17 results about "Eating behavior" patented technology

This application discloses a computer-implemented method for determining changes in pet eating behavior using historical pet eating data. The method includes receiving multiple historical pet eating data records from a database, determining a subset of the multiple historical pet eating data records, determining a expected distribution based on the subset of the multiple historical pet eating data records, wherein the expected distribution includes a baseline, an upper threshold, and a lower threshold, receiving current pet data from pet sensors, wherein the current pet data includes a total eating event value, analyzing whether the total eating event value exceeds the upper threshold or the lower threshold, and outputting a notification indicating the results of the analysis.

The invention discloses an intelligent weighing feeding bowl and an intelligent control method thereof, and relates to the field of medical nursing, and the feeding bowl comprises a bowl body and an intelligent base; the intelligent base comprises a suction cup structure, a weighing module, a main control unit, an alarm prompt unit, a communication module and a power management module. The suction cup structure is used for fixing the bowl body on a smooth table top; the weighing module is used for detecting weight data of food in the bowl in real time; the main control unit is used for processing the weight data and generating an alarm prompt instruction; the alarm prompting unit comprises an annular multi-color LED indicating lamp and a buzzer, and is used for giving out a sound-light alarm based on the alarm indicating instruction when the food is excessive or the food is eaten too fast; the communication module is used for carrying out data interaction with a mobile terminal. Digitalized recording and visual analysis of feeding behaviors are realized, and an accurate basis is provided for a doctor to evaluate the recovery progress of the swallowing function and adjust a training plan.

The present application relates to the technical field of intelligent management, in particular to a pig intelligent precise feeding system and method based on internet of things management and control and feeding reciprocity. It comprises: a multi-modal data acquisition unit for collecting environmental parameters, individual characteristics, behavior videos and feeding amount; a feeding reciprocity analysis unit for real-time processing and analysis of feeding behavior videos, combining the data obtained from the feedback of the weighing type trough to obtain the real-time data of pigs, and using the real-time data of pigs to establish a feeding efficiency evaluation model of individual pigs; a feeding parameter generation unit for obtaining the optimal feeding cycle and feeding amount according to the feeding efficiency index of each pig. The system and method can more accurately evaluate the actual eating state of each pig, not only improving the accuracy of eating behavior analysis, avoiding misjudgment caused by ignoring individual differences and environmental factors, but also effectively dealing with the problem of high-frequency noise interference caused by head posture changes, improving the accuracy of eating behavior analysis.

The application relates to the technical field of diet monitoring, in particular to a chewing action recognition method, a diet behavior monitoring earphone and a storage medium. The chewing action recognition method comprises the following steps: receiving a target sensing signal from a diet behavior monitoring earphone in a target terminal; wherein, the target behavior feature is obtained through the following steps in the diet behavior monitoring earphone: performing vibration sensing on a target object to obtain sensing data flow, screening out a noise frequency band from the sensing data flow to obtain the target sensing signal; performing feature extraction on the target sensing signal to obtain chewing rhythm features, chewing strength features and chewing time value features of the target object; and performing chewing behavior analysis on the chewing rhythm features, the chewing strength features and the chewing time value features to determine a chewing analysis result of the target object. In this way, the chewing action of the monitoring object can be accurately recognized.

This application discloses a method, device, and system for intervening in eating behavior based on an ear-worn device. The method involves: acquiring vibration signals collected by the ear-worn device during a user's eating process in real time; determining a set of candidate chewing segments based on the vibration signals; identifying the event type of each candidate chewing segment in the set of candidate chewing segments, and constructing a chewing event sequence based on the identified multiple chewing events; determining the corresponding swallowing event and its time boundary point based on the time interval between two adjacent chewing events in the chewing event sequence; determining an eating rhythm index based on the swallowing event and its time boundary point; and triggering the ear-worn device to play voice prompts according to a preset intervention strategy based on the eating rhythm index. This method enables the ear-worn device to sense the user's eating rhythm during daily meals and provide timely reminders at appropriate times, helping the user consciously slow down their eating speed.

