68 results about "Skin conductance" patented technology

The invention relates to the technical field of nervous system dysfunction, in particular to a nerve regulation and control system for degenerative disease treatment based on multi-modal physiological feedback, which comprises a central processing unit, an optical radiation applicator, a mechanical vibration applicator, an integrated biological signal sensing array and a man-machine interaction module, cooperative stimulation is applied by using a multispectral light source and a broadband vibrator, and multi-dimensional physiological signals such as heart rate variability, myoelectricity, galvanic skin and the like are monitored in real time through a sensor array. A multi-parameter adaptive control algorithm built in the central processing unit can dynamically and intelligently adjust stimulation parameters based on the feedback signals to form an accurate personalized treatment closed loop. Meanwhile, the safety monitoring module based on the biological thermal model ensures the safety boundary of the treatment process. According to the invention, intelligent, self-adaptive and non-invasive treatment of nerve dysfunction is realized.

The invention provides a group psychological data fluctuation early warning method based on VR emotion data processing, which comprises the following steps: acquiring multi-modal physiological signals such as electroencephalogram, heart rate variability and skin conductance of a user and behavior data such as voice, expression and virtual trajectory in real time, performing sliding window processing and feature extraction, fusing into multi-dimensional features, inputting the multi-dimensional features into an emotion recognition model, and performing emotion recognition on the emotion recognition model; generating an individual emotion dynamic sequence; a dynamic heterogeneous graph is constructed by combining the social relation and the emotion similarity, the emotion influence intensity between nodes is judged by using a dual-channel graph attention mechanism, and a propagation path is traced through an integral gradient method, so that interpretable group emotion diffusion mode recognition is realized; according to the method, a typical propagation mode and a time sequence convolutional network are combined, a group emotion intensity evolution trend is predicted, when fluctuation exceeds a threshold value, an emotion propagation path thermodynamic diagram is automatically generated and early warning is pushed, and the interpretability of an emotion propagation path and prediction and response efficiency of group psychological fluctuation are improved.

The invention relates to the technical field of signal analysis, in particular to an acute pain assessment method and system based on an EDA signal, and the method comprises the following steps: collecting a skin electrical activity EDA signal of a subject; calculating an individual time-varying sympathetic nerve activity TVSymp curve; the method comprises the following steps: constructing a priori knowledge base containing a standardized pain response sympathetic nerve SpainSymp template; inputting the EDA signal and the individual TVSymp curve into a PCVAE model, fusing features and decoupling the features into pain common features and individual specific features; carrying out pain intensity classification, and reconstructing and generating a standardized SpainSymp curve of which individual specific characteristics are removed; and outputting the pain intensity classification result and the dynamic change trend of the standardized SpainSymp curve in real time. By adopting the method, the pain-induced physiological reaction and individual baseline variation can be effectively decoupled and separated from the mixed EDA signal, the interference of individual difference is avoided, and the method has the characteristics of high accuracy and strong real-time performance.

The evaluation method of thermoregulatory function is characterized by comprising: a process of acquiring an autonomic nerve index of a subject; a process of acquiring a sweating index of the subject; and an evaluation process of evaluating a thermoregulatory function of the subject using the autonomic nerve index of the subject and the sweating index of the subject. Preferably, the autonomic nerve index comprises at least one of a heartbeat variation index and a pulse variation index, and the sweating index comprises at least one of a skin conductance and a sweating amount.

The application discloses a blood pressure monitoring device based on multi-source physiological parameter adaptive control, comprising a blood pressure measurement module, an electrocardiogram measurement module and a processing module. The blood pressure measurement module comprises a microcontroller unit and an air pump and a deflation valve controlled thereby. The processing module acquires multi-source physiological parameters and controls the air path accordingly: when the skin conductance response is high, the deflation time is shortened to reduce the stress stimulus of the patient; when the pulse pressure difference of the initial measurement is large, the deflation frequency is increased to adapt to the rapid change of blood pressure and avoid false hypotension; when the electrocardiogram QT interval is long, the inflation time is prolonged to ensure measurement accuracy. The application dynamically adjusts the inflation and deflation physical process through multi-source physiological feedback, significantly improving the measurement accuracy and adaptability of the device under complex physiological conditions.

