This study presents a thin-film transistor (TFT) with controllable temporal dynamics for temporal self-adaptive reservoir computing (RC) with a closed-loop architecture. The TFT, based on indium gallium zinc oxide (IGZO), exhibits excellent cycle-to-cycle uniformity and four-order tunable timescale. By leveraging the device's controllable temporal dynamics, the researchers constructed a temporal adaptive reservoir capable of extracting temporal information across multiple timescales, thereby improving accuracy in human activity recognition (HAR) tasks. The system was tested on six human activities, achieving an accuracy of 96.7%, a significant improvement over the 84.2% of a conventional RC system. The study also introduces a closed-loop architecture that dynamically adjusts reservoir hyperparameters based on real-time input, enabling the system to adapt to varying input speeds. This architecture was validated by accurately classifying objects moving at different speeds, demonstrating the system's ability to self-adapt to diverse temporal conditions. The closed-loop RC system was tested on a dataset of moving objects at varying speeds, showing real-time recognition accuracy even with incomplete input data. The researchers demonstrated that the proposed TFT-based RC system can achieve high performance with low power consumption and excellent endurance, with the drain current response remaining stable even after 10^8 pulses. The system's ability to self-adapt to varying input conditions was further validated through a closed-loop architecture that adjusts the reservoir's temporal dynamics based on real-time feedback. This approach allows the system to optimize its performance without prior knowledge of the input timescale, making it suitable for a wide range of applications. The study highlights the potential of TFT-based RC systems for real-time processing of spatiotemporal signals with complex temporal characteristics. The closed-loop architecture enables the system to self-adapt to varying input conditions, improving its performance and efficiency. The results demonstrate that the proposed system can achieve high accuracy in HAR tasks and real-time object recognition, making it a promising solution for applications requiring real-time processing of spatiotemporal data.This study presents a thin-film transistor (TFT) with controllable temporal dynamics for temporal self-adaptive reservoir computing (RC) with a closed-loop architecture. The TFT, based on indium gallium zinc oxide (IGZO), exhibits excellent cycle-to-cycle uniformity and four-order tunable timescale. By leveraging the device's controllable temporal dynamics, the researchers constructed a temporal adaptive reservoir capable of extracting temporal information across multiple timescales, thereby improving accuracy in human activity recognition (HAR) tasks. The system was tested on six human activities, achieving an accuracy of 96.7%, a significant improvement over the 84.2% of a conventional RC system. The study also introduces a closed-loop architecture that dynamically adjusts reservoir hyperparameters based on real-time input, enabling the system to adapt to varying input speeds. This architecture was validated by accurately classifying objects moving at different speeds, demonstrating the system's ability to self-adapt to diverse temporal conditions. The closed-loop RC system was tested on a dataset of moving objects at varying speeds, showing real-time recognition accuracy even with incomplete input data. The researchers demonstrated that the proposed TFT-based RC system can achieve high performance with low power consumption and excellent endurance, with the drain current response remaining stable even after 10^8 pulses. The system's ability to self-adapt to varying input conditions was further validated through a closed-loop architecture that adjusts the reservoir's temporal dynamics based on real-time feedback. This approach allows the system to optimize its performance without prior knowledge of the input timescale, making it suitable for a wide range of applications. The study highlights the potential of TFT-based RC systems for real-time processing of spatiotemporal signals with complex temporal characteristics. The closed-loop architecture enables the system to self-adapt to varying input conditions, improving its performance and efficiency. The results demonstrate that the proposed system can achieve high accuracy in HAR tasks and real-time object recognition, making it a promising solution for applications requiring real-time processing of spatiotemporal data.