852 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 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.

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.

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.

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.

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.

The invention belongs to the technical field of exercise training and analysis, and particularly relates to an intelligent badminton action analysis system and method based on a motion sensor, and the system comprises a sensor data collection module, a real-time data processing module, an action recognition analysis engine, an intelligent data storage module and a visualization module. The method comprises the following steps: receiving and buffering sensor data in real time; periodically extracting data segments by using a sliding window to carry out multi-feature (an acceleration peak value, an angular velocity peak value, a peak value ratio and the like) calculation; based on a predefined multi-feature threshold rule set, identifying action types (such as kicking, flat ball drawing and the like) and evaluating the quality; an intelligent strategy is adopted to selectively store data so as to optimize the system load; and finally, a result is visually fed back in real time. The method realizes objective, quantitative and real-time analysis of badminton actions, has the advantages of high recognition accuracy, high system real-time performance, efficient data processing and the like, and is suitable for autonomous training of athletes and auxiliary guidance of coaches.

Motion-based management of a logistics container uses elements of a wireless node network including a container node associated with the logistics container and equipped with a motion sensor. The motion sensor detects a motion status (e.g., moving, stationary, accelerating, decelerating) for the logistics container. The container node compares the motion status to a prior motion status, and then identifies a changed motion condition for the logistics container based upon the comparison. In response to the changed motion condition, the container node alters a broadcast profile used by the container node in communicating with other nodes in the wireless node network. The container node may be deployed as an apparatus within a logistics container, be considered together with the logistics container as a motion sensing container node apparatus, and be considered with multiple package ID nodes as a motion-based management system for the logistics container.

The invention relates to a wearable fitness monitoring system that uses advanced sensor technology to track real-time muscle engagement, posture, and fatigue levels during physical activity. The system comprises a capacitive sensor system, incorporating elastic silver fiber cloth and medical-grade silicone, for precise muscle movement detection; a motion sensor for tracking movement along multiple axes; and an AI-powered processing unit to provide ergonomic feedback, prevent injuries, and optimize performance. It features dynamic self-calibrating sensors that adjust to user-specific movements using machine learning algorithms. The system offers personalized workout plans, adjusting in real-time based on biometric feedback, environmental conditions, and muscle fatigue. Additionally, it integrates Bluetooth Low Energy (BLE) for wireless communication, haptic feedback for posture correction, and AR / VR for immersive workout guidance. The system includes cloud-based data storage with encryption and supports blockchain for secure data management, offering a comprehensive, personalized fitness experience that improves workout efficiency and safety.

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.

Systems and methods of performing localization of a vehicle are provided. The system receives data comprising one or more of: raw images from one or more vision sensors, motion sensor data, or lane-level map information. The system determines, based on the data, a lane in which the vehicle is located and lateral displacement of a center of the vehicle relative to a center of the lane.

A lighting fixture containing both visible light sources and UV light sources is disclosed herein. In one embodiment, the fixture is an overhead fixture. UV light can sanitize / disinfect the surrounding environment over a period of about 30-45 minutes. In this way, viruses, germs, bacteria, mold, and the like can be destroyed to clean the air and / or surfaces (e.g., furniture) of the room exposed to UV light. Accordingly, embodiments of the present invention include a physical input device, such as a wall-mounted button, switch, or wireless device, that can safely and efficiently activate a lighting assembly to produce UV light in a way that protects occupants from unintentional exposure to UV light. This configuration is especially useful in situations where the use of UV light is regulated, such as in a hospital setting.

According to the multi-modal digital art content generation and personalized recommendation system and method, a sequential dynamic knowledge graph is constructed, a pattern, color and narrative structure triple is extracted from an art database, a natural language processing technology is adopted to mine implicit relations, and the artistic content is recommended to the personalized recommendation system and method. Multi-modal feature fusion is realized by utilizing visual and text feature extraction and a gating attention mechanism, a style template is automatically loaded according to equipment performance, rendering parameters are dynamically adjusted, and a recommendation strategy is optimized through facial expression analysis, gaze point distribution calculation and emotional state inference. Rendering detail levels are adjusted based on data acquired by a touch screen sensor and a motion sensor, design contents conforming to culture specifications are generated by combining a conditional GAN correction network, real-time updating of culture symbols and related rules is supported, and the system has strong intelligent processing capability and is suitable for popularization and application. The efficient generation and personalized recommendation requirements of the digital art content in multiple scenes can be met, and the user experience and the interaction effect are remarkably improved.

The invention provides a continuous identity authentication method based on multichannel PPG signals in an uncontrolled environment. Firstly, a smart watch is used for collecting wrist double-channel green light, red light and infrared light PPG signals and motion sensor data, and original information is obtained. Aiming at the problem that an uncontrolled environment signal is easily interfered, noise reduction and optimization are carried out on the signal through abnormal value processing, FIR high-pass, band-pass and multi-band-pass filtering and ICA independent component analysis, so that motion artifacts are effectively inhibited, and the signal quality is improved. Then, based on pulse wave valley positioning segmentation signals, a multi-channel standardized data set is constructed; and finally, by means of an Inception-LSTM neural network fusing multi-scale feature extraction and time sequence modeling, end-to-end learning of biological features is carried out, and a high-precision identity authentication decision in a dynamic environment is realized. According to the method, the robustness is verified in seven motion state simulation scenes, and the authentication accuracy and reliability in an uncontrolled environment are remarkably improved.

