2624 results about "Tactile sense" patented technology

Provide a system. [Solution] A system including: a means for utilizing generative AI to provide information that serves as an alternative to vision; a means for accessing the AI ​​via a smart device or headset; a means for acquiring environmental information in real time; a means for recognizing surrounding objects and people; and a means for conveying information in the form of voice, audio messages, haptic feedback, etc.

The invention relates to the field of virtual reality positioning, and discloses a VR large-space positioning interaction system based on multi-modal perception, and the system comprises the steps: deploying a multi-modal sensor to obtain sensing data, carrying out the visual feature extraction and preprocessing, building a sparse point cloud map in a matching manner, carrying out the scale calibration, and constructing an environment model; pre-judging a UWB signal path based on an environment model, performing error optimization compensation on an NLOS state, and performing observation updating and fusion through degradation detection to obtain a predicted state change; constructing an interactive perception network, and tracking the hands and the whole body; tactile feedback is realized by using a layered tactile system, and a tactile effect is generated by using vibration frequency mapping; the transmission efficiency is improved by using a beam forming technology, an edge cloud server cluster renders a virtual scene, and the scene is pre-rendered in advance to offset network and rendering delay; an online calibration mechanism is designed, and system errors are corrected through visual loopback detection, UWB beacon dynamic correction and IMU drift compensation.

The invention discloses an image lesion recognition system for assisting a doctor in the digestive system department in sampling inspection. The image lesion recognition system comprises an image acquisition module, a preprocessing module, an AI lesion recognition module, a feature analysis module, a sampling recommendation module, a data storage module, a real-time interaction module, a system control center, a disinfection monitoring module and a power consumption optimization module. The system integrates white light, narrow-band light and fluorescent endoscope images based on a multi-modal large model, accurately identifies focuses such as polyps and ulcers in real time and marks boundaries, quantitatively analyzes the forms, infiltration depths and texture features of the focuses in combination with super-pixel segmentation and three-dimensional reconstruction technologies, and significantly improves the diagnosis objectivity. According to the system, a sampling scheme matched with a clinical guide is dynamically generated by relying on a reinforcement learning strategy and biomechanical simulation, a focus three-dimensional model and a real-time endoscope picture space are subjected to registration through an augmented reality technology, an operation path is guided by means of tactile feedback, and the accuracy and efficiency of doctor operation are greatly optimized.

The invention provides a robot grabbing pose optimization method based on visual and tactile fusion, and belongs to the field of robot grabbing pose estimation. According to the method, firstly, an initial grabbing pose is predicted based on target object sparse point cloud obtained through vision initial observation segmentation, then a robot plans and executes grabbing to obtain tactile data of the surface of an object, and target object point cloud information of an area where the initial grabbing pose is located is complemented based on the tactile data with object local information; and finally, the initial grabbing pose is optimized according to the complemented point cloud, so that more stable robot grabbing is achieved.

The invention relates to the technical field of robot decision making, and provides a robot multi-modal fusion autonomous decision making method and system based on a large language model.The method comprises the steps that a robot obtains multi-modal environment information through a visual sensor, a touch sensor, an auditory sensor and a laser radar which are carried by the robot; performing preliminary filtering and noise reduction processing on the original sensor data, and synchronously recording all the sensor data by timestamps; performing space-time semantic alignment on the preprocessed multi-modal data, mapping pixel coordinates of a target in a visual target coordinate quantization original image to a robot coordinate system, performing uncertainty evaluation on a multi-modal signal through a dynamic Bayesian network, and taking entropy or variance as an uncertainty quantitative evaluation index. According to the method, the information quality is improved from a data fusion source, accurate and reliable basic support is provided for subsequent decision making, and decision making errors caused by data deviation are greatly reduced.

The invention discloses a multi-mode interaction method for an accompanying robot, and relates to the technical field of robot interaction.The multi-mode interaction method comprises the steps that voice, visual and tactile signals are converted into quantum states through multi-mode quantum state coding, emotion weights are dynamically fused, and unified quantum representation is constructed; the dynamic quantum decision engine analyzes environmental noise and user emotion intensity in real time based on a quantum measurement theory, generates an adaptive strategy through dynamic modal weight distribution, and solves a multi-modal instruction conflict; holographic reinforcement learning optimization is combined with quantum acceleration calculation and classical reinforcement learning, the reward function weight is dynamically adjusted, cross-modal knowledge migration is achieved through the quantum tunneling effect, and the system strategy is continuously optimized. The problems that a traditional multi-mode interaction system is rigid in mode switching, high in emotion recognition error rate, lack of self-adaptive optimization of a feedback strategy and the like are solved, and the interaction efficiency and the user experience are improved.

