# Integration of AI in Self-Powered IoT Sensor Systems
## 7 The Use of ML Models in Self-Powered Sensor Systems
\begin{table}
\begin{tabular}{c c c c} \hline
**ML Algorithm Used** & **Application Integrating Self-Powered Sensor** & **Accuracy (\%)** & **Reference** \\ \hline Two-layer LSTM model & Self-sensing FR in VR and HMI & 99.87 & [235] \\ ML algorithm & Self-powered bionic hand with multi-functional & & 96.80 & [248] \\ RF & Human activity recognition using TENG-based HAR & 88 & [249] \\ RF, DNN & On-device PdM of machinery & 99 & [214] \\ NN & Speed monitoring of rotating machinery using TENG & \(>\)90 & [250] \\ RF, LR, DT & Sitting posture monitoring vest & 96.60; 95.50; 94.3 & [251] \\ Automated ML & Defect diagnosis of rolling bearings using TENG & 99.48 & [252] \\ CNN & Workplace activity monitoring using tactile & \(>\)98 & [253] \\  & tribo/piezo sensor & & \\ ML techniques & Human motion monitoring using hybrid & 85.6–100 & [254] \\ ML & Gait recognition and rehabilitation monitoring system & 96.70 & [255] \\ NN & Identity recognition via gait analysis & 100 & [256] \\ DL model & Monitoring vehicle collisions and human gestures & 100 & [257] \\ K-Means, SVM & Wrist posture recognition using PS-TENG & 95.18 & [258] \\ ML algorithms & Speech recognition and organ monitoring using & 99 & [259] \\ RF & HAR & 93 & [260] \\ Optimized ML algorithm & Motion sensing and smart sports applications & 99 & [261] \\ ML & Coaxial fibers for wearable strain sensing and & 95 & [236] \\  & triboelectric fabric & & \\ \hline \end{tabular} Note: BC—Bacterial cellulose; BCZT—BaCaZrTiO; DT—Decision Tree; HAR—Human activity recognition; HMI—Human Machine Interface; LR—Logistic Regression; PS—Polyacrylonitrile/sodium; PVA—Poly(vinyl alcohol); SVM—Support vector machine.  
\end{table} 
Table 5: ML in self-powered sensors. language recognition, achieving 95% and 100% accuracy, respectively. These algorithms have led to the development of complex applications that allow the integration of self-powered sensors. In the specialized literature, RF, LSTM, and CNN algorithms have been identified, and they have achieved accuracies of over 95%, allowing for the optimization of results from the hardware equipment. These applications range from medical monitoring, staff assistance, and helping people with disabilities to predictive diagnostics of machines, demonstrating the extensive use of fields for self-powered sensors. ML algorithms are evaluated through specific metrics, with accuracy being one of the most commonly used indicators in the comparative analysis of algorithms. The actual performance of self-powered sensor systems is influenced by the hardware constraints of the environment. Additional metrics, such as random-access memory (RAM) and flash memory consumption, inference time, optimality, completeness, and energy consumption per operation, can be evaluated in ML algorithms as future research directions. RF and SVM algorithms require more memory than optimized models, such as compact TinyML, MobileNet, or simple regressions. Studies [214,261] mention energy savings of up to 66.8% when opting for lightweight models adapted to the hardware. From this approach, future research directions include incorporating computational efficiency metrics alongside standard metrics such as accuracy, precision, recall, and F1-score, to facilitate the real-world implementation of self-powered IoT sensors in various applications. These results demonstrate the need to increase the research volume in the field by exploring new ML algorithms that could provide superior accuracy compared to those currently integrated. Additionally, expanding research will lead to new concepts and contribute to the technological advancement of self-powered sensors within applications that integrate ML algorithms.