# Integration of AI in Self-Powered IoT Sensor Systems
## 6 Self-Powered IoT Sensors in the AI Field
### Detailed Literature Analysis of Self-Power IoT Sensors in the AI Field
The results achieved an accuracy of 99.87%, highlighting the enhanced capabilities of combining technologies in human-machine interaction and virtual reality. The healthcare IoT field stands out with innovations such as self-powered footwear that monitors the plantar condition of the foot, making it possible to identify gait anomalies using AI tools. This system assists in medical diagnosis and patient recovery [202]. Additionally, autonomous smart prosthetics are integrated using TENG sensors, which use DL models to detect lower limb movements in data analysis. Thus, the paper by [PERSON] et al. [204] achieved an accuracy of over 99%. Data privacy is the largest issue facing the medical domain, especially concerning the self-powered sensors. Strict safeguards pertaining to GDPR and HIPAA regulations apply to their use. Unauthorized access is prevented by local edge computing, secure communication protocols, multifactor authentication, end-to-end encryption technologies, and other custom-made software layers. Medical data is stored on certified servers or in cloud environments that adhere to international data protection standards. Protection technologies, frequent audits, certifications, studies on large patient samples, assessments by specialized compliance assurance committees, and data processing transparency are required to guarantee the clinical compliance of self-powered IoT sensors in medical applications. Integrating self-powered sensors in the medical field is made possible by guaranteeing all of these security and privacy measures. In the field of smart cities, AI components predict weather conditions, such as wind direction and speed, using a self-powered wind generator based on an EMG with an RB-TENG and a GRU algorithm. This algorithm represents a simplified version of LSTM. The results led to an accuracy of 96% for speed and 99.04% for wind direction [197]. In the industrial IoT field, AI components are used to detect industrial machine failures early. The on-device PdM system uses self-powered sensors and TinyML technology, achieving over 99% accuracy with a 66.8% energy savings compared to traditional sensors [214]. Other research explores the lubrication of bearings using DL techniques [210]. The field of wearable devices introduces systems for smart textiles that also use TENG sensors. These systems identify body movement to monitor physical activities and emotional states. In this context, ML algorithms analyze the signals from these sensors and interpret them for sports or medical purposes [233; 237]. [PERSON] et al. [237] use a graphene textile to distinguish human movements, demonstrating superior results. Besides TENG, there are also hybrid energy sources that incorporate piezoelectricity combined with EMG in mobile applications. These applications target self-powered backpacks that use AI algorithms to recognize users' movements to optimize energy consumption, generating up to 4.5 mW of average power during running [225]. Optimizing energy consumption, increasing spectral efficiency, adapting transmission to the variable conditions of the communication channel, autonomy, and scalability are dictated by the modulation techniques used by IoT sensors. New modulation schemes have been introduced in the recent literature. Such a modulation scheme is present in the system based on reconfigurable intelligent surfaces [246]. It transmits information and energy harvesting with superior performance in terms of spectral efficiency and energy reliability. Additionally, the scheme of the paper [247] uses the initial condition indexing of chaotic sequences. They are used in short-range wireless communications, having low complexity.