The invention relates to the technical field of soft robots and intelligent control, in particular to an intelligent control method based on the Internet of Things. The method comprises the following steps: carrying out multi-modal data acquisition and preprocessing on a contact process of a gripper and an object to obtain a time synchronization data set; constructing a tactile feature tensor including pressure, friction force and strain force according to the time synchronization data set; performing stress gradient calculation and mapping according to the tactile feature tensor to obtain a stress gradient field and a region threshold mapping table; performing stress gradient threshold adaptive adjustment according to the regional threshold mapping table and the stress gradient field to obtain an adaptive threshold distribution map; and performing stress risk prediction according to the adaptive threshold distribution map to obtain a stress risk map. According to the method, the potential damage risk is predicted by sensing the contact state of the gripper and stress concentration, the adaptive capacity, robustness and success rate of the grabbing process are remarkably improved, and the method is particularly suitable for grabbing tasks of fragile and irregular objects which are difficult to prejudge.

The invention relates to the technical field of brain-computer interfaces, and discloses a self-adaptive dynamic feedback brain-computer interface training method and system based on virtual reality. The training method comprises the steps that a user performs motor imagery according to a training scene presented by the VR display device; the electroencephalogram acquisition equipment acquires electroencephalogram signals; configuring a data processing module, and operating a motor imagery continuous control model to calculate characteristic parameters by taking the electroencephalogram signals as input; under the condition that the task exists, the characteristic parameters are used as input, and a dynamic self-adaptive characteristic-feedback mapping model is operated to calculate control parameters; sending the control parameters to a functional electrical stimulation device to load electrical stimulation of corresponding intensity to the muscle to obtain tactile feedback; meanwhile, the dynamic adaptive feature-feedback mapping model is dynamically updated according to latest input feature parameters; the control parameters are sent to the VR display device to present visual feedback, and the user adjusts the motor imagery based on the visual feedback and the haptic feedback. The participation sense of the user and the training efficiency can be improved.

The invention relates to the technical field of body-equipped intelligent robots and intelligent equipment, in particular to a self-adaptive flexible tightening body-equipped intelligent robot which comprises a body-equipped moving platform, the top of the body-equipped moving platform is provided with a body-equipped lifting mechanism, and the top of the body-equipped moving platform is provided with a body-equipped articulated arm module. And a tightening actuator and a clamping jaw actuator are arranged at the tail end of the outer part of the body articulated arm module. According to the self-adaptive flexible tightening intelligent robot and the tightening process method, sensing and self-adaptive adjustment of external environment changes are achieved through multi-module integration, and the tightening process method based on a multi-mode sensing system and an intelligent control system is constructed; vision, force sense and touch sense data are fused, real-time monitoring and dynamic adjustment of the tightening process are achieved through a machine learning algorithm, and the intelligent level and quality stability of tightening operation are effectively improved.

A control method for a support robot, the method comprising: in response to recognizing that a target object has an intent to require support, controlling a support robot to move toward the target object, and when the support robot moves to a target movement position, controlling a robot arm to move to a target joint position, wherein the target movement position and the target joint position are both determined on the basis of the relative position between the target object and a target sensor; and in the process of the robot arm moving to the target joint position or after the robot arm moves to the target joint position, when it is detected by means of a tactile sensor that an external force is applied to the robot arm, performing compliance control on the support robot. Further provided are a support robot, an electronic device, a computer storage medium, and a computer program.

The invention discloses a robot hierarchical reinforcement learning variable impedance control method based on vision and touch, and belongs to the field of robot control. According to the method, an upper-layer planning system and a lower-layer variable impedance control system are included, the upper-layer planning system fuses information from a visual sensor and a tactile sensor, feature coding is conducted on a visual image through a variational self-coding model, tactile feedback and mechanical arm state information are combined, and an action strategy adapting to the current environment and corresponding impedance parameters are formulated. And the lower-layer variable impedance control system realizes variable impedance control of the robot based on a depth kinematics and dynamics model, and allows the robot to dynamically adjust the motion and contact force according to the sensed information when interacting with the environment, thereby improving the adaptability to the environment change and the task execution efficiency. According to the method, the success rate and execution efficiency of the robot for executing the complex assembly task in the unstructured environment are remarkably improved, and high robustness is shown.

