1859results about "Sleep inducing/ending devices" patented technology

The invention belongs to the technical field of artificial intelligence and health monitoring, and particularly relates to an emotion recognition and intervention system based on facial micro-expression and physiological signal fusion. The emotion recognition precision is improved through space-time alignment analysis of facial micro-expressions and physiological signals, adaptive feedback is achieved in combination with cognitive load correlation modeling and a wearable multi-channel regulation and control terminal, cross-period emotion evolution prediction and group situation awareness are supported, a'awareness-decision-intervention 'complete closed loop is constructed, and the emotion recognition efficiency is improved. And the accuracy and initiative of emotion management in a complex environment are enhanced.

The invention discloses a mild cognitive impairment brain rehabilitation training device and system in the technical field of cognitive nerve rehabilitation, and the device comprises a multi-modal data fusion module, a personalized training task generation module, a multi-sensory stimulation presentation module and a real-time adaptive regulation and control module. And the personalized training task generation module is used for dynamically generating a personalized narrative training task fusing the user interest theme and the cognitive training target by utilizing a reinforcement learning algorithm based on the user cognitive weak domain evaluation result and the user personal interest and life narrative content obtained through natural language processing. Clinical data of a patient and real-time electroencephalogram and behavior data are fused, a narrative training task deeply fused with life experience and interest of the patient is dynamically generated, the difficulty of the training task is automatically adjusted according to real-time physiological indexes, meanwhile, seamless connection with a medical system is achieved through digital biomarkers, and the training efficiency is improved. And the precision and compliance of rehabilitation training are obviously improved.

The invention discloses a sleep mild wake-up method based on a user sleep curve, and belongs to the technical field of sleep monitoring, and the method comprises the steps: collecting a physiological signal of a user, generating a sleep stage curve by using a pre-trained sleep stage model, and carrying out the parallel analysis of heart rate variability and respiratory coordination to generate a mood index curve; overlapping and fusing the two curves to form a sleep-mood alignment feature set; in a preset wake-up time range, analyzing the sleep stage and psychological state of the user according to the feature set, and dynamically determining an optimal wake-up starting opportunity; when the clock arrives, an instruction is sent to the linkage alarm clock, and sound, light and touch multi-mode stimulation is triggered in sequence in a cooperative mode; in the wake-up process, the sleep depth of the user is continuously monitored, if it is detected that the depth recovery exceeds the critical threshold value, the stimulation intensity is adaptively adjusted until the depth falls back, it is ensured that the user naturally wakes up under the condition that the discomfort is the lowest, and intelligent mild wake-up is achieved.

The invention relates to the technical field of medical rehabilitation, and discloses a vestibular function and cognitive function rehabilitation training system and method based on virtual reality, and the rehabilitation training system comprises a hardware platform, a software platform and a server. The hardware platform comprises VR interaction equipment, a head motion tracking sensor and calculation and display equipment; the software platform is integrated with a user management module, a visual stimulation module, a vestibular rehabilitation training module, a cognitive evaluation and training module, a dynamic visual acuity evaluation module and a data management and analysis module. By combining black and white chess grid visual stimulation, standardized vestibular rehabilitation tasks, spatial cognitive testing and DVA objective monitoring, synchronous evaluation and personalized collaborative intervention of vestibular functions and cognitive functions of dizzy patients are realized. According to the method, the problems of boring traditional rehabilitation means, subjective evaluation and lack of quantitative feedback are solved, and the scientificity, interestingness and curative effect testability of rehabilitation training are remarkably improved.

The invention discloses an intelligent intervention system for children with autism spectrum disorder based on multi-modal neural feedback, which comprises a multi-modal physiological signal acquisition module for acquiring electroencephalogram signals of a target child in real time and generating an original physiological signal set; the multi-modal fusion analysis module receives the original physiological signal set and generates a comprehensive evaluation signal representing the current neurocognitive state and the emotion awakening level of the child; the self-adaptive decision control module receives the comprehensive evaluation signal and generates a self-adaptive control signal containing a neural feedback parameter adjustment instruction and a game interaction strategy instruction; the neural feedback intervention module receives the neural feedback parameter adjustment instruction and generates an adaptive neural feedback stimulation signal to act on the child; and the intelligent interactive game module receives a game interactive strategy instruction and dynamically adjusts game scene contents. The intelligent intervention system for children with autism spectrum disorder based on multi-modal neural feedback can solve the problems of poor individual adaptation of autism intervention, multi-modal data splitting and lack of dynamic adjustment.