The invention discloses a real-time monitoring-based implant operation pain self-adaptive regulation and control method and system, and belongs to the field of biomedical engineering. The method comprises the following steps: establishing a tooth position-myoelectricity mapping relation according to an implant tooth position, and deploying bilateral myoelectricity electrodes to establish an individualized myoelectricity baseline; event-related myoelectricity response and skin conductance response are calculated, and pain side positioning is carried out; evaluating the anxiety level, and quantifying and correcting the pain intensity; based on the corrected pain intensity index and anxiety level index, performing state comprehensive judgment by adopting fuzzy logic; self-adaptive regulation and control including cognitive intervention, physical intervention, drug intervention and joint regulation and control are executed according to the state judgment result, and hierarchical upgrading is carried out according to the regulation and control effect. According to the invention, scientific distinguishing between pain and anxiety, accurate positioning of pain side identification and closed-loop self-adaptive regulation and control are realized.

The invention relates to the technical field of evaluation of heart rehabilitation curative effects of breast cancer chemotherapy patients, in particular to a method and system for evaluating the heart rehabilitation curative effects of the breast cancer chemotherapy patients, and the method comprises the following steps: continuously obtaining heart rate variability data and skin conductance data of the breast cancer chemotherapy patients; based on the heart rate variability data and the skin conductance data, the activation state of the autonomic nervous system of the patient is judged; according to the activation state of the autonomic nervous system of the patient, quantifying the intensity and duration of the internal emotional state of the patient, and generating an emotional energy index; when the conventional physiological indexes of the patient fluctuate, determining a physiological fluctuation attribution result in combination with the emotional energy index to distinguish whether the physiological fluctuation is derived from heart function change or emotional influence; and evaluating the cardiac rehabilitation effect of the patient based on the physiological fluctuation attribution result. The accuracy of heart rehabilitation curative effect evaluation can be improved.

This invention discloses a neurophysiological behavior-driven multimodal intelligent learning guidance system and method. The system achieves closed-loop control by constructing a cognitive-emotional-task adaptive adjustment space: 1) In the cognitive dimension, it integrates EEG neural oscillations and frontoparietal network connections, fNIRS cerebral oxygen metabolism indicators, and eye-tracking data to construct a multimodal cognitive load assessment model; 2) In the emotional dimension, it combines the nonlinear characteristics of skin conductance signals with facial expression recognition to quantify learners' motivation levels and emotional valence in real time; 3) In the task dimension, it parameterizes task difficulty and interaction form as moderating variables. During system operation, the system locates the learner's position on the cognitive-emotional state plane in real time and dynamically adjusts task dimension parameters through a nonlinear mapping strategy. This invention effectively utilizes the decoupling ability of multimodal signals and the interaction of intention to achieve precise adaptive delivery of teaching content.

A method of monitoring stress of a user includes receiving a plurality of time-series data inputs from a plurality of biometric sensor electrodes of a wearable computing device. The time-series data inputs includes continuous electrodermal activity data and at least one of heart rate data, skin temperature data, and heart rate variability data. The method also includes processing the time-series data inputs using a plurality of filtering techniques in sequence. Further, the method includes selecting a model from a plurality of models based on types of data inputs received as the time-series data inputs to calculate an indicator of a physiological response of the user at a certain time. Thus, the selected model is tailored to use all of the time-series data inputs in the calculation of the indicator of the physiological response. Further, the method includes controlling a function of the device when the indicator of the physiological response exceeds a threshold.

A system and a method for objectively assessing an experience undergone by an individual. The system comprises: a first sensing device, configured to detect a heart rate (HR) of the individual; a second sensing device, configured to detect a heart rate variability (HRV) of the individual; a third sensing device, configured to detect a body temperature (BT) of the individual; a fourth sensing device, configured to detect an electrodermal activity (EDA) of the individual; and a processing device, configured to: determine a current HR of the individual; determine a current HRV of the individual; determine a current BT of the individual; determine a current EDA of the individual; perform a comparison of the current HR, the current HRV, the current BT and the current EDA with respective recommended maximum thresholds; and provide a result of the comparison, in the form of a graphical representation and / or a summary indicator.

