This energy harvesting approach extends usability by eliminating the need for frequent loads. Thus, state-of-the-art electronics for smart insoles focus on maximizing the autonomy and reliability of the transmission without adding volume or weight that compromises use during intense physical activities. Table 1 presents a summary of some equipment available on the market and their specifications. Although these are distinct systems, the measurement devices share a similar underlying conceptual model that uses a sensor array to measure the pressure exerted in each sensor. These devices allow for the gathering of crucial, leading to a more accurate diagnosis, such as the pressure exerted by the foot (as a whole), the existence of areas that are the focus of superior tensions, the area of contact between the foot and the ground surface and, the center of pressure. Although the operating principle is based on the same fundamentals, there are some differences in the testing methods and data processing, partly due to the specifics of each software. Although several technologies are available for assessing plantar pressure, the effectiveness of each device in accurately capturing postural changes and their biomechanical implications still needs to be fully understood [4]. In more recent years, there were some innovations, and with that, other solutions were presented, such as a speckle-patterned system for strain analysis, utilizing digital image correlation [20]; and the insoles with piezoresistive sensors using carbon nanotube conductive films and ethylene vinyl acetate (EVA) foam [21]. Regarding the smart insole prototypes with a wireless connection, the 3D-printed capacitive insole [22] and the Polydimethylsiloxane (PDMS)-based capacitive sensor insole [23] demonstrated a superior performance, making them highly effective for high-precision plantar pressure and gait monitoring applications. The 3D-printed capacitive insole accomplished high scores in sensitivity, stability, linearity, low hysteresis, and rapid and accurate response. These two prototypes provide robust solutions for accurate real-time plantar pressure monitoring. \begin{table}
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In recent investigations of prototypes with AI but without wireless capability, the focus was on studying GRF. In one investigation, the ML technique Gaussian Process Regression was implemented in an insole with pressure sensors [24]. The results were accurate and appropriate for clinical trials and daily life. Another study presented a prototype with 3D-printed porous piezoresistive sensors in a commercial insole and with the employment of Hammerstein-Wiener models [25]. The various studies incorporating wireless transmission and AI technology revealed significant improvements to a range of fields of knowledge and applications. For instance, the study on low-cost smart insoles employing recurrent neural networks enhanced GRF and center of pressure (CoP) predictions by over 30% compared to traditional methods, promising cost-effective gait analysis in clinical and day-to-day life [26]. The CapSoles system reached a 95% accuracy in user identification and 82% in distinguishing floor types using capacitive sensing [27]. Another study involving a sensor insole designed for gait analysis achieved a remarkable 98.75% user recognition accuracy, underscoring its potential for sports and healthcare, where precision is critical [28]. Recent technological developments of smart insoles have driven significant advances in gait analysis and monitoring of plantar pressure distribution. However, relevant limitations persist in terms of anatomical adaptability, intelligent interpretation in real time, and applicability in real contexts outside the laboratory environment. The literature reports that most published research is based on limited scenarios and does not consider the range of postures or dynamic situations that can influence plantar pressure distribution.