The invention belongs to the field of intelligent rehabilitation medical instruments, and particularly relates to a brain-computer interface-based multi-modal child sensory integration training device, which comprises a brain-computer interface head ring, an intelligent touch floor and AR interactive glasses, the brain-computer interface head ring is connected with a biological signal acquisition module, the intelligent touch floor is connected with a motion trail analysis unit, the AR interactive glasses are connected with a universe scene engine, and the biological signal acquisition module, the motion trail analysis unit and the universe scene engine are connected with a digital twin generator. The digital twin generator is connected with a dynamic mode switching decision tree, and the dynamic mode switching decision tree is connected with a multi-mode feedback actuator; the brain-computer interface head ring is located on the head of the child and collects electroencephalogram, myoelectricity and electrocardiosignals through a biological signal collection module; and the biological signal acquisition module sends a signal to the digital twin generator through wireless transmission. According to the invention, the training efficiency can be improved, the evaluation dimension can be expanded, potential safety hazard early warning can be realized, and the compliance can be enhanced.

The invention discloses a cognitive enhancement training method and system based on brain signal pattern recognition. The method comprises the following steps: acquiring offline brain signal data of a user, preprocessing the offline brain signal data, and inputting the preprocessed offline brain signal data into a pre-selected decoding model for model training so as to construct a decoding model for the user; based on multiple preset cognitive states, constructing task adjustment logic to perform bidirectional adjustment on the cognitive training task for each cognitive state; in the cognitive training process of the user, on-line brain signal data of the user is decoded based on the decoding model so as to obtain the current cognitive state of the user, and the next cognitive training test is adjusted in real time according to task adjustment logic so as to perform EEG neural regulation and fNIRS neural regulation on the user and further perform cognitive enhancement on the user. According to the method, the current cognitive state of a user is recognized in real time by pre-constructing a decoding model, and bidirectional adjustment is performed on a task according to preset task adjustment logic, so that cognitive enhancement is performed on the user.

The invention discloses an elderly cognitive function adaptive training method and system based on multi-modal interaction feedback, and relates to the technical field of smart medical treatment. The method comprises the steps that basic information of a user is collected for initial cognitive ability evaluation, a user cognitive portrait is constructed according to an evaluation result, and an initial training task with the corresponding difficulty is allocated; collecting multi-modal interaction data in real time according to the initial training task; carrying out fusion analysis on the multi-modal interaction data by utilizing a machine learning model to obtain a quantized real-time state index; based on the real-time state index and the performance data of the current task, dynamically adjusting a subsequent training task through an adaptive decision rule engine; all-dimensional data of each training task is recorded, a visual cognitive competence development trend report is generated through longitudinal comparative analysis, and a machine learning model and a self-adaptive decision rule engine are continuously optimized and trained by utilizing accumulated user data to form an optimized training closed loop. The cognitive function training effect of the old people can be improved.

The invention relates to a self-adaptive visual training method based on eye features, and belongs to the technical field of visual health and artificial intelligence. In order to solve the problems that traditional visual training equipment is heavy in structure, single in training mode and lack of personalized regulation and control and concentration state monitoring, the visual training equipment obtains eye images of a user in real time through an image acquisition module, adopts visual intelligent analysis, extracts double features of eye contours and pupil positions, calculates the visual concentration degree, and improves the visual training efficiency. And a concentration state is judged by combining a dynamic self-adaptive threshold value. When the concentration degree is insufficient, the control module triggers intervention mechanisms such as prompt or training pause and the like, and the remote interaction module uploads data to the cloud platform to generate a personalized training scheme. Intelligent and personalized regulation and control of the training process are achieved, the training effect and compliance are improved, the system is compact in structure and suitable for various scenes, and an efficient solution is provided for vision health.

Methods and apparatuses are described for modulating multiple integrated neural networks to alter composite sensory processes, such as audition or hearing. These methods and apparatuses may be used for therapeutic and non-therapeutic uses, including enhancing entertainment and communication, by providing a cranio-cervical tuning apparatus worn in or around the outer ear or auricle. These methods and apparatuses may include functional neurosensory bias for neurosensory scrambling neuromodulation to influence composite or multi-modal sensory processes.

