CN113407583A - Method for emotion analysis and food recommendation based on AIot and TinyML technology - Google Patents
Method for emotion analysis and food recommendation based on AIot and TinyML technology Download PDFInfo
- Publication number
- CN113407583A CN113407583A CN202110702325.4A CN202110702325A CN113407583A CN 113407583 A CN113407583 A CN 113407583A CN 202110702325 A CN202110702325 A CN 202110702325A CN 113407583 A CN113407583 A CN 113407583A
- Authority
- CN
- China
- Prior art keywords
- model
- carrying
- different
- emotions
- tinyml
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2457—Query processing with adaptation to user needs
Abstract
The invention provides a method for emotion analysis and food recommendation based on AIot and TinyML technologies, which comprises the following steps of classifying according to image data of different emotions, and then marking foods capable of improving the emotion corresponding to different emotions; carrying out model training on image data sets with different emotions and the foods with improved emotions respectively corresponding to the image data sets with different emotions by using a deep learning framework to obtain a model file; carrying out TFLite format conversion on the obtained model, then carrying out binary format model curing on the TFLite format model, and converting the TFLite format model into a cpp binary model file and a h model header file; carrying out model deployment and reasoning according to MCU development boards of different end devices; and recommending proper food according to the expression of the user captured by the end equipment. According to the method, different types of foods can be recommended according to people with different moods, the weight of the model is lightened by using the TinyML technology, and finally the model is deployed on the terminal equipment with limited computing power for reasoning, so that the use cost is reduced.
Description
Technical Field
The invention relates to a method for emotion analysis and food recommendation based on AIot and TinyML technologies, and belongs to the technical field of Internet of things.
Background
Artificial Intelligence (AI) and internet of things (IoT) are popular areas in computer science. AIoT merges AI and IoT together, applying AI to IoT. The internet of things is formed when programming "things" and connecting them to a network. However, AIoT can be implemented when these systems of internet of things are capable of analyzing data without human intervention and have decision-making potential. The artificial intelligence provides power for the Internet of things through decision making and machine learning, and the Internet of things provides power for the artificial intelligence through data exchange and connectivity. Then, the artificial intelligence model with large weight is subjected to model lightweight through a TinyML (lightweight machine learning) technology so as to be deployed on a development board with limited computing power, and by means of the brain of AI, the body of IoT and the lightweight of TinyML, the systems can improve efficiency, performance and universality.
At present, healthy life is more and more concerned, mental health is the basis of healthy life, people's mood can be adjusted to food, reasonable diet can make people keep mental health, and a method capable of intelligently judging people's emotional condition and recommending and improving mood food is lacked at present.
Disclosure of Invention
The invention aims to provide a method for emotion analysis and food recommendation based on AIot and TinyML technologies, which can analyze the emotional state of a person through image processing and recommend foods for improving emotion.
In order to achieve the purpose, the invention is realized by the following technical scheme:
a method for emotion analysis and food recommendation based on AIot and TinyML technology, comprising the steps of,
step 1: classifying according to the image data of different emotions, and then marking foods capable of improving the emotion corresponding to the different emotions;
step 2: carrying out model training on image data sets with different emotions and the foods with improved emotions respectively corresponding to the image data sets with different emotions by using a deep learning framework to obtain a model file;
and step 3: carrying out TFLite format conversion on the obtained model, then carrying out binary format model curing on the TFLite format model, and converting the TFLite format model into a cpp binary model file and a h model header file;
and 4, step 4: carrying out model deployment and reasoning according to MCU development boards of different end devices;
and 5: and recommending proper food according to the expression of the user captured by the end equipment.
Preferably, the specific steps of the model training in step 2 are as follows: data sets of different emotions are collected and then trained by a convolutional neural network.
Preferably, the specific steps of the model training in step 2 are as follows: data sets of different emotions are collected and then trained through a classification model, wherein the classification model comprises a perceptron, a regression and a support vector machine.
The invention has the advantages that: different types of foods can be recommended according to people with different moods, model lightweight is carried out by using a TinyML technology, and finally the model is deployed on terminal equipment with limited computing power for reasoning, so that the use cost is reduced.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention.