This invention discloses an intelligent exercise assessment and early warning system based on multi-source biosignal fusion, belonging to the field of exercise risk early warning technology. It collects multi-source data, including electromyography (EMG), heart rate, blood oxygen, skin conductance, and acceleration, from various wearable sensors to assess muscle fatigue, cardiovascular load, exercise status, and environmental factors. After preprocessing, the system uses quantitative models such as the muscle fatigue correction index and the cardiovascular load dynamic coefficient to calculate a comprehensive exercise risk early warning index by integrating physiological state and environmental factors. This results in a normalized risk classification, corresponding to low, medium, and high risk levels and marked with different color icons. This system overcomes the limitations of single signals, considering the synergistic effects of multi-dimensional physiological indicators and the influence of environmental factors, improving the accuracy of assessment in complex scenarios. It can effectively intervene before risks develop and is suitable for scenarios such as mass fitness and professional training. It is integrated into lightweight wearable devices without interfering with the exercise process.

This invention proposes a health monitoring method and device based on a smart bracelet. The method uses the smart bracelet as the executing entity and includes the following steps: S1: Acquiring a multimodal physiological signal set and an environmental interference data set of the monitored object. The multimodal physiological signal set includes electrocardiogram (ECG) signals, heart rate signals, blood pressure signals, blood oxygen saturation signals, skin conductance signals, and body temperature signals. The environmental interference data set includes motion state data, light intensity data, wearing contact pressure data, and ambient temperature data. S2: Preprocessing the multimodal physiological signal set and the environmental interference data set. This invention can achieve accurate, efficient, and personalized health monitoring, output health level and abnormality tracing information, and trigger early warnings when necessary. It is suitable for daily health management, exercise monitoring, and disease early warning scenarios for different groups such as children, adults, and patients with chronic diseases.

The invention relates to the technical field of intelligent early warning, in particular to a wrist-wearing type acute infarction disease heart symptom home early warning system which comprises a signal alignment module, a feature association module, a trend recognition module, a risk triggering module and an event feedback module, and based on an electric pulse signal waveform, the occurrence time node of an R wave is judged to serve as a reference. Synchronous acquisition and unified time sequence processing of multichannel signals are realized, electrocardiogram, skin conductance and micro blood flow data are integrated on the basis of the same cardiac reference point, a time window-oriented multi-factor dynamic comparison structure is constructed, the trend relationship among multiple parameters is identified through correlation analysis, and the accuracy of the comparison result is improved. A threatened characteristic period is accurately positioned by means of a common pattern screening mechanism, real-time monitoring and historical comparison are integrated in a feedback process, data cyclic utilization is enhanced, multi-source signal dynamic collaborative judgment is realized, the timeliness and stability of risk identification are further improved, and the high-confidence early warning requirement of home health monitoring is effectively met.

The invention discloses a heating temperature regulation and control system and method for a massage instrument, and relates to the technical field of massage instrument heating temperature control, and the system comprises a user data acquisition module, a massage site temperature regulation and control module, and a temperature curve construction and adaptive adjustment module. The user data acquisition module comprises an array pressure sensor and a skin electric signal sensor, receives pressure of massage positions of the massage instrument through the array pressure sensor, constructs and records pressure distribution diagrams of different massage positions, performs similarity judgment on the pressure distribution diagrams, divides the positions, and transmits the pressure distribution diagrams to the user data acquisition module; a skin electric signal sensor is used for receiving and recording a working surface skin electric signal in the current working process of the massage instrument, and a user database is established for file establishment based on a user id. According to the heating temperature regulation and control system and method for the massage instrument, the massage position can be recognized, the comfortable temperature of a user can be excavated, personalized massage instrument temperature regulation and control can be provided, and the practicability of the massage instrument in the actual use process is enhanced.

This invention discloses a coordinated detection circuit for exercise physiological parameters, including a physiological parameter power supply, a physical parameter power supply, a memory power supply, a skin conductance sensor, a photoelectric sensor, an attitude sensor, a memory, a communication module, and a battery charging module.

This invention discloses a method for processing and analyzing VR emotion regulation task monitoring data based on multimodal data, belonging to the field of VR emotion monitoring technology. The method includes: collecting multi-source physiological time-series data such as heart rate variability, skin conductance response, EEG rhythm, and eye movement trajectory through a VR scene emotion association mapping gateway; eliminating data sampling frequency differences and time offsets through multi-source physiological time-series alignment processing; then filtering key feature subsets through high-dimensional feature dimensionality reduction and distillation; utilizing emotion fluctuation gradient tracking to analyze the dynamic fluctuation patterns of analytical features with VR scene changes; establishing a mapping relationship between analytical features and VR scene parameters; generating an emotion-scene association feature matrix; and finally, completing comprehensive processing and analysis of monitoring data based on this matrix to output dynamic emotion change association data. The method achieves deep integration of multimodal data and VR emotion regulation tasks through step-by-step feature extraction, fluctuation tracking, and scene mapping processes, improving the accuracy and targeting of data processing.

