Key result
A fault detection framework for wireless IoT sensor-based healthcare monitoring achieved an 80% accuracy in problem identification.
A proposed fault detection framework for wireless IoT sensor-based healthcare monitoring achieves 80% accuracy in problem identification.
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May support IoT monitoring reliability; hypothesis-generating and requires validation before cardiac practice adoption.
Background The Internet of Things (IoT) and wireless sensor networks (WSNs) are now being explored and used in various sectors, thanks to recent technological advancements. Objectives The healthcare sector is one of the areas we will examine in this research. This study’s primary focus will be on the fault detection framework (FDF) for healthcare monitoring employing IoT sensors in a wireless environment. Materials and Methods Because isolating defects yields more pertinent information about the issues, fault detection first finds weaknesses in a system or process before isolating the intricate process or variable. Results and Discussion The outcome demonstrates that the suggested strategy achieves an acceptable level of 80% accuracy in problem identification, in addition to the greater number of patients recorded. Conclusion The outcomes show that the defect-detection system for wireless IoT sensor-based healthcare monitoring is efficient.
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Mulani et al. (2025) studied Healthcare monitoring. Fault detection framework (FDF) employing IoT sensors was evaluated on Problem identification accuracy. A fault detection framework for wireless IoT sensor-based healthcare monitoring achieved an 80% accuracy in problem identification.
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