Fig. 1 is a schematic flow chart of a food recommendation application based on emotion analysis according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention provides a method for emotion analysis and food recommendation based on AIot (artificial intelligence Internet of things) and TinyML (lightweight machine learning) technologies. Wherein the sensors capture data and then the inference of the model is entirely on the microcontroller device side, even if it is not possible to connect to the network, the device can still capture images and analyze and infer emotions.
Comprises the following steps of (a) carrying out,
step 1: the image data of different emotions are classified, for example, happiness, anger, sadness, and happiness. And then foods corresponding to different emotional markers can improve mood.
Step 2: the image data sets of different emotions and the corresponding emotion-improving foods respectively are subjected to model training by using a deep learning framework (Tensorflow). Data sets of different emotions are collected and then trained by classification models, such as perceptrons, regression, support vector machines, etc., or by convolutional neural networks. Finally, a model file is obtained.
And step 3: and carrying out TFLite format conversion on the obtained model, then carrying out binary format model solidification on the TFLite format model, and converting the TFLite format model into a cpp binary model file and an h model header file.
And 4, step 4: model deployment and reasoning are carried out according to MCU development boards of different end devices
And 5: and recommending proper food according to the expression of the user captured by the end equipment.
Claims (3)
1. A method for emotion analysis and food recommendation based on AIot and TinyML techniques, comprising the steps of,
step 1: classifying according to the image data of different emotions, and then marking foods capable of improving the emotion corresponding to the different emotions;
step 2: carrying out model training on image data sets with different emotions and the foods with improved emotions respectively corresponding to the image data sets with different emotions by using a deep learning framework to obtain a model file;
and step 3: carrying out TFLite format conversion on the obtained model, then carrying out binary format model curing on the TFLite format model, and converting the TFLite format model into a cpp binary model file and a h model header file;
and 4, step 4: carrying out model deployment and reasoning according to MCU development boards of different end devices;
and 5: and recommending proper food according to the expression of the user captured by the end equipment.
2. The method for emotion analysis and food recommendation based on AIot and TinyML technology as claimed in claim 1, wherein the specific steps of model training in step 2 are as follows: data sets of different emotions are collected and then trained by a convolutional neural network.
3. The method for emotion analysis and food recommendation based on AIot and TinyML technology as claimed in claim 1, wherein the specific steps of model training in step 2 are as follows: data sets of different emotions are collected and then trained through a classification model, wherein the classification model comprises a perceptron, a regression and a support vector machine.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110702325.4A CN113407583A (en) | 2021-06-24 | 2021-06-24 | Method for emotion analysis and food recommendation based on AIot and TinyML technology |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110702325.4A CN113407583A (en) | 2021-06-24 | 2021-06-24 | Method for emotion analysis and food recommendation based on AIot and TinyML technology |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN113407583A true CN113407583A (en) | 2021-09-17 |
Family
ID=77682927
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110702325.4A Pending CN113407583A (en) | 2021-06-24 | 2021-06-24 | Method for emotion analysis and food recommendation based on AIot and TinyML technology |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113407583A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114528966A (en) * | 2022-01-27 | 2022-05-24 | 山东浪潮科学研究院有限公司 | Local learning method, equipment and medium |
| CN114879673A (en) * | 2022-05-11 | 2022-08-09 | 山东浪潮科学研究院有限公司 | Visual navigation device based on TinyML technology |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110298212A (en) * | 2018-03-21 | 2019-10-01 | 腾讯科技（深圳）有限公司 | Model training method, Emotion identification method, expression display methods and relevant device |