The invention relates to the technical field of physical education, in particular to a physical education method and system based on multimedia and holographic AR fusion, and the method comprises the following steps: collecting the motion data of a trainee based on a multi-source sensor array, constructing a motion parameterized model, and generating a holographic projection generation module through the cooperation with a standard motion database; a holographic AR teaching image is generated in real time, an environment perception module is combined to carry out adaptive projection on illumination and spatial layout, a spatial positioning module is used to establish coordinate mapping between a trainee and a virtual image, and a multi-channel feedback device is started to output visual, tactile and auditory signals. And finally, comparing the action parameters with the standard action data to automatically generate a training report containing wrong action labels and correction suggestions. According to the method, the motion capture precision and the interactive immersion can be effectively improved, and efficient and extensible technical support is provided for modern physical education and training.

The invention discloses an interactive virtual reality generation method, and belongs to the technical field of virtual reality, and the method comprises the steps: collecting the spatial position and motion data of a user through a pose sensor and a somatosensory sensor, carrying out the three-dimensional point cloud reconstruction and interaction plane parameter extraction through an edge calculation processor, and calling a matched virtual object model to generate an initial scene. A dynamic prediction model is loaded to predict influence parameters of user actions on a scene, scene evolution is calculated in real time in combination with a digital twin engine, and a result is transmitted to a multi-mode feedback module to generate visual, tactile and auditory feedback instructions. And the scene dynamic adjustment module is triggered to adjust the physical attribute parameters by monitoring the bio-electricity signal in real time. According to the method, geometric constraint consistency and semantic reasonable matching of a virtual scene and a real environment are realized through multi-source sensor data fusion and edge calculation, user actions are dynamically responded by combining space-time prediction and a digital twinning technology, scene physical attributes are adaptively adjusted on the basis of bio-electricity signals, and multi-modal feedback cooperation is assisted, so that the user experience is improved. And the reality, immersion and user comfort of virtual reality interaction are obviously improved.

The invention relates to the technical field of artificial intelligence, can be applied to service scenes such as pension service, financial science and technology and medical health, and discloses a grabbing control method, device and equipment based on collaborative grabbing characteristics and a medium. Generating collaborative grabbing features and feature weight vectors, constructing a grabbing model in combination with historical grabbing data, and determining an initial grabbing strategy; and in combination with an initial grabbing strategy and real-time tactile feedback data, a grabbing control sequence and a cooperative adaptation coefficient are generated through constraint optimization, grabbing operation is executed, real-time feedback is monitored, the grabbing force and posture are dynamically adjusted according to deviation and the cooperative adaptation coefficient, and accurate control is achieved. According to the method, the problem that in the prior art, grabbing response is not flexible is solved by constructing the collaborative grabbing features and the grabbing model and fusing real-time feedback and control strategies, dynamic self-adaption and fine adjustment in the grabbing process of different objects are achieved, and the damage risk is reduced.

The invention discloses a self-adaptive gravity center adjustment method and system for a transfer robot, and relates to the technical field of robot control, and the method comprises the steps: respectively collecting the three-dimensional information and contact force distribution information of an article and the contact force information between the robot and the article through a sensor, integrating the collected information to obtain a multi-modal data set, and carrying out the preprocessing of the multi-modal data set; utilizing a deep learning algorithm to extract visual feature vectors from the three-dimensional image information, and extracting tactile feature vectors from the contact force distribution information and the contact force information; fusing the extracted visual feature vector and the extracted tactile feature vector to obtain a multi-modal feature set; constructing a motion state model based on a deep learning algorithm, inputting the motion state model by using the historical multi-modal feature set, and outputting a control strategy; a control algorithm and a control strategy are optimized by using real-time feedback information; the invention provides a self-adaptive gravity center adjustment method by collecting multi-modal sensor data and combining a deep learning algorithm to perform feature extraction and fusion.

