## 3 Related Work
Yet, recent research demonstrates there is still a lack of complete frameworks that include embedded operation sensing, biomechanical analysis, socket optimization design, and experimental validation. As one approach to modern limb prosthesis, our work is searching for an integration of technologies to support novel sensory and data analytic capabilities. The overall concept of our _mechatronics-twin_ framework is similar to the notion of digital-twin in regard to the replication of a physical target system on the basis of measurements [25]. By the notion of _mechatronics_, we refer in particular to the effort of developing the framework with a synergetic integration of mechanical, electronic, and electrical solutions, with the key features ranging from biomechanical modeling and simulation, to physical emulation by a Stewart manipulator, and to operation sensing with printed sensors and advanced analysis functions. The framework differs from a digital-twin by also enabling a combination of virtual replication (by digital means) and physical replication (by the robotic manipulator). The approach addresses especially the challenge of complex biomechanical dynamics of prosthesis operation as well as the need of physical replica for data generation and sensor calibration. Clearly, any approaches that rely only on the amputees will not be preferable. This would, in the worst case, cause further trauma to the amputees. For the physical dynamic testing, our approach adopts the printed wearable soft sensor Quantum Technology Super Sensor (QTSS(tm)) [26] as the base technology for the intra-socket dynamic pressure measurement. Such a choice is motivated by the facts that such sensors could be more robust against environmental disturbances (e.g., vibration, acceleration, or temperature fluctuation) and more transparent for the intra-socket deployment, compared to other pressure sensor technologies, such as resistive transducer, piezoelectric transducer, optical pressure transducer, and capacitive transducer [27]. As a kind of Force Sensing Resistor (FSR), the sensel of the QTSS(tm) sensor strip consists of material with quantum conduction that exhibits a change of resistance according to the pressure applied on its surface. Meanwhile, as for many other applications of print wearable electronics, some fundamental performance concerns need to be carefully managed, such as regarding the drift (i.e., the change of measurement over time with the same load), hysteresis (i.e., the divergence of measurement in loading and unloading), and lack of repeatability (i.e., the ability of repeating a measurement with the same load) [28; 29; 30; 31]. For successful usage of wearable sensors for intra-socket condition measurement, sensor calibration becomes therefore important. For the calibration of wearable resistive sensors, it is necessary to take the actual operational conditions into consideration. One reason for this is that the curvature and surface hardness of prosthetic socket could affect sensor accuracy [32]. Moreover, different bio-mechanical configurations and gait behaviors also lead to different dynamic load conditions that in turn may interfere with the sensor drift and hysteresis, as well as other frequency response characteristics negatively. To cope with such a fundamental issue, we adopt neural networks as a special type of probabilistic model for being able to learn the sensor functions directly based on in-socket test data and to use the same data-driven strategy to facilitate sensor re-calibration. In concept, our approach is similar to the usage of probabilistic models for the treatment of uncertainties in robot perception [33; 34; 35; 36; 37]. We use in particular Autoencoder (AE), which is a specific variant of deep learning neural networks typically used for signal denoising and data compression [38; 39; 40] to support an unsupervised learning-based synthesis of sensor models.