56 results about "Abdominal movements" patented technology

The invention relates to the technical field of rehabilitation assistance, in particular to an intelligent breathing assistance system for lung rehabilitation, comprising: a breathing part, the breathing part comprising a breathing cylinder, the breathing cylinder comprising a fixed cylinder and a folding cylinder, the fixed cylinder being internally provided with an auxiliary assembly; the wearing part comprises a chest cover and an abdomen cover, a connecting channel and a vibration channel are formed in the chest cover, an air bag is embedded in the abdomen cover, and a vibration phlegm eliminating assembly is arranged in the vibration channel; the thoracico-abdominal movement mode recognition unit is used for detecting a breathing mode of a user; the lung function detection unit is used for calculating and detecting the tidal volume of a user; the personalized training control unit is used for adjusting breathing resistance according to the tidal volume and breathing modes, according to different breathing modes of users, the breathing assisting force is adjusted through kinetic energy of gas exhaled by the users, key parameters such as the tidal volume and the recovery condition are monitored in real time, the lung rehabilitation effect is further improved, and the lung rehabilitation effect is improved. And more accurate and effective breathing adjuvant therapy is realized.

The invention belongs to the technical field of data processing, and particularly discloses an intelligent bellyband for household respiratory rehabilitation and a management system.The intelligent bellyband and management system comprises the following steps that thoracic and abdominal movement data are collected through a flexible sensor array, and the thoracic and abdominal movement data comprise thoracic expansion amplitude, abdominal pressure change and blood oxygen saturation information; constructing an initial multi-mode signal set; according to the initial multi-mode signal set, a recurrent neural network is adopted to extract time sequence features, and dynamic feature representation is obtained and used for follow-up fusion processing; performing priority evaluation on respiratory training quality from the dynamic feature representation, and determining key signal subsets to identify potential deviations; if the key signal subset meets a triggering condition, video auxiliary analysis is carried out; the invention aims to solve the problems of multi-mode signal complexity and inaccurate real-time feedback in respiration data acquisition and analysis.

The embodiment of the invention provides a respiration monitoring analysis method and system based on multiple sensors, and the method comprises the steps: firstly building a respiration signal linkage relation among a plurality of sensors, such as a chest motion sensor, an abdomen motion sensor, an airflow sensor and an environment gas sensor; generating a sensor linkage relation table according to respiration phase correlation of output signals of the sensors, synchronously collecting respiration related signals of the sensors based on the sensor linkage relation table, arranging and generating a respiration analysis sequence according to a signal correlation sequence, and dynamically adapting to a preset respiration analysis model according to characteristic dimensions of the respiration analysis sequence. Adjusting model parameters to obtain an adapted target breathing analysis model, inputting a breathing analysis sequence into the target model, processing multi-sensor signal association information through a linkage feature analysis module, extracting abnormal association features in a breathing process, and finally integrating abnormal performance and association relationships of the sensor signals according to the abnormal association features to obtain a breathing analysis result. And generating a breathing state evaluation result.

The disclosure provides systems and methods for treating obstructive sleep apnea using an inertial measurement unit (IMU) comprising an accelerometer and a gyroscope, wherein the IMU is configured to detect chest and / or abdominal movement by a patient during the inspiration and expiration stages of a respiratory cycle and to generate positional data based on the detected movement. Positional data generated by the IMU is used by an implanted stimulation system to determine when to deliver electrical stimulation to a nerve which innervates an upper airway muscle, such as the hypoglossal nerve, to treat sleep apnea.

The invention discloses an intelligent critical case early warning analysis system based on multi-source clinical data fusion, belongs to the technical field of image processing, and can more accurately carry out respiration monitoring and early warning. Comprising a respiratory movement analysis module used for generating a respiratory movement amplitude change curve according to thoracic and abdominal movement video data; the breathing machine waveform analysis module is used for generating waveform characteristic data containing abnormal time slices according to the breathing machine waveform data; the blood oxygen signal processing module is used for generating a blood oxygen change trend vector according to the blood oxygen saturation degree signal data; the multi-modal fusion module is used for generating a multi-dimensional physiological feature vector under a synchronous timestamp according to the breathing movement amplitude change curve, the waveform feature data and the blood oxygen change trend vector; the risk assessment module is used for generating a breathing compensation state score according to the multi-dimensional physiological feature vector; and the alarm triggering module is used for triggering a grading alarm signal according to the deviation degree of the breathing compensation state score and the baseline range.