| CN110334658A (en) * | 2019-07-08 | 2019-10-15 | 腾讯科技（深圳）有限公司 | Information recommendation method, device, equipment and storage medium |
| KR102024722B1 (en) * | 2018-10-31 | 2019-11-04 | 이승보 | System and Method for Marketing Online To Offline based on Artificial Intelligence Recommendation |
| CN111222044A (en) * | 2019-12-31 | 2020-06-02 | 深圳Tcl数字技术有限公司 | Information recommendation method and device based on emotion perception and storage medium |
| CN112364744A (en) * | 2020-11-03 | 2021-02-12 | 珠海市卓轩科技有限公司 | TensorRT-based accelerated deep learning image recognition method, device and medium |
-
2021
- 2021-06-24 CN CN202110702325.4A patent/CN113407583A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110298212A (en) * | 2018-03-21 | 2019-10-01 | 腾讯科技（深圳）有限公司 | Model training method, Emotion identification method, expression display methods and relevant device |
| KR102024722B1 (en) * | 2018-10-31 | 2019-11-04 | 이승보 | System and Method for Marketing Online To Offline based on Artificial Intelligence Recommendation |
| CN110334658A (en) * | 2019-07-08 | 2019-10-15 | 腾讯科技（深圳）有限公司 | Information recommendation method, device, equipment and storage medium |
| CN111222044A (en) * | 2019-12-31 | 2020-06-02 | 深圳Tcl数字技术有限公司 | Information recommendation method and device based on emotion perception and storage medium |
| CN112364744A (en) * | 2020-11-03 | 2021-02-12 | 珠海市卓轩科技有限公司 | TensorRT-based accelerated deep learning image recognition method, device and medium |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114528966A (en) * | 2022-01-27 | 2022-05-24 | 山东浪潮科学研究院有限公司 | Local learning method, equipment and medium |
| CN114528966B (en) * | 2022-01-27 | 2023-09-26 | 山东浪潮科学研究院有限公司 | Local learning method, equipment and medium |
| CN114879673A (en) * | 2022-05-11 | 2022-08-09 | 山东浪潮科学研究院有限公司 | Visual navigation device based on TinyML technology |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US10922866B2 (en) | Multi-dimensional puppet with photorealistic movement | |
| Muhammad et al. | Emotion recognition for cognitive edge computing using deep learning | |
| CN113407583A (en) | Method for emotion analysis and food recommendation based on AIot and TinyML technology | |
| CN110059593B (en) | Facial expression recognition method based on feedback convolutional neural network | |
| Liu et al. | Benchmarking spike-based visual recognition: a dataset and evaluation | |
| CN104346503A (en) | Human face image based emotional health monitoring method and mobile phone | |
| US20200027442A1 (en) | Processing sensor data | |
| CN110717423B (en) | Training method and device for emotion recognition model of facial expression of old people | |
| Gao et al. | Graph reasoning-based emotion recognition network | |
| KR20210051812A (en) | Method and apparatus for analysing signal | |
| CN110633689B (en) | Face recognition model based on semi-supervised attention network | |
| Rajasekar et al. | A joint cross-attention model for audio-visual fusion in dimensional emotion recognition | |
| Guo et al. | Applying TS-DBN model into sports behavior recognition with deep learning approach | |
| CN114943324A (en) | Neural network training method, human motion recognition method and device, and storage medium | |
| Liu et al. | Ultralow power always-on intelligent and connected snn-based system for multimedia iot-enabled applications | |
| Shi et al. | Sensor‐based activity recognition independent of device placement and orientation | |
| CN117290730A (en) | Optimization method of individual emotion recognition model | |
| CN113159002A (en) | Facial expression recognition method based on self-attention weight auxiliary module | |
| Rawf et al. | Effective Kurdish sign language detection and classification using convolutional neural networks | |
| CN109935320A (en) | A kind of common skin diseases network assistance diagnostic system | |
| Nakisa | Emotion classification using advanced machine learning techniques applied to wearable physiological signals data | |
| CN110275455B (en) | Control method based on electroencephalogram signals, central control equipment, cloud server and system | |
| Zhang et al. | ECMER: Edge-Cloud Collaborative Personalized Multimodal Emotion Recognition Framework in the Internet of Vehicles | |
| CN110852270A (en) | Mixed grammar human body analysis method and device based on deep learning | |
| Dutta et al. | Data augmentation for ambulatory EEG based cognitive state taxonomy system with RNN-LSTM |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20210917 |