The invention discloses an abnormal sleep signal identification method for an intelligent sleep monitoring pillow, and relates to the technical field of abnormal sleep signal identification. Comprising the following steps of collecting multi-modal sleep physiological data, preprocessing the multi-modal sleep physiological data, extracting multi-dimensional features from the preprocessed data, constructing a fusion analysis model to judge abnormal sleep signals, triggering a hierarchical intervention mechanism and outputting associated intervention suggestions. According to the method, the time dimension alignment technology and the sliding window matching technology are adopted, the problem of data synchronization caused by multi-sensor sampling frequency difference in a traditional abnormal sleep signal recognition technology is solved, multi-source features are fused through deep learning, the accuracy of abnormal sleep signal recognition is remarkably improved, and the recognition accuracy of abnormal sleep signals is improved. An intelligent analysis model based on deep learning multi-network fusion is constructed, deep extraction and fusion judgment of complex sleep features are realized by utilizing cooperative processing of a convolutional neural network, a recurrent neural network and an attention mechanism, and the reliability of abnormal sleep signal recognition is improved.

Methods and apparatus for changing a brain state of a person from an initial brain state to a target brain state are described. The method includes receiving information characterizing the initial brain state, the information including a structural composition and a functional architecture of the brain, estimating based, at least in part, on the received information, a potential for the brain to change from the initial brain state to the target brain state, determining based, at least in part, on the received information and the estimated potential for the brain to change from the initial brain state to the target brain state, a non-invasive brain stimulation protocol, and controlling at least one non-invasive brain stimulation device to stimulate the brain according to the non-invasive brain stimulation protocol. The method also includes using brain information to inform the design of computational general artificial intelligence agents.

The invention belongs to the technical field of nerve regulation and control engineering, and provides a cerebrospinal fluid rhythm-based Alzheimer's disease electrical stimulation system, which is characterized in that first frequency band range data and second frequency band range data in brain wave signals are coupled to obtain cerebrospinal fluid rhythm data; then, calculating the phase difference between the first frequency band range data and the cerebrospinal fluid rhythm data; finally, the stimulation frequency is dynamically controlled according to the phase difference, and the stimulation current is dynamically controlled according to the brain tissue water volume fraction. The problem that specific metabolism requirements of non-fast eye movement sleep and fast eye movement sleep cycles cannot be matched by adopting single-frequency-band stimulation is solved, dynamic changes of post-traumatic encephaledema and requirements and influences of cerebrospinal fluid rhythm on current control are considered, the real-time adaptive capacity to the dynamic changes of the post-traumatic encephaledema is improved, and the application prospect is wide. And the treatment effect of the Alzheimer's disease is ensured.

The invention discloses a child autism adaptive brain-computer fusion intervention system based on a large model, and the system comprises a multi-modal data collection module which is used for collecting the neural data, behavior data and clinical scale data of a user; the feature extraction and user portrait construction module is used for performing feature extraction and subtype recognition on the multi-modal data to generate a personalized portrait; the multi-defect intervention normal form generation module is used for dynamically generating a personalized intervention task on the basis of a large language model in combination with the personalized portrait and the historical state of the task; the self-adaptive regulation and control module is used for dynamically adjusting an intervention strategy and task difficulty according to the feedback of the real-time neural data and the dynamic change of the behavior data; the user interaction interface module is used for providing a multi-modal human-computer interaction interface; and the effect evaluation and long-term tracking module is used for generating an individual and group intervention effect report. By utilizing the system and the method, accurate, personalized, multi-dimensional and long-term intervention on the autism children can be realized.

The invention relates to an acousto-optic coupled intelligent sleep aiding method and system. The method comprises the following steps: constructing an initial sound scene environment and controlling sound and light output to smoothly transit to a reference value; collecting a user respiration signal to obtain a respiration rhythm; adjusting the sound and light output rhythm to be synchronous with the respiratory rhythm; coupling the acousto-optic output with the respiratory rhythm on the basis of keeping synchronization; executing acousto-optic guidance according to the selected sleep aiding mode and the acousto-optic coupling mode, and guiding the respiratory rhythm to a target frequency by adjusting an acousto-optic output change frequency; and when the user is in the sleep state, controlling the sound-light output to smoothly transit to a closed state. By adopting the method, dynamic synchronization and self-adaptive guidance of acousto-optic output and the breathing rhythm of the user can be realized, the problem of rhythm mismatch caused by fixed output rhythm of traditional sleep-aiding equipment is effectively solved, and the sleep-aiding effect is improved.

