Sensor Algorithm

Exclusive Sensor Algorithm

s people pay more and more attention to their physical health, the sales of consumer electronics in the health field have been on the rise. To collect various vital sign data, the core is inseparable from professional sensor algorithms.

mmWave Radar Sensor Algorithm FMCW

From wearable devices to smart speakers, air purifiers and other smart home products, they have the ability to directly or contactlessly monitor various health data by carrying a variety of different types of sensors. The continuation of all this moves from sensors to algorithms.

In the field of smart health, we have cooperated with a series of partners to continuously optimize the algorithm, and have realized the mass production of its sensors in health management and monitoring functions.

According to data from the market research organization ABI, the market for the installation of Home Healthcare (health management in home scenarios) products may rise to more than 180 million units in the next 10 yearsMillimeter wave radar algorithm

mmWave Radar Sensor Algorithm

Millimeter-wave radar monitors subtle vital signs, senses distance and tracks gestures. Millimeter-wave radar can simulate the human eye and perceive the surrounding environment, especially for some motion detection, with higher sensitivity and reliability. Its radar sensor is combined with an AI algorithm to make a solution to collect micro-movements of the human body during sleep and the sound of snoring, so as to monitor the quality of sleep and determine whether there is sleep apnea.

Radar waves can penetrate any non-conductive surface and casing, so even if it is installed inside the product casing, its working effect will not be affected. At the same time, it is not affected by light, and can operate normally in dark, high temperature, foggy and dusty environments.

One of the difficulties of millimeter-wave radar is the algorithm. For some raw data information that can be extracted by the radar, the algorithm is different, and the MCU or processor required by the radar is also different.

Specifically, the radar sensor based on the millimeter-wave Doppler effect integrates a simple baseband and uses integrated antenna packaging technology, which directly and fundamentally solves the complexity of antenna design, radio frequency and radar signal processing. On this basis, combined with the complexity of the signal, after more than 10 years of continuous improvement of the algorithm, the monitoring of the product has reached the current excellent state.

Compared with the infrared solution, the millimeter wave is more sensitive and has a lower false alarm rate, which will greatly improve the user experience. At the same time, millimeter waves have the characteristics of penetration and non-conductivity. We adopt a multi-send and multi-receive architecture, which can extract information from different angles. Using our advanced algorithm development, it realizes functions such as gesture recognition, presence perception of indoor personnel, and heartbeat monitoring. It is suitable for scenarios such as smart buildings, smart homes, and elderly care.

It belongs to ultra-wideband, uses advanced FMCW algorithm, has very fine resolution, and can realize functions such as gesture control, vital sign monitoring, and personnel tracking.

It can sense any small movement, including the slight displacement of the chest when breathing, and thus monitor some vital signs. It can also use “presence sensing” to stimulate some device systems to perceive data information such as distance, position, speed, and angle. Combined with our advanced algorithms, it can realize tracking applications for breathing, heartbeat, and gestures.

Air Quality Monitoring Sensor Algorithm

Sensors related to the environment and air, mainly carbon dioxide sensors, TVOC, HCHO, etc., can be combined with analog and digital.

For example, the PPM raw reading output by the TVOC.CO2 sensor combined with our advanced algorithm can reflect the TVOC concentration value.

People tend to ignore the impact of TVOC/CO2 on our life and work. In crowded and poorly ventilated environments, CO2 concentrations can rise rapidly. For example, if a person is in a space of about 4 square meters, the CO2 concentration will rise from 500PPM to over 1000PPM within 45 minutes.

When the outdoor air is circulating, the PPM of CO2 is relatively low, but when the concentration of CO2 becomes higher and higher, such as exceeding 1000PPM, people will feel a little sleepy and have difficulty concentrating. Higher levels may even cause headaches or difficulty breathing. If the concentration reaches more than 2000PPM, people’s cognitive ability will be affected.

Therefore, it is necessary to add the function of measuring CO2 to monitor and control the air quality in a targeted manner, which will help improve people’s health, improve the comfort of life and improve work efficiency; at the same time, the ventilation system can be controlled and used according to needs, thereby saving energy and reducing the operating cost of the ventilation system.

A new CO2 sensor based on photoacoustic spectroscopy to provide a more universally applicable CO2 concentration measurement solution. There are four main technologies for CO2 sensors, in addition to photoacoustic spectroscopy, there are NDIR, EC and eCO2. Sensors based on photoacoustic spectroscopy and NDIR can realize the measurement of real CO2 concentration without being affected by other gases.

The advantage of the photoacoustic carbon dioxide sensor is that it can achieve the same performance as the high-end NIDR carbon dioxide sensor, meet the stringent air quality detection standards, and save more than 75% of the space.

Compared with NDIR sensors, photoacoustic spectral carbon dioxide sensors use high-sensitivity MEMS detectors, reducing the complexity of devices and designs. Therefore it can be made small and low cost, making it ideal for high volume industrial and consumer applications.

In smart health scenarios, advanced algorithms are combined with various types of sensors, which can be matched with MCU, Bluetooth, Wi-Fi, and data security chips. Combined with advanced algorithms, a health monitor with sensing and wireless functions can be realized, making smart health monitoring and management more accurate, effective, and easier.

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