A bionic mechanical finger, comprising a bionic finger sleeve (100), a tactile sensing unit (200), a capacitance-to-digital converter (CDC) circuit, a digital processing circuit, and an analog switch array. By means of the design of a sensor structure and an inner insulating layer and the combination with a CDC, the tactile sensitivity comparable to or even superior to that of a human hand is obtained; the design that an upper electrode (210) is grounded and self-capacitors are used as lower electrodes (221) can ensure the precision of pressure measurement, thereby lowering the dependence on chip resources; and in combination with a restriction rule of capacitor design, the mechanical finger achieves a good response in daily grabbing scenarios. Also provided is a mechanical hand comprising the bionic mechanical finger.

The invention relates to the technical field of intelligent inspection, and discloses an intelligent inspection method and system based on AI multi-source fusion, and the method comprises the steps: collecting multi-mode perception data when an expert carries out the touch inspection operation; analyzing the multi-modal perception data, and constructing a biomechanical model and a tactile knowledge mapping model; establishing a standard tactile operation model and an acoustic feature mapping relation database; converting the standard tactile operation model into animation guidance and tactile feedback in an augmented reality interface; actual operation data of the inspectors are collected and compared with the standard touch operation model, and operation improvement suggestions are generated; fusing the tactile inspection result, the acoustic detection result and the visual inspection result to generate a comprehensive evaluation report; according to the invention, digital inheritance and standardized application of the tactile skills are realized, and the technical problem that the tactile skills are difficult to transmit in traditional inspection is solved.

The invention discloses a joint denoising method for robot visual motion prediction, and the method comprises the steps: constructing a unified generative model through fusing an image and a depth map collected by a depth camera, motion data collected by CAN line communication of a Piper mechanical arm, and a tactile image collected by a Gelsight Mini tactile sensor; the method comprises two steps of data acquisition and input coding, and joint denoising and generation: firstly, multi-modal data are coded into low-dimensional potential representation, and then future images, depth maps, tactile data and robot actions are cooperatively predicted through a joint denoising framework based on Transform. A mask self-attention mechanism is innovatively introduced, information interaction between modes is dynamically adjusted, action generation is guided through tactile feedback, and the force control precision is improved. The model adopts a de-noising diffusion probability loss function to jointly optimize multi-modal prediction, so that the output consistency is ensured. According to the method, the robustness and the accuracy of flexible operation of the robot are remarkably improved.

The invention discloses a flexible magnetic tactile sensing device based on a microstructure elastic layer, a three-dimensional force measuring device and a measuring method.The flexible magnetic tactile sensing device comprises a flexible magnetic film, the microstructure elastic layer, a Hall sensor, a PCB and a bottom shell, the Hall sensor is installed on the PCB, and the flexible magnetic film, the microstructure elastic layer, the PCB and the bottom shell are sequentially stacked from top to bottom; the microstructure elastic layer is of a non-planar structure, when the flexible magnetic film deforms due to stress, deformation of the flexible magnetic film is transmitted to the microstructure elastic layer, the change of the three-axis magnetic field intensity is measured through the Hall sensor and uploaded to a PC terminal for data processing, and three-dimensional force measurement is achieved. Three-dimensional force measurement can be achieved, the flexible magnetic film can generate local deformation in the normal direction when normal force is applied, the flexible magnetic film can generate tangential displacement when tangential force is applied, and application scenes suitable for different microstructure elastic layers are provided according to quantitative analysis results.

The invention discloses an ultrasonic enhanced tactile sensor system, a sensor and a sensing method. The tactile sensor system comprises a multi-mode sensing unit and a processing unit. The multi-mode sensing unit integrates an ultrasonic module and a visual tactile sensing module, ultrasonic acoustic impedance matching and optical imaging transmission are achieved through an acousto-optic matching structure, and ultrasonic echo signals and tactile deformation images can be synchronously collected. In the processing unit, an event triggering module detects a contact event based on a tactile image and generates a switching signal, and a self-adaptive processing module dynamically controls an ultrasonic mode, distance measurement during non-contact, and switching to object internal imaging or material property identification during contact according to the switching signal. And the three-level fusion processing module outputs a multi-modal sensing result through signal-level synchronous acquisition, feature-level cross-modal fusion and decision-level task switching. According to the system, multi-dimensional perception of proximity sense, surface texture and deep information is realized, and the pre-judgment and accurate operation capability of the robot in a complex scene is improved.

