Safety monitoring system based on multiple sensors

Abstract

The disclosure relates to a safety monitoring system based on multiple sensors. The system comprises a sensing equipment end, a local server, a cloud server and a client, wherein the sensing equipmentend is used for acquiring environment information of a target user and sending the environment information to the local server; the local server is used for inputting the environment information as atraining sample into an artificial neural network model to carry out self-learning training so as to update parameters of the artificial neural network model; the cloud server is used for sending theupdated parameters to the sensing equipment end, receiving a safety monitoring state of the target user, which is from the sensing equipment end and is returned according to the updated parameters, and pushing the safety monitoring state of the target user to the client; and the client is used for receiving the safety monitoring state of the target user. According to the disclosure, by adopting amulti-dimension sensing technology and a self-learning algorithm based on big data, living habits of the target user, such as an old person, are learned and understood so as to achieve the ability ofpredicting the potential risks.

Description

technical field [0001] The present disclosure relates to the field of artificial intelligence, and in particular to a multi-sensor-based safety monitoring system in the big data technology of the Internet of Things. Background technique [0002] The current technologies and products for monitoring the potential risks of the elderly at home have the following characteristics and defects: [0003] 1. The product function is single and the method is simple: for example, an infrared detector is placed in the elderly toilet to detect whether the elderly spend too long in the toilet. [0004] 2. Wearable devices can monitor certain physical quantities of the elderly, such as heart rate or falls. However, in terms of actual usage, the elderly are unwilling to use wearable devices 24 hours a day, which makes the penetration rate of wearable devices quite low so far. [0005] 3. The sensing technology and algorithm for perceiving the home scene are complex and immature, and even us...

AI Technical Summary

Problems solved by technology

However, in terms of actual usage, the elderly are unwilling to use wearable devices 24 hours a day, which makes the penetration rate of wearable devices quite low so far

[0005] 3. The sensing technology and algorithm for perceiving the scene in the home are complex and immature, and even using deep learning requires expensive hardware settings

Therefore, the user's use cost is high

[0006] 4. The perception dimension is single, which cannot fully reflect and predict the possible risks of the elderly at home

For example, it can only make some simple judgments on the behavior of daily activities within a limited range, and cannot monitor areas beyond the range, and the hardware settings are also very complicated and expensive, and the practicability is poor

[0007] 5. All existing technical solutions are based on sensing what has happened to the elderly, and cannot predict potential risks

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