# Integration of AI in Self-Powered IoT Sensor Systems
## 7 The Use of ML Models in Self-Powered Sensor Systems  
ML technologies have been applied in recent years in a multitude of fields, including technologies aimed at self-powered sensors. These algorithms are used in intelligent systems for monitoring, recognition, diagnosis, prediction, or identification tasks. ML algorithms have been used in applications for these tasks. Still, the performance of the proposed model in the research is evaluated based on specific indicators of the type of algorithm used. A benchmark indicator for all these algorithms is accuracy, a widely used metric to express the proportion of correct predictions relative to the total predictions made. Although accuracy is insufficient to evaluate a model's performance fully, it provides an overview of the quality of the developed model. In the specialized literature, 61 papers were published between 2020 and 2025 that address ML models in self-powered sensor systems. Table 5 presents a comparative analysis of the models used in the reviewed works, the authors' accuracy, and the research's primary objective. [PERSON] et al. [235] use the two-layer LSTM model for facial and emotion recognition in the VR environment. The model achieved an accuracy of 99.87%, demonstrating its capabilities for use in psychological therapy. Similarly, [PERSON] et al. [248] employ an autonomous bionic sensor and an ML algorithm to classify human gestures, achieving an accuracy of 96.8%. This sensor contributed to the development of a multifunctional robotic hand. For monitoring human activities, [PERSON] et al. [249] use RF, which achieves an accuracy of 88% in classifying different types of activities, such as walking, running, or jumping. Another baseline study is by [PERSON] et al. [214], where the RF algorithm and the Deep Learning Network (DLN) are tested in PdM systems, achieving an accuracy of 99% and demonstrating a 66.8% reduction in energy consumption through integrating self-powered sensors. In the industrial field, [PERSON] et al. [250] propose a neural model for monitoring the speed of rotating machines, achieving an accuracy of 90%. The paper demonstrates early defect detection. In the paper by [PERSON] et al. [252], an ML model combined with a signal decomposition method allowed for diagnosing bearing defects, achieving an accuracy of 99.48%. In the field of health, refs. [251; 255] use TENG sensors together with RF, LR, and DT methods, achieving accuracies of 96.6%, 95.5%, and 94.3% for monitoring the human body's position. This type of application prevents problems associated with a sedentary lifestyle. [PERSON] at al. [255] introduce a textile system for recognizing pathological gait, achieving an accuracy of 96.7% in the medical recovery process. Some studies explore applications related to sports, and, thus, the well-being of the human body, such as refs. [255; 262], where the hybrid piezoelectric-electromagnetic system, together with ML techniques, monitors human movements and achieves an accuracy ranging from 85.6% to 100%. [PERSON] et al. [256] present a gait recognition system based on a piezoelectric sensor with precise identification of three individuals, achieving an accuracy of 100%. Refs. [257; 258] detect collisions and recognize postures using a CNN model, achieving an accuracy of 100%. In contrast, in the second work, K-Means and SVM are used, achieving an accuracy of 95.18% in recognizing 12 wrist postures. Also in the medical field, [PERSON] et al. [259] present 3D printed triboelectric sensors, along with ML algorithms for organ monitoring and silent speech recognition, achieving an accuracy of 99%. [PERSON] et al. [260] introduce the RF algorithm with an accuracy of 93% in recognizing human activities, a system that integrates TENG sensors with mobile applications. [PERSON] et al. [261], through a customizable textile sensor, use ML algorithms that achieve 99% accuracy in recognizing five body movements, while [PERSON] et al. [236] employ a coaxial wire resistant to 15,000 cycles for gait and sign