Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Body sensor network (BSN) applications depend on accurate and precise data from body-worn devices, but issues related to sensor variations, body mounting variations, and node-to-node synchronization can dramatically impact the quality and reliability of collected data and, ultimately, application fidelity. Characterizing and addressing these sources of error -- which are both static and dynamic (e.g. sensors suffer from static manufacturing variability and dynamic environmental impacts) -- within the context of application requirements is therefore necessary for the viability of such applications.   This work characterizes and addresses errors related to sensor and mounting calibration and node synchronization on a case study application -- knee joint angle as measured during walking by an accelerometer- and gyroscope-based BSN. Using an industrial optical motion capture system to provide ground truth, calibration and synchronization error are quantified and the efficacy of solutions for reducing such errors are evaluated.
['Shanshan Chen', 'Jeff S. Brantley', 'Taeyoung Kim', 'John Lach']
I am writing a research paper on a idea Characterizing and minimizing synchronization and calibration errors in inertial body sensor networks
[{"from": "human", "value": "I am writing a research paper on a idea Characterizing and minimizing synchronization and calibration errors in inertial body sensor networks"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Body sensor network (BSN) applications depend on accurate and precise data from body-worn devices, but issues related to sensor variations, body mounting variations, and node-to-node synchronization can dramatically impact the quality and reliability of collected data and, ultimately, application fidelity. Characterizing and addressing these sources of error -- which are both static and dynamic (e.g. sensors suffer from static manufacturing variability and dynamic environmental impacts) -- within the context of application requirements is therefore necessary for the viability of such applications.   This work characterizes and addresses errors related to sensor and mounting calibration and node synchronization on a case study application -- knee joint angle as measured during walking by an accelerometer- and gyroscope-based BSN. Using an industrial optical motion capture system to provide ground truth, calibration and synchronization error are quantified and the efficacy of solutions for reducing such errors are evaluated."}]