Researchers generally use physical sensors to track the physical activity of an individual. These sensors include commercial wearables that help in tracking important data from human body. However, these sensors are not useful in understanding body’s dynamic chemistry. Scientists recently developed a new system that can give important insights on individual’s body.
Using New Strategy to Address Challenges of Wearable Biosensors
In a new study, Yichao Zhao and associates created a freestanding electrochemical sensing system (FESS). To create this disposable system, they used integration of bioelectronics and materials and engineering in the U.S. Using FESS, they could form a system-level design strategy. This strategy helped them to address diverse challenges of wearable biosensors. Moreover, it allowed them to seamlessly integrate these wearable devices with consumer electronics. This study is available for access in the journal Science Advances.
Through this experiment, scientists created a FESS-enabled smartwatch. The specialty of this smartwatch was it was equipped with self-sufficient wearable platform. It had electrochemical sensing, sweat sampling, and data display or transmission ability. This system was used in monitoring the profiles of sweat metabolites among people in high-intensive exercise and inactive settings.
In this research, after creating FESS, Zhao and associates attached this system to individual’s skin and electronics. For this purpose, they used double-sided adhesion forces without firm connectors. The system was integrated inside a custom-built smartwatch. It helped them in sampling, sweat induction, signal processing, electrochemical sensing, and data transmission or display. In the outcomes of this research, scientists found high-fidelity signal transduction. They also achieved a strong mechanical contact with human skin without hampering user motion.
Researchers highlighted the possibility of linking freestanding sensing system with potential wearable electronics. They underlined the usability of this technology to create high-fidelity health and wellness-associated datasets based on the day-to-day activities of users.