The mobile crowd sensing technology in the environment integrating human, machines and things is an emerging direction in social computing. In kinematics research, continuous blood pressure monitoring and calibration are the basis for revealing the correlation between athlete motor function and blood pressure. At the same time, in the field of medical research, hypertension can be more easily controlled, thus improving the effectiveness of hypertension treatment. This paper presents the design principle of a human-machine fusion system based on CrowdOS, a mobile crowd sensing platform. The system innovatively establishes the correlation between blood pressure and exercise, improves the accuracy of cuffless blood pressure measurement, and verifies the feasibility of calibrating continuous cuffless blood pressure measurement based on exercise information. Using our system and electronic cuff sphygmomanometer, we measured 65 groups of data in walking, running, sitting and climbing stairs, each group lasting about 10 minutes. Based on these data, we established a regression analysis model for blood pressure measurement calibration. The accuracy of blood pressure calibration was improved from the original systolic root mean square error of 13.43mmHg and diastolic root mean square error of 8.35mmHg to 9.76mmHg and 5.56mmHg. The design method proposed in this paper provides a feasible solution for continuous cuffless blood pressure measurement and calibration, and shows broad application prospects in the fields of athlete scientific training and medical care. INDEX TERMS IoT, human-computer interaction, blood pressure calibration, blood pressure monitoring.

The information in the working environment of industrial Internet is characterized by diversity, semantics, hierarchy, and relevance. However, the existing representation methods of environmental information mostly emphasize the concepts and relationships in the environment and have an insufficient understanding of the items and relationships at the instance level. There are also some problems such as low visualization of knowledge representation, poor human-machine interaction ability, insufficient knowledge reasoning ability, and slow knowledge search speed, which cannot meet the needs of intelligent and personalized service. Based on this, this paper designs a cognitive information representation model based on a knowledge graph, which combines the perceptual information of industrial robot ontology with semantic description information such as functional attributes obtained from the Internet to form a structured and logically reasoned cognitive knowledge graph including perception layer and cognition layer. Aiming at the problem that the data sources of the knowledge base for constructing the cognitive knowledge graph are wide and heterogeneous, and there are entity semantic differences and knowledge system differences among different data sources, a multimodal entity semantic fusion model based on vector features and a system fusion framework based on HowNet are designed, and the environment description information such as object semantics, attributes, relations, spatial location, and context acquired by industrial robots and their own state information are unified and standardized. The automatic representation of robot perceived information is realized, and the universality, systematicness, and intuition of robot cognitive information representation are enhanced, so that the cognition reasoning ability and knowledge retrieval efficiency of robots in the industrial Internet environment can be effectively improved.