Text | Shen Xiao
Editor | Wang Yutong
China is accelerating into an aging society. According to the data of the Ministry of Civil Affairs, by the end of 2021, the proportion of elderly people aged 60 and over in China was 18.9% of the total population, and it is estimated that by 2025 and 2035, this number will exceed 20% and 30% respectively. With the acceleration of demographic changes, the urgency of providing for the aged is prominent.
However, China’s old-age service market is still facing the gap between supply and demand of caregivers and places of care. In this context, the state and local governments have successively issued a series of policies to guide the development of the old-age care industry, such as 9073 and 9064, encouraging home-based care (90%), supplemented by community day care (7%/6%) and institutional care as a service supplement (3%/4%). However, due to the gradual reduction in the size of domestic family units in recent years, it is difficult to effectively guarantee the manpower needed for home-based care for the elderly. Therefore, it is still necessary to seek other ways to solve the gap between supply and demand of old-age care.
This provides an opportunity for the popularization and application of scientific and technological products in the pension industry. The smart pension industry has emerged, and enterprises have emerged to develop smart devices for different pension application scenarios and provide health monitoring, data analysis and other services based on technologies such as AI and big data.
In the scene where the elderly need care, falling prevention is considered to be a key link. According to the data released by China Disease Surveillance System, falling is the leading cause of injury-related death among the elderly over 65 years old in China.
In order to prevent the elderly from falling, Shanghai Vision Blue City Information Technology Co., Ltd. (hereinafter referred to as Vision Blue City) launched a series of 3D intelligent human posture sensor "Little Blue Lion" based on ToF radar and AI algorithm, and built a corresponding AIoT platform to provide intelligent care services such as posture warning, health data management and analysis.
Through ToF(Time of Flight) sensor, the electronic equipment associated with it can collect the depth data of environment, objects and human body in the scene, and support real-time imaging. With the help of the algorithm, these data can be used to measure the distance, size and track the movement. The AI algorithm expert model and deep learning network based on ToF depth data can realize accurate recognition of human postures such as falling, sitting, lying and walking. At present, ToF technology has been widely used in face recognition and gesture judgment of smart phones. Other application scenarios include: obstacle recognition of sweeping robot; 3D photography and 3D modeling of intelligent hARdware devices such as ar glasses; Unmanned obstacle identification and ranging.
In addition to the "Little Blue Lion" series products, Trendy Blue City has also developed a smart and healthy SaaS platform to provide integrated intelligent care solutions for residential communities, pension real estate, government and private pension institutions, and home-based pension scenarios. The platform covers daily entertainment life management, health management, home care management, etc., and can integrate health data and service early warning information fed back from AIoT platform.
Vision Blue City was established in 2022 with the joint investment of Shanghai Vision Information Technology Co., Ltd. (hereinafter referred to as Vision Technology) and Zhejiang Blue City Chunfeng Construction Management Co., Ltd. (hereinafter referred to as Blue City Chunfeng).
Vision Technology focuses on AI visual behavior analysis technology, and related products and solutions have been applied in retail, security, smart communities and other fields. Blue City Chunfeng belongs to Blue City Group, a comprehensive real estate service provider. Blue City Group has more than 20 years of experience in real estate development and operation management, with blue city agriculture, blue city support and blue city health as its core service industries.
In terms of products, Shan Yiyong, CEO of Trendy Blue City, told 36Kr: "At present, millimeter-wave radar is the main anti-fall technology product used in the smart old-age scene on the market. Compared with RGB cameras, millimeter-wave radar can identify height changes and protect the privacy of the elderly. However, the accuracy of millimeter-wave radar identification is poor, and the probability of attitude false alarm is high. ToF radar can significantly improve the accuracy of attitude reporting while protecting the privacy of the elderly. "
In addition, Vision Blue City will launch the whole industrial chain products based on the combination of body recognition and health care, aiming at systematically collecting human micro-expressions and all kinds of movement data, and using AI technology to realize comprehensive health assessment, rehabilitation and sports guidance, service demand reporting and other functions.
On the one hand, the accuracy and privacy of "Little Blue Lion" series products are double guaranteed because ToF scheme itself has certain technical advantages compared with other technical routes such as millimeter wave radar and laser radar. At the same time, in recent years, the hardware module of ToF sensor has made some progress in technology. On the other hand, it benefits from the technology accumulation of vision recognition AI algorithm.
First of all, the technical advantage lies in the higher privacy protection of imaging, but the collected posture data is clearer and richer. On this basis, supplemented by three-dimensional spatial data,Vision Blue City can use AI technology to make a more accurate judgment and analysis of the user’s posture and behavior information.. Shan Yiyong, CEO of Vision Blue City, said: "The low accuracy of millimeter-wave radar is mainly due to the low amount of data available at present, which can only be used for mechanical judgment and it is difficult to use AI technology. And each of our ToF devices has an AI chip, which can be directly judged. "
Compared with the imaging of other radar sensors
Blue ToF-3D sensor imaging
At present, the ToF sensor applied in Blue City has a maximum distance of 8 meters and a resolution of 640*480. Shan Yiyong, CEO of Vision Blue City, pointed out: "It is very important to improve the collection distance and resolution. In 2021, the ToF sensor module can only support the acquisition distance of 6 meters. By 2022, this number has increased to 8-10 meters. In addition, at the resolution level, it has reached a level similar to VGA, which is enough to clearly distinguish human posture and behavior. "
Secondly, the improvement of the accuracy of body recognition needs the support of corresponding AI algorithm and model, and the model is continuously optimized through a large amount of data. In the early stage of vision technology, a large amount of data has been accumulated for deep learning, and it has a mature model.