The invention provides a postoperative patient compliance monitoring method and device based on biological impedance analysis, and relates to the technical field of medical monitoring, the method comprises the following steps: measuring patient physiological data of postoperative body composition parameters of a patient in real time through a physiological monitoring device integrated with a biological impedance analysis module worn by the patient; exercise monitoring equipment collects exercise amount data of a patient in real time, the collected physiological data and exercise data of the patient are uploaded to a cloud platform through a wireless communication module to analyze multi-dimensional data of the patient, and the postoperative rehabilitation progress of the patient is evaluated; and performing compliance evaluation on movement, diet, nutrition intake and eating behaviors of the patient based on a real-time analysis result, feeding back compliance conditions and improvement suggestions in real time through a patient terminal device, performing personalized intervention and guidance when the compliance of the patient is insufficient, and adjusting a rehabilitation scheme. According to the method, the patient can be helped to better manage postoperative recovery, and personalized diet, exercise and rehabilitation guidance is provided.

The present disclosure relates generally to keeping animals (10) and in particular to a method for investigating eating behavior of animals (10) in a livestock area (30). The present disclosure also relates to a control device (100) and to a computer program for performing the method. According to a first aspect, the present disclosure relates to a method for investigating eating behavior of animals (10) in a livestock area (30). The method comprises monitoring animal data for a period of time after feed has been dispensed at a feeding station (31) in the livestock area (30), the animal data being collected using tags carried by the animals (10) and being indicative of activity of the animals (10) and / or position of the animals (10) relative to the feeding station (31). The method further comprises performing an action when the monitored animal data fails to meet one or more eating criteria, the one or more eating criteria defining normal eating behavior of the animals (10) in terms of activity of the animals (10) and / or position of the animals (10) relative to the feeding station (31). The present disclosure also relates to a control device (100) and to a computer program for performing the method.

The embodiment of the application discloses a kind of healthy diet management method, system, electronic equipment and storage medium, it is related to health management technical field, comprising: obtaining the diet scene time sequence image frame under diet scene, and the diet behavior time sequence image frame corresponding to target object under diet scene;Diet scene time sequence image frame is obtained according to the image of pre-configured image acquisition equipment and is collected diet area, and diet area is the area where target object is located, and diet behavior time sequence image frame is obtained according to the image of pre-configured image acquisition equipment and is collected target object;In response to feature extraction is carried out to diet behavior time sequence image frame, obtain diet behavior time sequence data, according to diet behavior time sequence data, determine the effective eating behavior data of target object under diet scene;According to diet scene time sequence image frame, determine the nutrition characteristic data of diet under diet scene;According to effective eating behavior data and nutrition characteristic data, generate the diet suggestion scheme corresponding to target object.

The invention discloses a livestock health data intelligent management method and system, and relates to the technical field of image recognition and edge calculation. The livestock health data intelligent management method comprises the following steps: S1, acquiring an individual behavior image sequence in a free feeding environment, constructing a behavior image basic data set, and preprocessing the behavior image basic data set; s2, identifying visible states of a head and a slot position based on the behavior image basic data set, extracting a corresponding shielding section and evaluating a shielding coverage degree; s3, identifying continuity characteristics of behaviors before and after the shielding section, and dividing continuation levels based on head posture and position change; and S4, calling an ingestion behavior correction sequence generated after behavior connection as a judgment basis, and judging the interference intensity level of the interpolation section on the ingestion behavior structure. The problem that the feeding time is severely underestimated due to interruption of a feeding behavior section caused by the fact that a head-slot position contact structure in an image in a sheltered scene cannot be continuously recognized is solved.

The invention discloses a cognitive state evaluation and regulation training integrated system and method for weight loss, and relates to the technical field of brain function regulation, the system monitors the attenuation degree of multiple beams of near-infrared light with the wavelength of 760 nm and 850 nm after the near-infrared light is emitted from the brain so as to calculate the concentration change value of oxyhemoglobin in the frontal parietal lobe area of the brain, and the concentration change value of the oxyhemoglobin in the frontal parietal lobe area of the brain is calculated. And inputting the oxyhemoglobin concentration change value into a long-short-term memory neural network fused with an attention mechanism, outputting a food desire state of an overweight / obesity user when the overweight / obesity user faces food picture stimulation, and further taking the right dorsal-lateral prefrontal lobe as a regulation and control target, and obtaining the food desire state of the overweight / obesity user through an exogenous or endogenous nerve regulation and control mode. The activity level of the brain region is enhanced, and the food desire state of the overweight / obese user is reduced. The diet control ability of the overweight user can be remarkably enhanced through multiple regulation and control training, the food desire is reduced, and then the effects of improving the ingestion behavior and reducing weight are achieved.