The invention discloses a ship cabin healthy environment dynamic regulation and control method and system based on multi-modal data fusion and deep learning. The method comprises the steps that multi-modal environment data and user physiological data (such as heart rate, body movement and skin conductance) are collected through a multi-sensor network; preprocessing the data; extracting space and time features by using a hybrid model of a convolutional neural network and a long-short-term memory network, and generating a fusion feature vector; generating a user health state score based on a deep reinforcement learning framework; environment parameter adjustment amounts (such as temperature, humidity, air purification intensity and illumination adjustment amount) are calculated through a fuzzy logic controller; and realizing closed-loop regulation and control by controlling the environment equipment through the actuator network. The problems of inaccurate health risk assessment and regulation response lag caused by single data and simple processing of an existing environment regulation system are solved, real-time, accurate and adaptive dynamic regulation is realized, and the safety and comfort of the cabin health environment are improved.

The invention provides a software usability defect detection method and device, a storage medium and electronic equipment, and relates to the technical field of computers.The method comprises the steps that multi-modal data generated when a user operates a user interface of software is collected, and the multi-modal data comprises an operation log, an eye movement track and a skin conductance signal; calculating the absolute change rate of the skin conductance signal in a set time window and the relative change rate of the relative resting reference; if the absolute change rate is greater than a first threshold value and the relative change rate is greater than a second threshold value, judging that the user is in an abnormal state; in response to the judged abnormal state, performing fusion analysis on the eye movement track of the occurrence time period of the abnormal state and the operation log to obtain a fusion analysis result; and under the condition that the fusion analysis result shows that at least one of the eye movement track and the operation log is abnormal, determining that the user interface has the usability defect. The accuracy of usability defect judgment can be improved.

The invention discloses a sleep management system based on a smart watch, and relates to the technical field of health management, the system comprises a multi-modal data fusion acquisition module, a dynamic sleep stage division model, a personalized sleep intervention engine and a closed loop feedback optimization module; the personalized sleep intervention engine generates and executes a personalized intervention scheme based on a sleep stage result and a user portrait output by the dynamic sleep stage division model; the multi-modal data fusion acquisition module adds oxyhemoglobin saturation (SpO2), skin conductivity (EDA) and environmental parameters (temperature, humidity and illumination), and compared with two-dimensional data in the prior art, 8-dimensional fusion data can capture subtle features of a sleep state (such as association between SpO2 sudden drop caused by night hypoxia and interruption of a deep sleep period and a sleep stress state reflected by skin electrical activity fluctuation); and a data foundation is laid for accurate identification.

The invention relates to the technical field of stroke monitoring, and discloses a method and a system for detecting the stroke risk after waking of a home elder based on water and electricity information, and the method comprises the steps: synchronously obtaining household water, electricity and body data in a morning waking monitoring time period, and generating a morning waking water event sequence and an electricity load sequence through flow sequence form segmentation and electricity event steady-state analysis; morning awakening physiological indexes are formed according to the skin electricity, the heart rate and other data; determining a unified time reference according to a morning beginning behavior completion moment or a physiological composite sign, constructing an alignment time window, and mapping the three types of data to a discrete time grid; establishing double-hypothesis probability inference, respectively constructing water utilization, power utilization and body three-channel observation likelihoods, and performing joint fusion with a dependency relationship to obtain a current occurrence probability; and establishing a survival analysis model by using the time variation covariable in the aligned time window, and giving the occurrence probability of the future time period. The method does not depend on videos, and the judgment capability can be kept when the body data are missing.