Looking forward to Blue City can directly call its ready-made model and quickly write the device algorithm..
At present, the technical team led by Dr. Zhong Zhang, the chief scientist, has solved two ToF technical difficulties.
First, through data modulation, the remote depth data drift can be controlled. According to reports, in the traditional sense, after the monitoring distance reaches 8 meters, the drift distortion of depth data will be more serious;
Secondly, based on deep learning algorithm and expert model, the accuracy of posture recognition is improved. Shan Yiyong, CEO of Vision Blue City, told 36Kr: "There are scenes where deep learning can’t achieve accurate recognition, such as scenes where human feature data is completely or partially lost due to the covering or blocking of obstructions. For example, the elderly may fall behind the coffee table, and ToF equipment can’t capture the portrait behind the coffee table. At this time, the traditional deep learning system will have false positives, but we can infer through the memory and analysis ability of the expert system. "
In the view of blue city, this expert system is also the core technical barrier of the company at present, which can help it build a certain first-Mover advantage.. Shan Yiyong, CEO of Blue City, pointed out: "There are still few people doing things with this technology, and some people do it, but the landing is slow."
On the market side, relying on the resources of Blue City Group and its affiliated enterprises in the real estate industry, it is tending to see that Blue City has signed some old-age real estate projects, such as Hangzhou Blue City Taoranli, etc., with CCRC community and high-end old-age care institutions as the main formats. It is estimated that there will be more than 10 landing projects this year, and mass production will be realized in May, and about 10,000 products will be delivered to customers.
But at the same time, by looking at the technical advantages and resource advantages that Blue City has at this stage, it may face two corresponding challenges at the market level.
First, the product price is high, while the market’s willingness to pay and purchasing power are still low.
From the cost side, Shan Yiyong, CEO of Vision Blue City, said that every ToF device is equipped with AI chips, which leads to the fact that the price of ToF devices cannot be directly reduced to the same cost level as ordinary radar sensors. From the demand side, market education and fee payment are considered as two obstacles for the domestic pension market to move from just-needed pension service to refined management mode of CCRC.
At present, the equipment of Blue City is priced at 2000 yuan. Usually, one piece of equipment needs to be placed in the toilet, bedroom and living room respectively, that is, a single household needs to pay at least 6000 yuan for the equipment. Shan Yiyong, CEO of Vision Blue City, said frankly that this is one of the main reasons why Vision Blue City should first cut into the market from CCRC community and high-end pension institutions. But at the same time, he also said that with the increase of output in the future, the price of external mining modules will decrease; In addition, through the back-end "module+self-developed motherboard" form, the company is expected to achieve a continuous decline in costs, and the price of a device in the future can be reduced to around 1000 yuan.
Second, it remains to be verified whether the existing industry resources can continue to play an advantage in the process of product penetration to B-end customers, G-end customers and C-end customers except affiliated enterprises.
Generally speaking, the business model of Blue City at this stage is mainly divided into two categories: BtoBtoC and BtoGtoC, and the goal of the next stage is to directly face C-end users.
Under BtoBtoC mode, B-end customers such as CCRC community and pension real estate may pay more attention to the actual economic value that the equipment can create for the project, whether the new project adopts ToF scheme or not, and whether the completed project is replaced by ToF scheme. This, in turn, depends to some extent on whether the value of the equipment can be recognized by C-end users. Specifically, the first is the weight of ToF scheme in the decision-making process of users choosing pension institutions; The second is whether users are willing to pay a premium because of the adoption of ToF scheme.
In addition, the body recognition products of Blue City only solve a small part of the needs of the elderly-real-time care and immediate feedback. In the provision of follow-up services, professional institutions and service personnel are needed to fill in. Therefore, whether the adoption of equipment can actually reduce the demand of nursing staff, improve the efficiency of care, improve the reuse rate of personnel and reduce the cost of care may also constitute a part of the core considerations.
Under the BtoGtoC model, the government pays more attention to the creation of social value. Although Vision Blue City proposed a subscription payment model to solve the problem of high equipment cost, as mentioned above,Whether you can achieve synergy with back-end services may be the key.. This is equally important for the C-end users themselves.
In this way, doing a good job in extending the form of products and services and establishing a reliable partnership with upstream and downstream enterprises in the industrial chain may be the future concern of Blue City.
In this regard, Trendy Blue City said that it would explore the form of "property+"and reach a cooperation with the property company. Trendy Blue City is responsible for the provision and basic operation of smart devices, and the property is responsible for the service output after posture warning. In this mode, residents in the community can also pay in the form of equipment and service subscriptions. However, this cooperation model may face some difficulties in actual implementation, such as the delineation of related responsibilities and the ability of the property to provide related services.
At present, the team is mainly composed of R&D personnel, and a marketing team is being established.
Dr. Zhong Zhang, the founder and chief scientist, graduated from Tsinghua University and worked as the chief scientist of OBJECTVIDEO, the world’s first visual behavior analysis company, for nearly 10 years, leading the team to develop an industry-leading expert algorithm model. At present, based on the deep learning model, Dr. Zhong Zhang has built a mature technical path of "expert model system+deep learning system".
Founder and CEO Shan Yiyong has worked in Kangyang Real Estate Head Enterprise for nearly 20 years. He has participated in the planning, construction and operation of many industry benchmarks Kangyang Real Estate projects, and has also participated in the establishment of health management companies and launched O2O community health management solutions based on IoT.
Dr. Mei Jian, founder and CFO, has more than 20 years of experience in the financial industry and 3 years of entrepreneurial experience in the real estate consulting industry. He used to be the chief information officer of state-owned and joint-stock commercial banks and the president of the investment banking department.
Other core members of the team have many years of experience in artificial intelligence, IOT and other fields.