The invention discloses a cognitive disorder nostalgic audio-video intervention therapy method, which comprises the following steps of: building a scene containing nostalgic music art, classical movies and retro original sound, playing matched audio and visual materials to a patient, and guiding the patient to perform structured intervention activities such as puzzle, theme recall or emotion sharing. Therefore, memory is stimulated, cognition is exercised, and emotion expression is promoted; according to the invention, by integrating three layouts of audios of nostalgic music art, classical movies and retro original sounds with matched visual elements, a multi-scene nostalgic sensory environment is constructed, and auditory and visual channels of a patient can be synchronously stimulated; compared with single auditory or visual stimulation, the compound sensory stimulation can more effectively activate a plurality of brain areas related to memory and emotion in the brain, so that long-term memory of deep sleep is more intensively aroused, and the strength and awakening efficiency of reminiscence stimulation are greatly improved.

The invention relates to a self-adaptive sleep assisting method, system and device based on multi-mode perception and a medium. The method comprises the steps of performing seasonal correction based on latitude and longitude coordinates, constructing a dynamic rhythm curve, and obtaining user portrait data and real-time multi-source data; performing Kalman filtering fusion on the respiratory frequency signal and the vibration spectrum to generate a fused sleep staging result, performing classification processing on the original audio data, and generating a noise type label and duration; on the basis of the dynamic rhythm curve and a preset adjustment rate, calculating an upward light real-time parameter, and on the basis of the fused sleep staging result, the noise type label and the user portrait data, generating a downward light real-time parameter; and inputting the real-time parameters of the upper light and the real-time parameters of the lower light into a light field coupling model to calculate a light field distribution target value, and generating a light source driving instruction. According to the method, through multi-modal data perception, rhythm curve construction and light field cooperative regulation and control, the sleep staging precision, the light intervention suitability and the sleep assisting effect can be improved.

The invention discloses a multi-modal stimulation regulation and control system combining interactive feedback and AI self-adaption. The system comprises an AI main control and synchronization module which generates a unified rhythm time sequence; the acoustic stimulation output module outputs a rhythm acoustic signal; the visual LED stimulation module outputs rhythm visual signals; the game / animation interaction module provides an interaction task synchronized with the acousto-optic rhythm; the sensing acquisition and feedback module is used for acquiring multi-modal physiological and behavior data in real time; the amplifier output module drives the execution module or equipment; the power management module realizes voltage stabilization and low power consumption switching; the behavior habit forming mechanism module binds a stimulation signal and an interaction task; and the human-assisted management interface module supports remote parameter setting and real-time monitoring. The interactive feedback and AI self-adaptive multi-mode stimulation regulation and control system is combined, microsecond-level synchronization and multi-source closed-loop self-adaption of sound and light and animation can be achieved, a guidance scheme for personalized customization of treatment and behavioral habit cultivation can be achieved, and the efficiency, accuracy and effectiveness of system regulation and control are remarkably improved.

The invention relates to a carsickness control method, a storage medium, a program product, equipment, a system and a vehicle, and relates to the technical field of vehicles. The carsickness control method comprises the steps that under the condition that it is determined that a first user is in a carsickness state based on a first electroencephalogram signal, carsickness suppression operation is executed; wherein the first user is a user using the first headrest, and the first electroencephalogram signal is an electroencephalogram signal collected by the first headrest for the first user. The carsickness state can be intelligently detected in real time and effectively relieved in time, and the carsickness state of the user can be efficiently detected and relieved.

A lighting device, a controller for the lighting device, and a method for using the lighting are disclosed. The lighting device comprises one or more light sources. The lighting device is configured to operate in a predefined first operation mode wherein the lighting device is configured to emit greenish white light. The greenish white light is white light as defined by ANSI C78.377-2017. The greenish white light has a relative M-cone-opic irradiance of at least 0.393, wherein the relative M-cone-opic irradiance is defined as a ratio between an M-cone-opic irradiance and a sum of an L-cone-opic irradiance, the M-cone-opic irradiance, and an S-cone-opic irradiance as defined in publication CIE S 026 / E:2018.

