The sensor fusion technology
Sensor fusion technology is used to collect, process and evaluate data on the body, in the environment or in closed systems. The technology aims to improve the accuracy, reliability, and overall performance of the system. Automated processes can be created by fusing the data with the control elements creating new functionalities for wearable and stationary devices.
Sensor fusion systems are used in various segments such as automotive, aviation, industry 4.0, smart clothing, smart interior design, etc.
Monitoring of vital signs and biometric data
Vital signs monitoring involves the continuous or periodic measurement of key physiological parameters that provide insight into a person's health and well-being. The most important parameters include heart rate, blood pressure, respiratory rate, body temperature and oxygen saturation. In addition, other aspects such as movement and muscle contraction can be recorded.
Monitoring vital signs can help detect changes in a person's health such as signs of infection, dehydration or shortness of breath early to make more accurate and effective decisions about treatment and care.
Monitoring of industrial facilities
Information generated from the collected data in production plants can lead to productivity and reduction of downtime.
The system includes following sensor options: temperature sensors, pressure sensors, flow sensors, vibration sensors, light sensors, gas sensors, position and movement sensors.
The sensor data can be analyzed in real time to detect anomalies or potential errors, optimize production processes and improve workers safety and efficiency. The data can also be used to generate reports and insights that help plant managers make data-driven decisions about maintenance, production, and other aspects of the plant.
Artificial intelligence and control systems
Artificial intelligence (AI) plays a crucial role in sensor fusion systems by using data from multiple sensors to be analyzed and interpreted to generate insights for decision making. AI algorithms can learn from the sensor data and make predictions about future events or detect anomalies in the system.
The AI in sensor fusion systems can be used for the following examples: data processing and analysis, predictive maintenance, error detection and diagnosis, intelligent control, performance optimization, automation of processes and much more.
Sensor integration in textiles
The sensor integration process depends on the product and system architecture. Generally speaking, sensors can be embedded in fabrics, including clothing, bedding, seats, and upholstered furniture as well as into hard-cased mechanical parts as well as on-board of a PCBA.
Different integration techniques are used to integrate sensors into textile such as knitting, printing, weaving, gluing, using elastomers or mechanical connection components. Fully encapsulated integration methods can make the sensors and associated electronics waterproof, fire resistant, shock or abrasion resistant. A well thought-out integration leads to an improvement in data acquisition quality, ergonomics and outstanding usability.
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