In an example system, multi-sensor motion data that reflects the motion of a user over a period of time is received as recorded by a plurality of motion sensors on wearable devices, specific changes are determined in the data by, firstly, quantifying it using a two-dimensional data transform; secondly, extracting an anomalous area; and thirdly, calculating a number of the anomaly's properties, and the results are input into a machine learning model to detect that the user fell. The machine learning model processes the anomaly's properties and evaluates the current state of the user's activity by classifying the properties against a state space previously calculated by analyzing historical activities of daily living. Depending on a two-dimensional transform implemented, the machine learning model detects when the user falls, as well as potentially allows predicting that the user will suffer a fall in advance of the actual event, aiming at solving the stroke prediction problem.

A machine guidance system and method utilize optical sensors, position sensors, and movement sensors to generate point cloud data for a construction asset. Data encompassing terrain features, obstacles, and the real-world position and orientation of the asset and its attachment are calculated. The fused data is used to autonomously control the movement of the asset and the position of the attachment to maintain a cutting edge at a target grade. As a result, terrain mapping and obstacle detection are performed in real-time, enabling safe and efficient operation without predefined paths. In this manner, applications include automated earthmoving, grading, and material handling in dynamic construction environments.

The invention provides an anti-pinch control method, device and system for an electric tail door and a vehicle, and relates to the technical field of vehicle control. The method comprises the following steps: receiving radar original point cloud data generated by a millimeter wave radar sensor in an obstacle sensing module; receiving tail gate motion data sensed by a tail gate motion sensor in the electric tail gate actuating mechanism; filtering the radar original point cloud data to generate preprocessed data; obstacle identification and classification are carried out on the preprocessed data through an obstacle identification and classification model, and the obstacle type is determined; determining the position, motion data and distance of the obstacle according to the preprocessed data; determining a critical distance according to the movement data of the obstacle, the movement data of the tail gate and predefined braking response time; according to the obstacle type, the obstacle distance and the critical distance, an anti-pinch control instruction is determined; and an anti-pinch control instruction is sent to an electric tail gate executing mechanism. The anti-pinch control sensitivity is improved, and the safety is improved.

A motion sensing mount apparatus for mounting to a building corner, tree, or the like and directing a camera toward a source of detected motion includes a structure mounting bracket which mounts to a support structure. A support module is removably coupled to and extending away from the structure mounting bracket. A camera is movably mounted atop the support module via a turntable or the like. Motion sensors detect motion and a direction from which the motion occurred relative to the camera. A drive mechanism is operable to rotate the turntable to direct the camera toward the source of the motion.

The invention discloses an intelligent help-seeking ring system based on low-power-consumption Bluetooth, and the system comprises a multi-mode sensor combination which comprises a motion sensor, a physiological sensor, and an environment sensor; the multi-level triggering module is configured to judge a user state through a preset multi-level early warning algorithm based on the fusion data acquired by the multi-mode sensor, and trigger corresponding distress signals according to different risk levels; the self-adaptive communication module is configured to carry out distress signal transmission through low-power-consumption Bluetooth; the self-organizing network system comprises a plurality of Bluetooth receiving nodes, and mesh networks are formed among the Bluetooth receiving nodes to relay and forward distress signals; and the monitoring processing system is configured to receive and process the distress signal from the self-organizing network system and execute a corresponding rescue response. By constructing the combination of the multi-level triggering module and the multi-mode sensor, the accuracy and the intelligent level of triggering the distress signal are improved.

The invention relates to an optical anti-shake test method and system, computer equipment and a storage medium. The method comprises the following steps: generating a simulation motion sensor signal without physical vibration according to a preset waveform parameter; the simulation motion sensor signal is applied to a target optical anti-shake module so as to drive a motor to perform anti-shake motion; performing target reference point tracking identification on a target plate image acquired by an image sensor, and fitting to obtain an actually measured motion curve of a target reference point in the target plate image; determining a theoretical motion curve of a target reference point in the target plate image according to the preset waveform parameter and the actually measured motion curve; and determining a test result of the target optical anti-vibration module according to a deviation calculation result of the actually measured motion curve and the theoretical motion curve. By adopting the method, the efficiency and the accuracy of the optical anti-shake test can be improved.

The invention relates to the technical field of navigation and positioning, in particular to a pedestrian dead reckoning optimization method based on improved wave crest detection and CNN-LSTM, comprising the following steps: S1, data acquisition: acquiring pedestrian motion sensor data including acceleration, angular velocity and magnetic field intensity through an accelerometer, a gyroscope and a magnetometer; s2, on the basis of an improved wave crest detection algorithm, real-time monitoring and analysis are carried out on the acceleration signal by setting a dynamic threshold value, and an effective gait cycle is extracted; s3, extracting reference features of the step length from the pedestrian motion sensor data, training a step length prediction model of the CNN-LSTM network, and optimizing the step length prediction model; and S4, obtaining a pedestrian course angle based on quaternion attitude calculation, realizing gait cycle segmentation and step length prediction in combination with an improved wave crest detection algorithm and a CNN-LSTM model, and finally calculating the position of the pedestrian at the next moment through the course angle and the step length so as to complete continuous navigation positioning. The method can achieve the dynamic extraction and fusion of the spatial-temporal features of multiple sensors, improves the gait detection robustness and step length estimation precision in a complex scene, remarkably optimizes the overall positioning performance of a PDR, and solves a problem that the positioning precision is reduced because a signal is affected by sensor noise, behavior diversity and the like.

A fall detector device (102) for mounting on a wall for monitoring an environment (100), comprising: a motion sensor (204) for detecting motion of a person (106) within a first field of view of the motion sensor; an active reflective wave sensor (206) for detecting the presence of a person within a second field of view of the active reflective wave sensor using wave reflections from the environment, the first and second fields of views at least partially overlapping one another, and a processor coupled to the motion sensor and active reflective wave sensor for receiving output from each of the motion sensor and active reflective wave sensor, wherein operation of the active reflective wave sensor is dependent on a detection of motion of a person within at least a portion of the first field of view by the motion sensor.