The present application relates to the technical field of gastrointestinal nursing, and particularly relates to a nursing device, which comprises: an outer shell, wherein the outer shell is in an arc-shaped structure, and a plurality of pressure applying members for massaging the abdomen of a patient are arranged on the outer shell; a bandage connected to the outer shell, wherein the bandage can make the outer shell closely adhere to the abdomen of the patient; an adjusting mechanism arranged between the bandage and the outer shell, wherein the adjusting mechanism comprises a plug-in assembly and a tightening assembly, the plug-in assembly can connect one end of the bandage to the outer shell, and the tightening assembly can change the length of the bandage; and a power assembly arranged on the outer shell, wherein when the power assembly is in action, the pressure applying members can be sequentially driven to move towards the abdomen of the patient, so that the device can be suitable for people with different body types.

The present invention discloses a head piece, such as a headband or helmet, that fits around the cranium and includes one or more contractile elements which contract in synchrony with inspiration during sleep. The head piece is connected to a breath sensor which signals the timing of inspiration and software which incorporates the generated timing signals from the breath sensor to control the timing and release of the contraction of the head piece to coordinate with the user's breath. The breath sensor may comprise a variety of mechanisms such as a flow valve in the tubing of a CPAP or other type of automated breathing machine, an anemometer such as a hot wire anemometer to directly detect airflow in front of the nose or mouth, acoustic sensors to record breathing sounds, or a radar detector that monitors chest or abdomen movements from above the bed. In the illustrated embodiments, the breath sensor is a chest band that signals inspiration by recording an increase in tension produced by expansion of the chest. Coupling expansion of the chest with compression of the cranium is advantageous, because these movements occur simultaneously and because the expansion of the chest occurs with much greater force than the compression of the cranium, therefore the natural expansion of the chest can power the compressive forces which are desirably applied to the cranium. The chest band and head piece can be coupled to provide such a self powered head pumping mechanism by an electrical connection or a tube that permits flow of a liquid or air between the two areas.

The invention relates to a sleep respiration diagnosis method and system based on a simple brain electrode, and belongs to the technical field of sleep monitoring. The method comprises the steps that electroencephalogram data and sleep respiration data are obtained, and timestamps are added to the electroencephalogram data and the sleep respiration data; the time sequence electrocardio data, the time sequence electroencephalogram data, the time sequence oxyhemoglobin saturation data and the time sequence thoracoabdominal movement data are preprocessed; presetting a unit time length, and performing segmentation processing on the electrocardio preprocessing data, the electroencephalogram preprocessing data, the oxyhemoglobin saturation preprocessing data and the thoracoabdominal movement preprocessing data according to the unit time length; extracting electrocardio feature data, electroencephalogram feature data, blood oxygen feature data and thoracoabdominal movement feature data; performing normalization and parallel fusion on the electrocardio feature data, the electroencephalogram feature data, the blood oxygen feature data and the thoracico-abdominal motion feature data to obtain a splicing vector, and performing calculation through a classifier to obtain sleep respiration diagnosis data. The sleep breathing equipment is simplified, and the accuracy of sleep diagnosis is improved.

The utility model discloses a pediatric antidiarrheal traditional Chinese medicine application tape which comprises an application main body, a medicine storage groove is arranged in the middle of the inner side of the application main body, antidiarrheal traditional Chinese medicine is filled in the medicine storage groove, four groups of separation lines are arranged at the top of the application main body, adhesive sticker is arranged at the bottom of the application main body, and the adhesive sticker is arranged in the medicine storage groove. Four groups of protective paper B and one group of protective paper A are arranged at the bottom of the adhesive sticker; according to the utility model, the application main body, the separation lines and the adhesive sticker are matched with one another, so that the application belt can be divided from the separation lines according to the body type of a child, and the application main body can be more attached to the skin, can be more suitable for the abdominal movement range of a user compared with square application, and is larger in adhesion area compared with cross-shaped application; the sticking effect is better, the sticking is not easy to fall off, and the use stability is improved.

