing comfort and convenience of the signal acquisition device are improved.

The invention discloses an intelligent lighting control system based on the Internet of Things and AI prediction, and relates to the technical field of intelligent lighting, and the system comprises a data collection module which is connected with an ambient light sensor, a motion sensor and an electric quantity sensor through an intelligent lamp, collects the state data of intelligent lighting in real time, and sends the state data to a server; by integrating the ambient light sensor, the motion sensor and the electric quantity sensor, the system can sense environment changes and user behaviors in real time, self-adaptive adjustment of lamplight is achieved, user operation is simplified, a user does not need to frequently and manually adjust lamplight, the indoor illumination brightness can be automatically adjusted according to the ambient light intensity, and the user experience is improved. Meanwhile, through application of the AI prediction module, through deep analysis of user behavior modes and environment data, the illumination requirements of the user in different scenes can be accurately predicted, the lamplight state is adjusted in advance, and the user experience is further improved.

Various implementations disclosed herein include devices, systems, and methods for modifying display of virtual content. In some implementations, the method is performed by an electronic device including a non-transitory memory, one or more processors, a display, an eye tracker and a movement sensor. In some implementations, the method includes displaying, via the display, virtual content. In some implementations, the method includes detecting, via the eye tracker, a movement of a gaze in a first direction. In some implementations, the method includes detecting, via the movement sensor, a movement of a head in a second direction while detecting the movement of the gaze in the first direction. In some implementations, the method includes, in accordance with a determination that the movement of the gaze in the first direction and the movement of the head in the second direction are corresponding opposing movements, modifying a display characteristic of the virtual content.

The invention provides a parking space navigation method and system for an underground parking lot, and the method comprises the steps: collecting magnetic field signals in the underground parking lot, RSSI signals of a cellular base station, and motion sensor data of each user during walking through a plurality of users, forming multi-source data, and recognizing the positions of all potential anchor points through a sliding window and a trend analysis algorithm; obtaining a virtual anchor point to construct an undirected graph; performing path reconstruction by using the multi-source data to obtain a walking path of each user and an undirected graph to construct a virtual map so as to determine a parking position of a target vehicle and a current position of a target user; according to the parking position and the current position of the target user, an optimal path from the current position of the target user to the parking position is planned by using an anchor point connection relation in the virtual map, and navigation guidance is generated; by fusing various signal sources, the dependence on fixed infrastructures is reduced, and meanwhile, the positioning precision and reliability are improved, so that a user can accurately navigate to a parking spot in an underground parking lot with signal congestion.

A method for motion synchronization-based head pose estimation, by a head mounted display (HMD) device and an HMD device for performing the same are provided. The method includes receiving, by the HMD device, motion data from a plurality of motion sensors of the HMD device, receiving, by the HMD device, a plurality of image frames from at least one simultaneous localization and mapping SLAM camera of the HMD device, estimating, by the HMD device, a plurality of motion parameters of head movements of a user from the plurality of image frames received from memory, generating, by the HMD device, a filtered subset of the motion data received from the plurality of motion sensors based on the plurality of motion parameters of the head movements, synchronizing, by the HMD device, the plurality of image frames received from the memory and filtered subset of motion data, and estimating, by the HMD device, the head pose based on the synchronized plurality of image frames and motion data.

The invention discloses wearable electronic equipment for occupational environment health monitoring. The problems that existing fixed and handheld monitoring equipment is poor in real-time performance and insufficient in individual protection are solved. The wearable electronic equipment comprises a main control module, an environment sensing module, a positioning module, a motion sensor, an alarm module, a wireless communication module and a data storage module, the main control module processes the collected environment, position and activity state information, compares the information with a preset safety threshold value, immediately starts multi-mode grading alarm when dangerous parameters exceed the standard, uploads the information to a monitoring center or a cloud platform through dual-mode communication, and uploads a protection mode from passive response to active early warning. According to the invention, one-stop monitoring of a complex dangerous environment is realized, active continuous monitoring and active early warning can be realized without personnel operation, effective transmission of information is guaranteed by multi-mode alarm, an end-edge-cloud protection system is constructed, wearing is comfortable, operation is not affected, and all-weather safety monitoring is realized.

