Feeding behavior analysis and detection method based on chewing and swallowing acts and cardiac electrical activity

Abstract

The invention discloses a feeding behavior analysis and detection method based on chewing and swallowing acts and cardiac electrical activity. By adopting two wearable myoelectric activity sensors, a sound sensor and two electrocardio sensors, one myoelectric activity sensor is arranged on the surface of skin of the masticatory muscle in from of the ear of the head, the other myoelectric activity sensor is arranged at the surface of the skin of a thyro-mylohyoid-muscle part of the neck, the myoelectric activity sensors are connected to an earphone type connecting structure, the sound sensor and one electrocardio sensor are arranged at the suprasternal fossa part of the neck, the other electrocardio sensor is arranged at the rear side of the neck, and the sound sensor and the electrocardio sensors are all connected to the necklace type connecting structure; according to the detected myoelectric signals combined with detected sound signals and electrocardio signals, the feeding behavior mode is analyzed, recognized and recorded, the feeding food type, the feeding behavior and the recorded feeding interval and time can be analyzed and recognized, the detection result is sent to a mobile phone of a user, the result is displayed through the mobile phone of the user, and suggestions are formed.

Description

technical field [0001] The invention relates to a method for analyzing and detecting feeding behavior. The present invention also relates to a feeding behavior monitoring system using the above feeding behavior analysis and detection method. Background technique [0002] Human feeding is a complex behavioral process, and multiple systems are involved in this process, including the central nervous system, autonomic nervous system, skeletal muscle (chewing and swallowing) system, endocrine system, digestive system and so on. Among them, the autonomic nervous system can be detected by electrocardiographic activity; chewing and swallowing can be detected and recorded by recording the myoelectric activity of related muscles and / or swallowing sounds. By comparing and analyzing the above detection and recording, it can be more objective to reflect different individuals The characteristics and habits of eating behaviors, such as the interval between meals, the type of food preferen...

AI Technical Summary

Problems solved by technology

The data obtained simply recorded the action of eating, and failed to link the emotional activity, autonomic nervous activity, and endocrine and exocrine activities (the above activities are closely related to electrocardiographic activity), and could not fully reflect people's eating behavior. It is impossible to make accurate judgments on eating habits, etc., and it is also impossible to give reasonable dietary advice. For example, if we observe that a person has a rapid heart rate before eating, and the amount of food eaten afterward will increase significantly, this suggests that the possible cause is meal. Pre-hypoglycemia or emotional agitation will cause the person to eat too much, and we can provide targeted intervention and management based on this. If there is no ECG activity record, we will not be able to obtain a complete picture of the patient's eating behavior

[0004] Another technical disadvantage of the existing technology is that the chewing and swallowing actions during the feeding process are very close to some other non-ingesting actions of the human body. Therefore, when the above-mentioned monitoring method is used to monitor the feeding behavior, it is often impossible to rule out the non-ingesting actions during the monitoring process. impact, resulting in unreliable monitoring results

[0005] At the same time, the existing monitoring methods are often unable to distinguish the type of food ingested, for example: whether the food is dry solid, juicy solid or liquid food, semi-liquid food, etc.;

[0006] Most of the feeding monitoring systems used in the prior art cannot be worn around, and cannot achieve long-term continuous monitoring, which affects the monitoring effect

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