The invention discloses a dual-mode electrophysiology acquisition system and method for a multi-mode fingertip monitor, and belongs to the technical field of medical electronics and biological signal measurement. The system is integrated in fingerstall type wearable equipment supporting double working modes of high-fidelity recording and continuous monitoring, and is characterized by comprising at least two electrodes in contact with the skin; the high-precision bioelectricity analog front-end chip conforms to the medical standard; a low power consumption bioelectricity monitoring unit; and the channel switching circuit is controlled by the main controller. The high-precision analog front-end chip is configured to switch or perform time division multiplexing on at least one signal input channel between a first configuration for collecting electrocardiogram signals and a second configuration for collecting skin electrical activity signals by dynamically reconfiguring an internal register of the high-precision analog front-end chip; therefore, hardware-level synchronous acquisition of two kinds of bio-electricity signals in a high-fidelity recording mode is realized, and sampling of the two kinds of bio-electricity signals is based on the same main clock source. Through the architecture of dual-core isomerism, mode driving and hardware dynamic multiplexing, the contradiction between medical-level precision electrocardio / skin electricity synchronous acquisition and ultra-long endurance continuous monitoring which are simultaneously realized in micro fingerstall equipment is solved.

The invention discloses an environment control method and system based on wind speed sensitivity, and the method comprises the steps: carrying out the weakness grade division (non-weakness, weakness early stage and weakness) of old people according to a Fried weakness phenotype; secondly, through a subjective thermal sensation voting (TSV) experiment, personalized wind speed threshold models of old people with different weakness levels under various operation temperatures are established; then, monitoring the head skin temperature and the sweat amount of the user in real time by using an infrared thermal imager and a skin conductivity sensor; and finally, based on the real-time physiological data, the environment temperature and a preset wind speed threshold model, the wind speed of the fan is controlled through a dynamic adjustment algorithm, an attenuation mode is started when the wind speed is close to a tolerance threshold, and meanwhile the environment temperature is cooperatively adjusted. According to the invention, precise and dynamic environment regulation and control of the elderly, especially the weak elderly in the early stage is realized, the thermal comfort is effectively improved, discomfort caused by too strong wind speed is avoided, and the system is especially suitable for the scenes such as nursing homes and apartments for the elderly.

The invention relates to the field of medical data processing, and discloses an endometriosis pain emotion pressure data management system based on AI prediction, which comprises a mobile terminal, an Internet of Things treatment monitoring device, an expiration detection device and a data processing server. The expiration detection device is used for detecting volatile organic compounds in expired gas by utilizing a sensor array; the Internet of Things treatment monitoring device collects heartbeat interval and skin conductivity data and executes transcutaneous nerve electrical stimulation; and the server runs the deep learning model. According to the invention, time alignment and feature splicing are carried out on physiological, expiration and behavior data through a multi-source heterogeneous data fusion module, and the pain occurrence probability is predicted by using a long-short-term memory network and an attention mechanism; early warning is generated according to the prediction result, the electrical stimulation frequency and waveform are adjusted in a closed-loop mode based on the real-time skin conductivity change slope, and multi-dimensional objective monitoring, pain risk active prediction and self-adaptive closed-loop intervention of endometriosis are achieved.

The application discloses a multi-modal physiological data fusion driven mental stress classification method, and belongs to the technical field of physiological signal processing and health monitoring. The method comprises the following steps: pre-processing electrodermal activity, body temperature, acceleration, heart rate and blood oxygen saturation signals; adopting an NSGA-II algorithm to adaptively optimize multi-modal fusion weights; realizing data-level fusion through weighted average; extracting statistical features and eliminating redundant features based on correlation analysis; and finally classifying by using a convolutional neural network with an enhanced multi-head attention mechanism. Through multi-scale fusion and adaptive weight optimization, the application improves classification accuracy and robustness, and is suitable for wearable devices, mental health monitoring and other fields.

This invention discloses a visual early warning device and method for cardiovascular diseases in obese children, relating to the field of early warning measurement technology. The method includes the following specific steps: S1: Simultaneously collecting cardiovascular-related parameters and status data of children in both clinical and home modes using a flexible wearable device; S2: Correcting the collected physiological parameters through motion artifact filtering and establishing a correlation model between skin conductance response and cardiovascular parameters to correct for emotional interference in diagnosis; S3: Constructing visual interactive interfaces for children and adults to display parameters and provide emotional guidance; S4: Constructing an early warning indicator system based on children's baseline indicators and metabolic-related indicators, classifying early warning levels, and displaying, transmitting, and storing the data through the adult visual interface. This invention improves the convenience and success rate of early warning measurement by determining the emotional state during measurement through motion artifact filtering and emotional interference correction.