The invention relates to a robot dynamic grabbing control method based on visual sense and tactile sense depth fusion. The method comprises the following steps that firstly, based on visual information, surface geometric features of a target object are extracted, grabbing adaptability scores are calculated, an optimal grabbing point is selected, and a collision-free approaching track is planned; secondly, in a contact establishment stage, detecting a contact event through a touch sensor, fusing vision and touch data to unify a coordinate system, calculating a vision-touch consistency score, and applying an initial grabbing force; and thirdly, in the stable holding stage, slip detection is conducted through wavelet packet energy entropy and pressure gradient, and the grabbing force and impedance parameters are dynamically adjusted in combination with self-adaptive impedance control. According to the method, intelligent control over the whole process from approaching to contact to stable holding is achieved, and the adaptability, stability and safety of robot grabbing in the dynamic environment are improved.

The invention relates to the technical field of pose control, in particular to a robot flexible grabbing method and system based on a touch sensor, and the method comprises the steps: carrying out the image collection of an environment where a to-be-operated object is located based on a camera integrated in a robot, so as to obtain an image of the environment where the to-be-operated object is located; and acquiring a real-time pose of an end effector of the robot based on inertial measurement equipment integrated in the robot, and performing pose estimation on a to-be-operated object in the environment image based on a pre-trained pose estimation model to obtain the pose of the to-be-operated object. By combining the environment image acquired by the camera and the real-time pose information provided by the inertial measurement equipment, the robot can estimate the pose of the object to be operated in real time, and the position and the pose of the object can be accurately speculated by utilizing the pre-trained pose estimation model, so that the accuracy of the operation of the robot is improved. Therefore, the operation of the end effector of the robot can be accurately butted with the object, and errors in the operation process are reduced.

The invention discloses a VR teaching experience enhancement system and method, and belongs to the technical field of virtual teaching, and the method specifically comprises the steps: collecting the position data and posture data of a trainee in a VR environment, building a multi-user coordination interaction model based on the position data and posture data of the trainee in the VR environment, and carrying out the interaction of the multi-user coordination interaction model. The method comprises the following steps: acquiring training participants, identifying an interaction relationship and an interaction event among the training participants, generating tactile feedback data corresponding to the interaction event based on the interaction event, performing calibration processing on the tactile feedback data, and synchronously sending the calibrated tactile feedback data to VR equipment of the training participants, the calibration processing comprises adjusting the tactile feedback data based on the spatial propagation difference quantity and the tactile feedback time offset value of the trainees; according to the invention, when multiple trainees cooperatively operate the same virtual object or scene, consistent and real-time tactile response and spatial perception can be obtained, and the teaching experience in a multi-person cooperation scene is improved.

The invention discloses a multi-modal interaction control method for a Bluetooth knob screen, and relates to the technical field of multi-modal interaction control, and the method comprises the steps: synchronously collecting and forming a multi-modal feature vector set, and outputting a final operation instruction through a multi-modal fusion model; performing function mapping according to the final operation instruction, and identifying a control command; judging whether to resend the same instruction or adjust the instruction parameter based on the control command, and dynamically adjusting the tactile feedback parameter based on the function mapping of the current scene mode; performing low power consumption management based on the dynamically adjusted tactile feedback parameters, and triggering scene mode mapping update according to the wake-up instruction type; and optimizing the instruction output accuracy and the function mapping rule of the multi-modal fusion model based on the updated user interaction data. According to the method, deep fusion and intelligent cooperation of multiple interaction modes are realized, and accurate output of operation instructions, intelligent management of tactile feedback and adaptive updating of scene modes are achieved through dynamic function mapping, intelligent feedback adjustment and continuous learning optimization.

The invention discloses a remote medical interaction method based on virtual reality, and particularly relates to the field of remote medical interaction.The method comprises the steps that by synchronously collecting multi-modal tactile data and transmission state parameters of a patient end, a joint feature vector is established, and the transmission delay of each modal tactile signal is predicted in real time. The method comprises the following steps: extracting sensitivity indexes of an operation stage and a tactile signal through a doctor end real-time image, calculating a reward value in combination with a double-branch regression network, updating a modal transmission strategy, and generating a dynamic priority distribution matrix; based on the matrix, optimizing a bandwidth slicing strategy and performing signal compensation by combining a real-time network state and delay prediction; in addition, switching and pathological mutation of surgical instruments are detected, priority distribution is dynamically adjusted, and efficient transmission and operation feedback of tactile signals are ensured.