Aspects of the disclosure relate to detection and recording of actual or potential impacts to parked vehicle, including hit-and-run incidents involving the parked vehicle and a moving vehicle. For example, using a camera of a parked vehicle having motion sensors that are in a sleep state or other inactive state, the parked vehicle may determine that a moving object, other than a person, is within a threshold distance of the parked vehicle. The parked vehicle may then capture at least one image using at least the camera of the parked vehicle. In this way, the parked vehicle can use its own cameras to detect and document a potential or actual impact on the parked vehicle by the moving object.

A force and / or motion measurement system is disclosed herein. In one or more embodiments, the force and / or motion measurement system includes at least one kinematic sensor device configured to detect a position and / or movement of a body portion of a person, the at least one kinematic sensor device being mounted in a floor of a room or in a force measurement assembly; and at least one data processing device operatively coupled to the at least one kinematic sensor device, the at least one data processing device including at least one hardware component storing computer executable instructions, and the at least one data processing device configured to execute the computer executable instructions. The computer executable instructions comprise instructions for determining a position and / or movement of the person based upon output data from the at least one kinematic sensor device.

Systems and methods to process motion signals obtained from at least one motion sensor during delivery of plurality of PFA pulses or pacing signals to heart tissue location(s) and determine degrees of diaphragm spasm affected by the PFA or pacing signals. In some embodiments the system identifies reduction of diaphragm spasm during delivery of PFA pulses to a certain location and upon such identification indicates that continued delivery of further PFA pulses to that certain location may cause irreversible damage to the Phrenic nerve. Additionally, or alternatively, the system utilizes the diaphragm spasm degrees measured following pacing or ablation of at least two heart tissue locations, to map the degrees of spasm affected by the ablation of various heart tissue locations and thereby enables to conduct ablation at location associated with reduced spasm.

The invention relates to the technical field of data processing, and particularly provides a traffic data trusted sharing method, system and device and a storage medium, and the method comprises the steps: obtaining motion sensor data, and extracting a motion feature vector from the motion sensor data; acquiring camera data, and extracting a visual feature vector from the camera data; fusing the motion feature vector and the visual feature vector into a vehicle feature vector through a cross-modal attention fusion mechanism; and verifying the compliance of the feature generation process by using zero-knowledge proof, and uploading a verification result and the hash value of the vehicle feature vector to a shared block chain. According to the invention, a key technical guarantee is provided for realizing efficient, safe and credible traffic data sharing.

Techniques are disclosed to build a map for an area around a route traversed by a moving platform. An integrated navigation solution based at least in part on obtained motion sensor data for a device within the moving platform is used to build an online map during a first instance of time for the area using perception sensor data, such as, for example, radar measurements and / or optical samples. The integrated navigation solution may then be revised in a second instance of time with a nonlinear state estimation technique that uses a nonlinear measurement model for perception sensor data, wherein integrating the motion sensor data and perception sensor data in the nonlinear state estimation technique is tightly-coupled. Generating the revised integrated navigation solution in this tightly coupled technique includes using the motion sensor data with the nonlinear state estimation technique and integrating the perception sensor data directly by updating the nonlinear state estimation technique using the nonlinear measurement models and the online map information.

The invention provides a sheep abnormal behavior real-time monitoring method and system based on the Internet of Things, and relates to the technical field of abnormal monitoring, and the method comprises the steps: obtaining multi-source data information of sheep, including a position coordinate sequence, motion sensor data, a gait rhythm sequence, a colony house environment parameter sequence and a group topological relation; the influence of environmental constraints on group movement is quantified by constructing a group behavior field intensity model; based on an individual physiological energy consumption backstepping mechanism, an energy metabolism imbalance index is identified by using a deep residual network; through Granger causal relationship and conditional independence analysis, behavior causal separation of true and false anomalies is realized; and performing cross-cycle abnormal evolution path prediction in combination with historical behavior data, and generating a graded early warning conclusion. According to the invention, the false anomaly and the real health anomaly caused by environmental factors can be effectively distinguished, and the monitoring accuracy and the early warning timeliness are remarkably improved.

