# A Systematic Review of Insole Sensor Technology: Recent Studies and Future Directions
## 5 Discussion  
The integration of multiple sensors enhances data accuracy, reliability, and functionality, offering improved spatial and temporal resolution, redundancy, and comprehensive environmental monitoring capabilities across various scientific and industrial applications. Results obtained with laboratory solutions are more accurate; on the other hand, wearable solutions offer the advantage of estimating temporal parameters during regular training sessions [12]. In [PERSON], [PERSON] et al. [8] study, it was possible to estimate peak tibial bone force during running with an IMU system combined with a pressure-sensing insole and a trained algorithm with average errors of 5,7%. The authors conclude that it is necessary to improve and determine calibration methods to reduce the percentage of errors and to estimate the forces with more precision. In another study [24], the results obtained show that a low-cost pressure insole and tendon sensor can produce estimates similar to the reported accuracy of commercial devices. Insole data offers high precision and accuracy in estimations, facilitating detailed analysis of gait patterns, pressure distribution, and biomechanical metrics, which is crucial for advanced clinical assessments and optimizing sports performance. For the ankle joint torque estimations, we found that the insole data was generally more accurate than the ankle angle from inverse kinematics or the tendon sensor. Additionally, the insole data alone were accurate for walking. However, it was insufficient for the calf to raise task [24]. The validation of market-available equipment ensures accuracy, reliability, and compliance with industry standards, enhancing performance consistency and user safety, which is crucial for scientific research, medical applications, and industrial processes. For example, a study showed the validation of the gait parameters from FeetMe insoles under single and cognitive dual-task conditions, with promising results for future applications. Their flexibility, portability, and ease of use make them relevant tools for large-scale analyses of patients with Parkinson's disease [9]. In this study, it is important to understand what has been accomplished so far, define what is on the right track and what urgently needs to be improved, and do so in future works and research. Nevertheless, there has been no conclusion on which sensor type or position is the best for a specific application. The data of most studies lack diversity. Moreover, data collection is mostly carried out in fully controlled environments. Therefore, the data collected and analyzed by the studies may not represent daily life conditions due to the limited amount of data. Multi-discipline research should be encouraged to overcome this limitation, connecting engineers to doctors and healthcare workers to collect more data for analysis and improve robustness. From the perspective of applications, gait studies are the main focus. A diverse subject database, including sensor readings from various types and positions, should be collected to compare the prototypes and evaluate the performance with the same data [3]. Regarding hardware, [PERSON] et al. 's work [26] identified the necessity, in future work, of optimizing sensor technology to reduce the error in the estimation of the force and acceleration values to improve the wireless communication range [29], as well as the need to develop dynamic sensor-driven power management techniques to extend battery life [29]. Other works [1,17] pointed out the necessity of improving the ergonomics; size; manufacturing and cost; and battery technology, optimization and storage of wearable devices. The use and necessity of equipping the insole with GPS is also referred in [PERSON] et al. [29] work, especially in adults with Alzheimer's disease, dementia, autism, or other cognitive disorders and people who need real-time location information.