The integration of 3D printing technology into wearable sensor systems has catalyzed a paradigm shift in sports psychology and athlete health monitoring by enabling real-time, personalized data collection on physiological and psychological states. In this study, not only is the technical potential of these advancements examined but their real-world applications in sports psychology are also critically assessed. While the existing research primarily focuses on sensor fabrication and data acquisition, a significant gap remains in the evaluation of their direct impact on decision-making processes in coaching, mental resilience, and long-term psychological adaptation in athletes. A critical analysis of the current state of 3D-printed wearable sensors is conducted, highlighting both their advantages and limitations. By combining theoretical insights with practical considerations, a comprehensive framework is established for understanding how sensor-based interventions can be effectively incorporated into sports training and psychological evaluation. Future research should prioritize longitudinal studies, athlete-centered validation, and interdisciplinary collaborations to bridge the gap between technological developments and real-world applications. Additionally, the integration of artificial intelligence and advanced biomaterials has significant potential to enhance the reliability and interpretability of sensor-driven interventions. However, without rigorous scientific validation, their effectiveness remains uncertain. This study highlights the importance of a systematic approach in implementing and evaluating 3D-printed wearable sensors in sports psychology.

Monitoring object displacement is critical for structural health monitoring (SHM). Radio frequency identification (RFID) sensors can be used for this purpose. Using more sensors enhances displacement estimation accuracy, especially when it is realized through the use of machine learning (ML) algorithms for predicting the direction of arrival of the associated signals. Our research shows that ML algorithms, in conjunction with adequate RFID passive sensor data, can precisely evaluate azimuth angles. However, increasing the number of sensors can lead to gaps in the data, which typical numerical methods such as interpolation and imputation may not fully resolve. To overcome this challenge, we propose enhancing the sensitivity of 3D-printed passive RFID sensor arrays using a novel photoluminescence-based RF signal enhancement technique. This can boost received RF signal levels by 2 dB to 8 dB, depending on the propagation mode (near-field or far-field). Hence, it effectively mitigates the issue of missing data without necessitating changes in transmit power levels or the number of sensors. This approach, which enables remote shaping of radiation patterns via light, can herald new prospects in the development of smart antennas for various applications apart from SHM, such as biomedicine and aerospace.

Radio frequency identification (RFID) tags are small, low-cost, wearable, and wireless sensors that can detect movement in structures, humans, or robots. In this paper, we use passive RFID tags for structural health monitoring by detecting displacements. We employ a novel process of using 3D printable embedded passive RFID tags within uniform linear arrays together with the multiple signal classification algorithm to estimate the direction of arrival using only the phase of the backscattered signals. We validate our proposed approach via data collected from real-world experiments using a unipolar RFID reader antenna and both narrowband and wideband measurements.

Fiber-reinforced polymers (FRPs) are used in various applications and industries such as aerospace, automotive, marine, and energy. Therefore, monitoring their integrity is critical from both safety and economic viewpoints. Non-destructive testing (NDT) methods can help detect defects or damage in FRPs during manufacturing or while in use. In this work, we develop a new NDT approach for FRPs to detect and identify common defects that may occur during manufacturing. The proposed approach is based on scanning FRP plates using a 3D-printed radio-frequency identification (RFID) tag array mounted on a conveyor belt and processing the sensed data via appropriate deep learning algorithms. Using data collected from real experiments, we verify that our novel RFID-tag-array-based NDT approach can identify defects such as voids and misalignments in carbon- and glass-fiber-reinforced polymers with high accuracy. The proposed approach is cost-effective, scalable, and can be used instead of or in addition to existing NDT techniques that use alternative modes of sensing such as ultrasound, x-ray, or vision.