The invention discloses a five-dimensional dynamic emotion visual chemotherapy healing method and system, and relates to the technical field of emotion visual chemotherapy healing, and the method comprises the steps: employing a multi-sensor fusion method to collect physiological data, psychological assessment questionnaire results and historical emotion data of a user, and obtaining a basic emotion feature vector of the user; mapping the emotional state of the user to a five-dimensional emotional space based on the basic emotional feature vector, and initializing a particle set; collecting multi-mode biological signals of electroencephalogram, heart rate variability, electrodermal response, body temperature and voice emotion recognition of the user, and processing to obtain a feature vector reflecting the current emotion state of the user; the geometric morphology and kinetic parameters in the particle set are dynamically adjusted, a visualization engine is used for rendering particles, a two-way feedback adjustment mechanism is constructed, particle behaviors are adjusted according to the emotional state of the user, and the emotional adjustment ability of the user is enhanced; and designing an interactive emotion regulation game.

A device for a cot or child's bed to encourage sleep, comprising a support device to be placed in or under a mattress, comprising at least at least one plank, and at least one support, a lifting unit, wherein the lifting unit comprises at least one bladder, preferably at least two bladders, positioned between the at least one plank and the at least one support.The lifting unit further comprising a control unit, wherein the control unit is configured to inflate and / or deflate the at least one bladder via a pump of a pump system for providing an alternating upward and downward motion of the at least one plank, a pliable skirt extending at least outward from a circumference of the at least one plank.

The invention provides a concentration training method and device based on visual tracking, and the method comprises the steps: collecting the real-time eye movement data of a user, including the staring point coordinate and the eye movement speed, and initializing the target trajectory of a training task; calculating a concentration deviation between the current gazing point and the target trajectory, and dynamically adjusting the target trajectory through nonlinear mapping based on the deviation to generate a dynamic trajectory; constructing a multi-feature fusion distraction scoring model based on the concentration deviation and the change trend of the concentration deviation in combination with the geometric features of the trajectory, and detecting and marking distraction events; when a distraction event is detected, self-adaptive visual correction stimulation is executed at the event position immediately, and a user is guided to return to a task; and after the training period is finished, dynamically adjusting the task difficulty coefficient of the next period according to the frequency and the average deviation of the distraction events in the period.

The invention relates to the field of virtual reality, in particular to an adaptive virtual reality cognitive training method and system. The method comprises the steps of obtaining a user individual information set and a real-time multi-modal physiological data stream, performing real environment three-dimensional reconstruction and relative and friend face modeling based on the user individual information set, and generating an individual virtual training scene; based on the real-time multi-modal physiological data flow and the individualized virtual training scene, nerve-behavior feature extraction and digital twinning dynamic modeling are carried out, and a user cognitive state evaluation information set is generated; based on the user cognitive state evaluation information set, training task parameter real-time optimization and virtual scene element dynamic adjustment are carried out through an adaptive strategy, and a cognitive enhancement intervention instruction set is generated; and based on the cognitive enhancement intervention instruction set, performing neural regulation intervention and training data closed-loop feedback, and generating and outputting an individualized cognitive training report. According to the method and the device, accurate self-adaptive intervention is realized in a virtual reality cognitive training process.

The invention discloses a children language cognition rehabilitation assisting system and method based on a self-adaptive co-creation mechanism, and belongs to the field of medical artificial intelligence and psychological rehabilitation engineering. The system comprises five core modules, the modules are connected in a closed-loop mode in a data flow mode to form a sustainable and optimized interaction system, a semantic vector, an emotion vector and a task vector of child voice are fused, a fusion state vector is constructed through a dynamic weight generation network and a cross-modal attention mechanism, and the fusion state vector and the task vector are integrated. And generating a co-creation control instruction by adopting an attention-enhanced gating circulation unit and a strategy network, and further synthesizing an interactive voice signal with emotional expressive force. Synchronous fusion and linkage feedback of semantic understanding and emotion recognition can be achieved, the interaction strategy is dynamically adjusted according to the real-time state of children, strategy network parameters are optimized through online self-learning, and the naturalness, coherence and individual adaptability of rehabilitation interaction are improved.

