# Robust IoT Activity Recognition via Stochastic and Deep Learning  
[PERSON]  
1 Department of Decision Sciences, School of Business, Macau University of Science and Technology, Macau, China; [EMAIL_ADDRESS]  
[PERSON]  
2 School of Computer and Science and Engineering, Sun Yat-sen University, Guangzhou 510275, China; [EMAIL_ADDRESS] (S.W. ); [EMAIL_ADDRESS] (X.Z.) [PERSON]  
2 School of Computer and Science and Engineering, Sun Yat-sen University, Guangzhou 510275, China; [EMAIL_ADDRESS] (S.W. ); [EMAIL_ADDRESS] (X.Z.) [PERSON]  
1 Department of Decision Sciences, School of Business, Macau University of Science and Technology, Macau, China; [EMAIL_ADDRESS]  
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: 2025  
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or deliver only sparse, low-resolution information [8]. In contrast, wireless sensing methods--including WiFi CSI analysis and radar systems--offer device-free monitoring by leveraging existing infrastructure; however, they can be less accurate due to environmental noise and may still pose indirect privacy risks by revealing personal routines through signal patterns. Also, radar is usually used for distance detection instead of activities, and meanwhile, most IoT devices are low-end hardware, without equipped with such devices [9]. These different methods underscore ongoing tradeoffs between privacy, accuracy, and usability. Systems that use visual or acoustic data can capture rich information at the cost of privacy, while wearable and ambient sensors are easier to use but sometimes lack the detail needed for precise recognition. Wireless techniques, although less intrusive, may not robustly capture fine-grained activities, and user compliance remains an issue for wearables. Additionally, complex scenarios like overlapping activities in smart homes can challenge ambient systems. Together, these challenges point to the need for a balanced solution that preserves privacy, ensures high accuracy, and integrates seamlessly into daily life without being overly intrusive or dependent on user actions. Modern sensor technology offers a promising solution. The devices, which come equipped with accelerometers and gyroscopes, can continuously track and record movement data in real time [10]. Originally developed to improve user interfaces and enhance gaming experiences [11], these sensors now enable detailed and non-invasive monitoring of everyday activities. Continuous monitoring with such devices reduces reliance on self-reported data and offers a more accurate picture of a person's physical state. Despite these benefits, it is not straightforward to implement an effective human activity recognition appraoch via sensing. Sensor data can be noisy and affected by environmental disturbances, complicating the extraction of meaningful information [12; 13]. In this paper, we aim to enhance the recognition accuracy and robustness of human activity recognition (HAR) systems in noisy IoT sensing environments by leveraging an innovative approach based on IMU data processing and feature extraction. Moreover, combining robust data analysis techniques with strong security measures is essential in IoT applications to safeguard user information. To overcome these challenges, our work presents a novel framework that merges traditional statistical methods with modern machine learning approaches. Therefore, the central research question addressed in this work is how to enhance human activity recognition accuracy and robustness in a noisy sensing environment for IoT devices. To address this problem, our method starts with a careful preprocessing stage that includes filtering and normalization to reduce noise and improve accuracy. We then apply principles from stochastic processes and statistical signal processing to the preprocessed data, extracting key features such as mean, variance, skewness, and kurtosis. This process helps us uncover the underlying patterns associated with different activities like walking, running, and squatting.