The application provides a method and system for promoting diaphragm recovery by abdominal compression based on respiratory depression recognition, relates to the technical field of respiratory function rehabilitation, and comprises the following steps: acquiring chest and abdominal movement signals and respiratory airflow signals, calculating respiratory state representation parameters to determine respiratory depression, calculating pressure conduction efficiency of a compression point based on the chest and abdominal movement signals and establishing a pressure superposition constraint relationship, generating force coordination control parameters and periodic compression control instructions, and driving a compression device to apply mechanical stress and realize real-time optimization. The method can accurately identify the respiratory depression state, accurately control and adaptively optimize the compression force, and effectively promote diaphragm function recovery.

The invention discloses a home respiratory rehabilitation multi-modal intelligent management system based on a reinforcement learning algorithm, which belongs to the crossing field of artificial intelligence and medical health technology, and comprises a multi-modal physiological signal acquisition module, a graph attention feature fusion module, a near-end strategy optimization rehabilitation decision module and a long-short term memory health prediction module, the multi-modal physiological signal acquisition module acquires breathing audio, thoracic and abdominal movement, oxyhemoglobin saturation and heart rate variability signals in real time, the graph attention feature fusion module constructs multi-modal data into a heterogeneous graph structure, and cross-modal correlation features are extracted through a graph attention network. The near-end strategy optimization rehabilitation decision-making module generates a personalized rehabilitation training strategy through an actor reviewer architecture, the long and short-term memory health prediction module predicts a respiratory function deterioration risk value and dynamically adjusts reward function parameters, and the four modules form a deep coupling closed-loop collaborative system, so that strategy optimization of risk perception is realized; and the household rehabilitation effect is improved by 45%.

An acquisition unit acquires image data indicating at least a chest and an abdomen of a test subject. A detection unit detects the displacement of each of the chest and abdomen of the test subject by using the image data. A display control unit performs control so that the movement image of each of the chest and abdomen is displayed based on the detected displacement of the chest and abdomen. When the movement of the chest and the movement of the abdomen are synchronized with each other, the display control unit performs control so that the display form of the chest movement image and the display form of the abdomen movement image are the same as each other.

The invention provides a neural network model training method, a mouth-nose airflow signal reconstruction method and equipment. The method is applied to radar signal processing. The method comprises the following steps: acquiring a plurality of thoracico-abdominal movement signals acquired by a millimeter wave radar for a target object; processing the plurality of thoracico-abdominal movement signals by using a neural network model to obtain a reconstructed mouth-nose airflow signal and an apnea prediction result; calculating a regression error according to the reconstructed mouth-nose airflow signal and the real mouth-nose airflow signal, and calculating an apnea prediction error according to an apnea prediction result and an apnea label; performing weighted calculation according to the regression error and the apnea prediction error to obtain integration loss; and performing back propagation and gradient updating on the neural network model according to the integration loss, and optimizing model parameters. The mouth-nose airflow signal reconstructed by the neural network model is finer, more comprehensive and more accurate.

The invention relates to the technical field of respiration monitoring, in particular to a human respiration and thoracico-abdominal movement monitoring device which comprises a shell used for installing a respiration monitoring module, and binding belts are fixedly connected to the two sides of the shell; a driving mechanism used for driving the respiration monitoring module to be attached to the chest skin is arranged in the shell, and the respiration monitoring module is electrically connected with a signal transmission module used for transmitting deformation signals to the outside in real time. A first pressure sensor used for detecting first pressure data of pressure applied to chest adipose tissue and a second pressure sensor used for detecting second pressure data of pressure applied to chest skin are fixedly connected to the side, close to the binding belt, of the shell. A control panel of the signal transmission module is used for controlling a driving mechanism according to the change condition of the first pressure data and the second pressure data; the sensor is attached to the thoracoabdominal skin surface through pressure change in the wearing process of a patient, the data collection accuracy in the monitoring process of the sensor is guaranteed, and the breathing data monitoring accuracy is improved.