The invention discloses a surgical operation system with a training or auxiliary function, particularly relates to the field of intelligent surgical operation systems, and cooperatively works through five modules including multi-modal sensing, layered AI decision making, dynamic feedback, closed-loop learning and an expansion interface. MEMS pressure sensing and biological signal fusion sensing are adopted, strategic layer three-dimensional navigation and execution layer magneto-rheological damping control are combined, and an operation whole-process optimization system is constructed; a multi-source data persistent evolution model is integrated through federal learning, and precise man-machine interaction is realized by matching eye movement tracking AR and a variable stiffness touch glove. According to the system, the intraoperative risk identification accuracy is improved, the operation efficiency is improved, third-party equipment extension and doctor skill quantitative evaluation are supported, and the operation safety and the training standardization level are remarkably improved.

The invention relates to the technical field of electromyographic signal monitoring, and discloses a guided monitoring method and system based on a wireless surface electromyographic analysis feedback instrument. The method comprises the following steps: acquiring electromyographic signal time sequence data and limb movement track space coordinate information when a target muscle group moves through a multi-channel wireless surface electromyographic sensor; muscle activation intensity and fatigue change features are extracted, a joint activity angle change sequence is calculated, and a muscle-joint collaborative activity feature matrix is generated through space-time alignment fusion; analyzing signal transduction delay and amplitude matching degree in the matrix, and evaluating the nerve driving coordination level; according to the deviation from the preset standard mode, generating a real-time biological feedback signal containing an action correction instruction and a muscle activation intensity adjustment parameter; and converting into a tactile vibration mode and a visual guidance mark to be output. According to the method, synchronous acquisition and fusion analysis of multi-dimensional motion information are realized, and visual guidance is provided through multi-modal feedback.

The invention discloses a touch feedback enhanced multi-mode robot grabbing control system and method. The touch feedback enhanced multi-mode robot grabbing control system comprises a touch feedback enhancement module, a visual touch language large model semantic generation module and a touch enhancement diffusion control optimization module. The tactile feedback enhancement module is based on a multi-agent cooperation system, performs semantic level and signal level processing on tactile signals, and realizes upper and lower layer coordination through a feedback routing mechanism. And the visual touch language large model module adopts a touch encoder based on CLIP to realize multi-mode semantic conversion and material attribute identification. And the tactile enhancement diffusion control module adopts a BRIDGeR style interpolation diffusion technology, and combines visual embedding and tactile constraint to realize high-frequency optimization of an action track. According to the method, the material identification accuracy, the precision operation success rate and the complex scene self-adaptive capability are remarkably improved.

A robot control method based on tactile prediction pre-training comprises the following steps: acquiring and generating a human playing data set consisting of three-channel image tensors in an offline stage, and training a constructed conditional diffusion model comprising a tactile encoder, a tactile decoder and an action and visual encoder; in the online stage, the trained conditional diffusion model is integrated into a standard imitation learning strategy network, and an action instruction of the robot is generated according to the state of the robot, the current visual features and the tactile feature vectors extracted by the imitation learning strategy network. According to the method, a specific agent task is completed by training a deep neural network model, that is, a future tactile signal sequence is predicted according to historical information and future action intentions; the model is enabled to characterize generic haptic features contacting physical dynamic laws for further migration into downstream robot control tasks.

The invention discloses an intelligent motion capture system and method of a multi-mode sensor, and the method comprises the steps: creating and installing links among an optical module, an inertia module, a touch module, a synchronization core module and a to-be-detected object; sending a synchronous starting pulse signal based on the synchronous core module, setting the synchronous starting pulse signal as a clock reference starting point, and starting the optical module, the inertia module and the touch module based on the clock reference starting point; performing data acquisition on a to-be-measured object based on the optical module, the inertia module and the touch module to obtain an object acquisition information data set; and creating an acquisition original timestamp based on the synchronous start pulse signal, creating a sensor network delay model to dynamically adjust the acquisition original timestamp to obtain a calibration timestamp, and analyzing and correcting the time offset of the object data acquisition information. The method and the device have the effect of improving the accuracy of intelligent motion capture of the multi-mode sensor.