The invention provides a cleaning and nursing device and method with a warm keeping function for nursing in the obstetrics and gynecology department. Biological thermal field distribution of a target puerpera nursing area is constructed through multi-dimensional thermal field data of the nursing area; determining an interference compensation factor of environmental heat loss by combining the biological thermal field distribution with the temperature and humidity parameters of the surrounding environment of the nursing area in the cleaning and nursing process; determining the thermal response coefficient of the biological tissue of the target puerpera nursing area to the current temperature by combining the biological thermal field distribution with the skin conductivity of the target puerpera nursing area; and according to the interference compensation factor and the thermal response coefficient, generating a feedforward compensation amount of nursing area temperature control in the cleaning and nursing process, and further performing feedforward-feedback composite adjustment on the output power of the heating body based on the feedforward compensation amount. According to the scheme, a feedforward-feedback composite temperature control mechanism can be constructed through multi-dimensional thermal field sensing of the puerpera nursing area in combination with individual physiological characteristics and environmental heat loss interference to achieve accurate temperature control of the nursing area.

The invention discloses a head physiotherapy instrument control method and system based on a multi-element stimulation mode, and relates to the technical field of head physiotherapy, and the method comprises the steps: calculating a comprehensive emotional state value through a fusion algorithm according to the geometric features, deep expression features, voice emotion scores and skin electric emotion scores of a face region, and obtaining a comprehensive emotional state value; judging an emotion category according to the emotion state value, and selecting a physiotherapy instrument mode according to the emotion category; when the physiotherapy instrument is used, a skin conductance value is collected in real time, heart rate variability data is collected through the photoelectric volume pulse wave sensor, and the physiotherapy instrument mode is dynamically adjusted through the skin conductance value and the heart rate variability data. According to the method, the facial image, the voice signal and the skin conductance data are comprehensively collected, deep learning and machine learning algorithms are combined, the emotional state of the user is accurately evaluated, and the problems of low emotional evaluation precision, fixed treatment mode and the like in the prior art are solved.

The invention discloses a driver state monitoring method and device based on AR glasses and electronic equipment, and relates to the field of auxiliary driving, and the method comprises the steps: obtaining the physiological reference data of a driver within a preset time threshold value, and synchronizing the initial state data of a vehicle; the physiological reference data comprises a PERCLOS value, a skin conductance baseline and a basal body temperature, and the vehicle initial state data comprises an air conditioner temperature, a fragrance concentration and an ADAS working state; physiological signals, motion state signals and environment data of a driver are collected through a sensor assembly integrated on the AR glasses; performing noise reduction and standardization processing on the physiological signal, the motion state signal and the environmental data of the driver; inputting the processed data into a preset model for analysis, and obtaining a driver state quantification result; according to the driver state quantification result, a third-level safety response instruction and a comfort level optimization instruction are generated; and outputting the third-level safety response instruction through AR glasses, and outputting a comfort level optimization instruction through a vehicle control interface.

A computer-implemented method for measuring skin conductance of a writing instrument's user, including: applying a DC voltage to user's fingers contacting the writing instrument during a writing session with the writing instrument; measuring a current generated by reverse iontophoresis following applying the DC voltage; calculating an average skin conductance over a specific timeframe from the measured current; storing calculated average skin conductance data as historical data; comparing the average skin conductance to historical data from the same user or to reference data from a group of users; and providing an indication to the user in case that the skin conductance has decreased and / or the calculation results.

The invention provides an anti-carsickness seat active compensation control method and system based on physiological signal monitoring, and the method comprises the steps: collecting a physiological signal of a passenger, the physiological signal comprising at least one of heart rate, heart rate variability and skin conductance level; collecting current motion state information of the vehicle; carrying out fusion processing on the physiological signal and the motion state information of the vehicle to calculate a real-time carsickness risk level of the passenger; based on the carsickness risk level and the motion state information of the vehicle, a seat motion control instruction is generated, and a seat is driven to perform compensation motion according to the seat motion control instruction, so that individuation of an anti-carsickness intervention strategy, intellectualization of a judgment process and automation of an execution process are realized; finally, the fundamental purpose of effectively improving the comfort of passengers is achieved, and real personalized active intervention is achieved.