Embodiments of the present invention provide a game control system for converting physical efforts into a virtual gaming environment. The game control system comprises a pair of gaming pedals equipped with a plurality of motion sensors and a plurality of force sensors. The motion sensors and the force sensors detect the physical efforts of a user. A printed circuit board is provided within the pedals and includes a processing unit programmed to synchronize and analyze the data from the motion sensors and the force sensors, translating the physical efforts of the user into game input commands. A communication module facilitates wireless connectivity between the gaming pedals and an external communication device running a gaming software.

A gun lock, comprising a-gun disabling element configured to prevent operation of a gun when connected to a gun, a locking element operable to selectable secure the gun disabling element, a motion sensor operable to detect motion of the lock and generate a motion signal in response to motion of the lock, and a transmitter connected to the motion sensor and operable to transmit an alert signal in response to a motion signal.

The invention relates to the technical field of continuous authentication, and provides a continuous identity authentication method, device and equipment based on multi-modal characteristics, and the method comprises the steps: collecting the motion behavior data of an authenticated user in a preset time length through a motion sensor on intelligent equipment, and collecting the touch behavior data of the authenticated user in the preset time length through a touch screen sensor; according to the motion behavior data of the authenticated user, obtaining posture features of the intelligent equipment, and according to the touch behavior data of the authenticated user, obtaining user touch features; and inputting the posture feature and the user touch feature into an identity authentication model to obtain an identity authentication result of the user. Therefore, the accuracy of continuous identity authentication of the identity authentication model is improved, the problem that the identity authentication model cannot be flexibly applied to other applications in the prior art is solved, multi-modal feature fusion is not needed, and the problems that in the prior art, excessive dependence on behavior biological feature acquisition, processing, modeling and learning of a user is caused, the model training calculation amount is large, and the user experience is poor are solved. And the complexity is high.

The embodiment of the invention provides a travel state detection method, wearable equipment and a storage medium, and relates to the technical field of state detection. The method is applied to the wearable device, the wearable device comprises a motion sensor and an image sensor, and the motion sensor comprises an IMU: obtaining an image sequence collected by the image sensor in response to the fact that the accuracy of a first travel state detection result is lower than a preset threshold; inputting the image sequence into a pre-trained feature extraction model, and extracting spatial features and time sequence features of the image sequence; obtaining a second travel state detection result by using a pre-trained multi-layer perception model based on the spatial features, the time sequence features and a corresponding relation learned in the training process of the multi-layer perception model; and determining a target travel state detection result based on the probability that the user takes the vehicle. Therefore, the travel state detection is performed twice by using the data and the image sequence acquired by the IMU, and the travel state detection accuracy is improved.

A holiday tabletop decoration kit designed for creating customizable dioramas for holidays such as Easter, Halloween and Christmas is disclosed. The kit includes a base with pre-drilled holes for secure placement of various holiday-themed figures and scenery elements. The kit incorporates electronic components such as LED lighting, sound effects, and motion sensors to enhance the interactivity of the display. The kit can be controlled via a mobile application with augmented reality features, allowing users to customize their scenes and control embedded lighting and sound effects remotely.

Detecting Onset of Motion Sickness in Virtual Reality (VR) Signals from a motion sensor on a head-mounted device (HMD) used by a player of a computer game are analyzed to determine whether motion of the player such as sway may resemble a precursor motion pattern to motion sickness, so that the player may be advised accordingly before the symptoms of motion sickness manifest themselves.

The disclosed device relates to a multiple lighting mode LED lamp. The lamp has an LED assembly with a plurality of white LEDs for a first illumination source, and a plurality of UV LEDs for a second illumination source. The LED assembly is attached to the first end of a flexible support, and a base is attached to the second end of the flexible support. The lamp base incorporates an LED driver module in electrical communication with the illumination sources. A manual touch switch in the base, when touched by a user activates the white LEDs to operate the lamp in a lighting mode. A motion sensor in the form of an infrared induction sensor, is incorporated into the face of the LED assembly for detecting motion. Upon motion being detected, the UV LEDs are activated for a predetermined period of time to operate in UV light mode.

A patient litter basket spin control assembly includes a first gyroscope and a motion sensor for sensing an angular acceleration of a patient litter basket. The first gyroscope generates a counter torque to the litter basket to slow the angular acceleration of the litter basket to provide stabilization. The first gyroscope and a second gyroscope may be configured as a pair and located at opposite ends of the litter basket.