The present invention relates to the field of rehabilitation assistance technology, and particularly relates to an intelligent breathing assistance system for pulmonary rehabilitation, comprising: a breathing part, the breathing part includes a breathing cylinder body, the breathing cylinder body includes a fixed cylinder body and a folding cylinder body, and an auxiliary component is provided in the fixed cylinder body; a wearing part, the wearing part includes a chest cover and an abdominal cover, a connection channel and a vibration channel are opened in the chest cover, an airbag is embedded in the abdominal cover, and a vibration expectoration component is provided in the vibration channel; a chest-abdominal movement pattern recognition unit for detecting the breathing pattern of a user; a pulmonary function detection unit for calculating and detecting the tidal volume of the user; a personalized training control unit for adjusting the breathing resistance according to the tidal volume and the breathing pattern. According to different breathing patterns of the user, the present invention utilizes the kinetic energy of the exhaled gas of the user to adjust the breathing assistance force, and real-time monitors key parameters such as tidal volume and recovery condition, further improving the effect of pulmonary rehabilitation and realizing more accurate and effective breathing assistance treatment.

The invention belongs to the technical field of DAS, discloses a three-dimensional dynamic path planning algorithm for a liver interventional puncture operation, and aims to solve the problem that the complexity of an operation environment is increased due to abdominal movement caused by respiratory movement change of a patient in a liver interventional operation. According to the method, collision punishment is calculated through a respiratory movement model to avoid the influence of abdominal movement on path planning, focus positioning is actively updated through a reward function according to observation data collected in an operation, and dynamic active tracking of the accurate position of a target spot is achieved. The three-dimensional optimal path from puncture to the target spot is sought in a dynamic environment, accurate dynamic surgery simulation is achieved, and the most safe, accurate and effective treatment purpose aiming at the treatment target spot is achieved.

The invention discloses a device for simulating respiratory thoracico-abdominal movement of a neonate, and relates to the technical field of physiological sign simulation, the device comprises a neonate simulation model, the thoracic position of the neonate simulation model is provided with a displacement generator A used for simulating up-and-down displacement of the thorax caused by the respiration and heartbeat process; and a displacement generator B for simulating up-and-down displacement of the abdomen during abdominal respiration of the newborn is arranged at the abdomen position of the newborn simulation model. The invention aims to provide a device for simulating up-and-down thoracico-abdominal movement caused by respiration and heartbeat of a neonate, and the device is particularly suitable for occasions of research, development and calibration of non-contact respiration monitoring equipment for the neonate (or a premature infant), and is also suitable for teaching occasions of teaching respiration characteristics of the neonate to pediatricians or nurses. The defects that an existing breathing simulation device cannot simulate breathing and heartbeat movement at the same time and cannot simulate rapid and large-dynamic-range displacement of the chest and abdomen at the same time due to the fact that the transient response speed is low are overcome.

To provide a hybrid continuous positive pressure and surface imaging system for use in a radiotherapy system.SOLUTION: The hybrid continuous positive pressure and surface imaging system includes: a positive pressure unit adapted to apply a continuous positive pressure to lungs of a patient during radiotherapy; a surface camera system configured to continuously, or at intervals, capture body surface images of the patient; and a processing unit configured to continuously, or at intervals, model chest and / or abdominal movement of the patient in response to the applied continuous positive pressure based on the body surface images.SELECTED DRAWING: Figure 1

The application discloses a wireless sensing method for quantitatively evaluating upper airway obstruction degree, and belongs to the technical field of biomedical signal processing. The method comprises the following steps: collecting chest and abdominal movement and respiratory sound signals, and obtaining physiological signals and labels to process the chest and abdominal movement and respiratory sound signals; performing feature extraction on the processed chest and abdominal movement and respiratory sound signals through a recurrent neural network; iteratively training a hypergraph neural network to obtain a trained respiratory parameter quantification model, fusing the extracted features and inputting the features into the respiratory parameter quantification model, outputting ventilation drive and tidal volume prediction results through operation of the respiratory parameter quantification model, and thus obtaining an upper airway obstruction degree result. Compared with the prior art, the method has the advantages that the upper airway obstruction degree quantification method based on hypergraph dual-modal signal fusion is combined with LSTM and self-attention mechanism to extract deep learning features, and the completeness and accuracy of the evaluation result are improved.

The invention relates to the technical field of biomedical radar signal processing and artificial intelligence crossing, in particular to an attention-based thoracico-abdominal movement signal segmentation method and system and a storage medium, and the segmentation method comprises the following steps: obtaining a physiological signal obtained by detecting a human body through a millimeter wave radar; performing feature extraction on the acquired physiological signals to obtain a multi-scale feature map; performing fusion of time domain attention, space domain attention and channel attention on the multi-scale feature map by using a constructed attention fusion model to obtain a fusion signal based on attention enhancement; and inputting the fusion signal based on attention enhancement into the segmentation network model to obtain a respiratory movement signal and a thoracico-abdominal movement signal. According to the method, the FMCW millimeter wave radar signal is processed by using the deep learning model, the related characteristics of the thoracic and abdominal movement are enhanced through an attention mechanism, the thoracic and abdominal movement signal is accurately segmented, and reliable input is provided for subsequent respiratory rate and heart rate calculation.