The present application relates to the technical field of food / functional food or health food, in particular to a composition and its preparation method and application.The composition comprises the following components: coffee raw materials, herbal extract; the preparation raw materials of the herbal extract comprise green tea, bitter gourd, licorice, cassia seed and lotus leaf; the preparation method of the herbal extract comprises the following steps: (1) mixing the preparation raw materials according to the proportion, extracting by water to obtain an extract; (2) the extract is subjected to enzymatic hydrolysis treatment by cellulase, pectinase, cellobiase and tannase, and solid-liquid separation, and the liquid phase is the herbal extract.The composition of the present application can effectively inhibit fat absorption, has good weight loss effect, and can reduce body weight, BMI index, waist circumference, hip circumference, abdominal fat thickness and adverse eating behavior.

Disclosed is a method for managing animals by surveying eating behavior of the animals in a livestock area, including monitoring the animals over a time period subsequent to distributing feed at a feed table in the livestock area, identifying animal behavior that fails to meet a criteria, and performing an such as regrouping the animals or conducting a health check on the animals.

Methods, devices and systems for treating dysregulated eating behavior in a subject, in a subject with an impaired orexigenic appetitive processing circuit mediating activity between a lateral hypothalamus (LH) and a dorsolateral hippocampus (dlHPC), including invasively and / or non-invasively delivering neurostimulation to at least a portion of the impaired subject to at least partially repair the impaired circuit.

The invention relates to the technical field of diet monitoring, in particular to a chewing action recognition method, a diet behavior monitoring earphone and a storage medium. According to the chewing action recognition method, a target sensing signal needs to be received from a dietary behavior monitoring earphone in a target terminal; wherein the target behavior characteristics are obtained in the dietary behavior monitoring earphone through the following steps: performing vibration sensing on a target object to obtain a sensing data stream, and performing noise frequency band screening on the sensing data stream to obtain a target sensing signal; performing feature extraction on the target sensing signal to obtain a chewing rhythm feature, a chewing force feature and a chewing time value feature of the target object; and performing chewing behavior analysis on the chewing rhythm feature, the chewing force feature and the chewing time value feature to determine a chewing analysis result of the target object. In this way, the chewing action of the monitored object can be accurately recognized.

The application relates to the technical field of animal experiment devices, in particular to an experimental device for limiting feces eating behavior of rodents, which comprises a box body and a blocking ring arranged on one side of the box body, the blocking ring is detachably connected to the box body, the lower side of the box body is provided with a collection cavity, a plastic net with holes is arranged in the box body and located on the upper side of the collection cavity, a pull-out frame body is slidably arranged in the box body and located on the lower side of the collection cavity, and the pull-out frame body is used for receiving animal excrement.

The invention discloses a non-contact eating behavior identification method based on a depth time sequence coding and decoding network, and belongs to the field of wireless sensing and behavior monitoring. The method comprises a space-time heat map construction module based on millimeter wave signal processing, a space-time feature extraction module based on a sequence-to-sequence codec architecture and a feeding behavior recognition module based on a cross attention connection mechanism. The method comprises the following steps: preprocessing an original frequency-modulated continuous wave signal acquired by a millimeter-wave radar, and constructing a three-dimensional space-time heat map; capturing the spatial features of the heat map by using a spatial feature extractor, and integrating multi-scale time sequence context information while maintaining the time resolution by expanding a time sequence convolutional network; multi-scale features output by an encoder are combined with features of a decoder, context features are dynamically aggregated, and fine-grained recognition of feeding behaviors is achieved. The method can effectively improve the behavior recognition precision and generalization ability in a continuous real eating scene, and provides reliable technical support for healthy diet monitoring.