The invention provides a hand rehabilitation training glove and system based on electromyographic signal guidance and pressure feedback, and the glove comprises a flexible glove main body which is made of an elastic material, is designed to fit the anatomical structure of a human hand, and is used for collecting bioelectrical signals generated by the remaining hand and forearm muscles of a patient when the patient actively intends to generate power; the embedded signal processing module is used for carrying out amplification, filtering, noise reduction and analog-to-digital conversion processing on the collected original electromyographic signals; and the main control unit is used for calculating time domain and frequency domain characteristic values of the electromyographic signals and identifying the motion intention of the patient based on a preset threshold value or an adaptive algorithm. Weak electromyographic signals are collected and processed in a high-precision mode and immediately converted into local touch pressure feedback corresponding to the functions of the weak electromyographic signals, neural pathway connection between the cerebral cortex and target muscles is enhanced, training interestingness and immersion and compliance of a patient are remarkably improved, and the training effect is improved. And key power is provided for continuous long-term rehabilitation training.

A device 100 for developing the concentration, the device 100 comprising: an optical sensor unit, the optical sensor unit comprising a plurality of one or more lenses 201, 202 capable of holding sensitive elements, the plurality of sensitive elements being configured to detect a user-provided biological signal in at least three operating modes; three switches for switching between the plurality of operating modes; a plurality of illumination units configured to indicate each of the plurality of operating modes by emitting a predetermined light signal; a processing unit for processing information using artificial intelligence.

The invention relates to a closed-loop vagus nerve intervention control system combined with meditation treatment, and the system comprises a physiological signal collection module which is used for collecting an EEG (electroencephalogram) signal and an ECG (electrocardiosignal) signal of a patient in real time; the data processing and analyzing unit is used for calculating heart-brain and brain-heart bidirectional coupling coefficients on the basis of the acquired signal data and evaluating the execution control function indexes of the patient in real time; the stimulation parameter regulation and control module is used for dynamically adjusting the frequency, the pulse width and the current intensity of the transaural vagus nerve stimulation taVNS according to the calculated bidirectional coupling coefficient; the meditation guidance module is used for providing personalized meditation guidance based on the real-time heart and brain state; the feedback control module is connected with the stimulation parameter regulation and control module and the meditation guidance module and is used for realizing collaborative optimization of stimulation parameters and meditation intervention through a closed-loop adaptive mechanism; and the execution control evaluation module is connected with the feedback control module and is used for improving the risk decision-making capability of the system for joint meditation treatment by setting a plurality of task evaluation modes.

The invention relates to the field of medical treatment, and discloses a method for treating chronic insomnia by percutaneous magnetic stimulation of stellate ganglion block, which comprises the following steps: preprocessing: determining the initial position of stellate ganglion, tissue initial parameters and initial stimulation parameters through multi-modal image positioning and initialization to obtain reference data; dynamic sensing: collecting neck tissue displacement data, temperature field data and electromyographic signals in real time, and calculating spatial position variation of stellate ganglions and time-varying parameters of tissue electromagnetic characteristics; and modeling optimization: constructing a time-varying electromagnetic field distribution model based on the reference data, the spatial position variable quantity and the time-varying parameters. The method comprises the following steps: acquiring neck tissue displacement, a temperature field and an electromyographic signal in real time through reference data, capturing a stellate ganglion space position change and tissue electromagnetic property time-varying rule, constructing a time-varying electromagnetic field distribution model, and solving target stimulation parameters adaptive to a real-time tissue state through an optimal control algorithm.

The invention discloses a portable fatigue driving monitoring and early warning system, and relates to the technical field of fatigue early warning. Comprising an acquisition module, a control module, a reminding module and a power module. The input end of the control module is connected with the output end of the acquisition module, and the output end is connected with the reminding module. Fatigue state judgment and intervention are carried out by collecting and analyzing the forehead lobe single-channel electroencephalogram signals of the user on the basis of integrated independent equipment, the process does not need to depend on intelligent terminal equipment such as an intelligent vehicle, an intelligent mobile phone or a cloud server, and power consumption and system complexity are reduced. The invention provides a set of non-electroencephalogram signal component identification and elimination method based on independent component energy spectrum, spectrum entropy and correlation analysis, interference of artifacts such as eye movement and myoelectricity is effectively inhibited, and the accuracy and reliability of fatigue monitoring of portable equipment are improved.