The invention discloses a sleep hypopnea type automatic discrimination method and system based on a multi-modal signal, and aims to solve the technical bottlenecks of strong subjectivity, poor consistency and limited single signal feature discrimination capability of the existing visual scoring method. The method comprises the following steps: collecting multi-modal signals in a polysleep pattern (PSG), wherein the multi-modal signals comprise nasal ventilation airflow, thoracico-abdominal breathing movement, blood oxygen saturation and other signals; extracting multi-dimensional features related to hypopnea typing, wherein the multi-dimensional features comprise an inspiration flow limited index, thoracico-abdominal movement coordination, an event termination mode, an awakening sequential relationship and a sleep stage associated feature; the features are fused and optimized based on a machine learning algorithm, a high-precision classification model is constructed, and automatic discrimination of obstructive and central hypoventilation is realized. According to the method, the key problems that a traditional method depends on manpower in hypopnea typing, feature utilization is not comprehensive, and judgment precision is insufficient are solved, and a reliable basis is provided for accurate diagnosis, typing and treatment strategy formulation of sleep breathing disorder.

The application provides a radiotherapy breath synchronization training device, an application system and a monitoring method, and relates to the technical field of medical instruments.The training device comprises a breathing mask, a wearable monitoring belt, a first sensor module, a second sensor module, a third sensor module and a central processing communication module.The first sensor module collects a breathing airflow signal, is suitable for breast radiotherapy patients and liver radiotherapy patients;the second sensor module collects chest and abdominal movement signals respectively, obtains chest and abdominal movement amplitudes and posture information, and is suitable for lung radiotherapy patients.The central processing communication module performs time synchronization, feature extraction, individualized template establishment and matching on multiple source signals, calculates an index, identifies invalid modes, and adaptively adjusts a training rhythm, a feedback mode and a training target.The application system can generate recommended parameters according to continuous training results, realizes monitoring, intervention and clinical parameter support closed-loop management of radiotherapy breath training.

The application discloses a pediatric wheezing severity intelligent evaluation method and system based on respiratory sound analysis, relates to the technical field of medical information evaluation, and comprises the following steps: acquiring multi-modal monitoring data composed of respiratory sound audio, chest and abdominal movement signals and blood oxygen saturation; calculating a respiratory compensation index reflecting a physiological compensation state based on the data and dynamically adjustable wheezing sound weight coefficients and chest and abdominal time difference weight coefficients; constructing a multi-dimensional drug response curve by spline interpolation using the index sequence; segmenting the curve and extracting physiological mode features to generate an identification vector; when the length of the identification vector exceeds a threshold value, dynamically optimizing the weight coefficients by a preset maximum value function based on the deviation ratio of each component of the identification vector to the threshold value, the direction similarity and the length of the reference vector; and finally inputting the curve into a pre-trained neural network model to output pediatric response auxiliary evaluation information. The application realizes objective, continuous, quantitative and adaptive evaluation of the treatment response of the patient.

The invention provides a pulmonary nodule real-time positioning system based on patient respiratory fluctuation modeling. The system comprises a multi-source sensing unit, a signal processing module, a respiratory dynamics modeling module, a regionalization sensitivity field module, a disturbance observer, a self-healing registration module, a fusion filtering module and an output and interaction module. A dynamic model with a hysteretic operator is established through multi-source signals such as thoracico-abdominal movement, laryngeal acoustics and vibration, end tidal carbon dioxide, respiratory flow, body position drifting and instrument states, millimeter-level positioning of pulmonary nodules along with respiration is achieved in combination with regionalized sensitivity mapping, errors caused by respiratory movement, local disturbance and body position drifting are effectively solved, and the positioning accuracy of the pulmonary nodules is improved. And the safety and effectiveness of clinical operation are obviously improved.

