In many practical applications of the Internet of things, the original collected signal data contains a lot of noise, especially in motion-related scenes. It is necessary to accurately identify the start and end points of the effective signal activity area from the one-dimensional time series signal with a lot of noise to support the relevant analysis. Existing recognition methods based on dual threshold rules are very sensitive to noise. The presence of noise will cause the calculated recognition threshold to fail to match the original data of the non-noise segment, which leads to the recognition of random noise data as signal activity intervals or missed signal activity interval. Recognition methods based on machine learning and deep learning require a large amount of sample data. In IoT scenarios with a small sample size, the model will have underfitting problems, thereby reducing recognition accuracy. In order to accurately identify the signal activity interval in a one-dimensional time series signal with a lot of noise and a small amount of data, a signal activity interval recognition based on local dynamic threshold EasiLTOM is proposed. This method calculates the recognition threshold based on the local signal, and it uses the shortest signal length to filter noise spikes, which can avoid the influence of random noise on the recognition of signal activity intervals, solve the problems of missed detection and false detection, and improve the recognition accuracy. In addition, EasiLTOM requires a small amount of data, which is suitable for IoT scenarios with scarce data. In order to verify the effectiveness of EasiLTOM, this study collects surface EMG data of 14 people in 3 months, and conducts comparative experiments using two public data sets. The results show that EasiLTOM method can achieve an average recognition accuracy of 93.17% for the signal activity range, which is 15.03% and 4.70% higher than the existing dual threshold and machine learning methods, and has practical value in motion analysis related scenes.