1. The name of this design product: chest monitoring module housing. 2. Purpose of this design product: The shell of a module used for real-time monitoring of the patient's mandibular electromyography, electrocardiogram, respiratory airflow, chest and abdominal movement, body position, body movement, ambient light, and snoring during sleep. 3. The key point of the design of this product lies in its shape. 4. The picture or photo that best illustrates the design points: Stereoscopic drawing 1.

The present invention provides a sleep monitoring device and a respiratory event detection method. The sleep monitoring device includes a memory and a processor, the memory being configured to store program instructions, and the processor being configured to execute the program instructions to perform the respiratory event detection method. The method comprises: extracting the maximum amplitude value and the minimum amplitude value within a preset time period from a nasal airflow respiratory signal and a chest-abdominal movement respiratory signal to be processed, respectively, to obtain four amplitude values; determining whether the four amplitude values ​​meet a first preset condition; if the first preset condition is met, calculating the nasal airflow respiratory cycle amplitude and the chest-abdominal movement respiratory cycle amplitude within the preset time period to obtain a first cycle amplitude and a second cycle amplitude; determining whether the first cycle amplitude and the second cycle amplitude meet a second preset condition; and if the second preset condition is met, determining that the event is a respiratory effort-related micro-arousal event. The present invention improves the accuracy of respiratory event detection while also reducing detection costs.

The present application relates to the technical field of respiratory health monitoring, in particular to a respiratory health auxiliary monitoring method and system for chronic lung diseases, comprising: collecting a chest fluctuation image sequence, an environmental sound stream and a wearable chest and abdominal movement waveform to form multi-modal respiratory data, and obtaining a respiratory effort signal, a respiratory sound feature and a chest and abdominal breathing component through targeted processing. Relying on the respiratory physiological coupling relationship, a high-credibility comprehensive respiratory waveform is generated through an asynchronous adaptive fusion algorithm, multi-dimensional features such as respiratory rhythm, depth and symmetry are extracted, a pre-trained chronic disease state classification model is input, and a corresponding disease category and severity grade are output. The method can weaken the time sequence difference of multi-source signals, strengthen the expression of respiratory physiological features, improve the recognition accuracy of abnormal respiratory patterns, and realize continuous and refined respiratory state monitoring and disease grading evaluation for chronic lung diseases.

The application provides a neural network model training method, an oral-nasal airflow signal reconstruction method and equipment. The method is applied to the field of radar signal processing. The method comprises the following steps: acquiring multiple chest and abdominal movement signals collected by a millimeter wave radar on a target object; processing the multiple chest and abdominal movement signals by using a neural network model to obtain a reconstructed oral-nasal airflow signal and a prediction result of apnea; calculating a regression error according to the reconstructed oral-nasal airflow signal and a real oral-nasal airflow signal, and calculating an apnea prediction error according to the prediction result of apnea and an apnea label; performing weighted calculation according to the regression error and the apnea prediction error to obtain an integrated loss; and performing back propagation and gradient updating on the neural network model according to the integrated loss, and optimizing model parameters. The oral-nasal airflow signal reconstructed by the neural network model is more fine, comprehensive and accurate.

The invention provides a nursing information management method and system for sleep medicine, and relates to the technical field of sleep monitoring nursing, and the nursing information management method for sleep medicine comprises the following steps: synchronously collecting respiratory nursing data of a nursed person in a sleep process, the respiration nursing data comprises snore data, thoracico-abdominal movement data, limb movement data and exposed body surface temperature data; identifying apnea candidate events appearing in various types of data and recording the starting time of the apnea candidate events; the current state of the nursed person is judged according to the apnea candidate events appearing in the various data and the corresponding starting time of the apnea candidate events; and outputting a corresponding early warning signal according to a judgment result. According to the method, the comfort level of the nursed person can be guaranteed, the acceptance of the nursed person to nursing work is improved, the current state of the nursed person can be rapidly and accurately analyzed, and a corresponding early warning signal is output.

The present application discloses a hybrid continuous positive pressure and surface imaging system and method wherein the hybrid continuous positive pressure and surface imaging system comprises: a positive pressure unit adapted to apply a continuous positive pressure to a lung of a patient during radiotherapy; a surface camera system configured to capture body surface images of the patient continuously or at intervals; and a processing unit configured to model thoracic and / or abdominal movement of the patient in response to the applied continuous positive pressure continuously or at intervals based on the body surface image.