This study demonstrates a data-driven decision support system to aid in rectification of prosthetic sockets aimed at improving overall comfort perceived by amputees. Prosthetic technology, particularly in the realm of socket design, plays a pivotal role in rehabilitation for individuals with limb amputations. Prosthetic sockets, which serve as the critical interface between the residual limb and the artificial limb, enable amputees to walk without the need for invasive implants that connect directly to the bone of the residual limb. This study focuses on the role of intra-socket pressure in socket performance and its impact on optimal socket rectifications for improving comfort in transfemoral amputees. Employing thin Force Sensing Resistor (FSR) sensors, the research measures dynamic pressure variations across individual gait cycles. To explore the effects of altered pressure distribution on socket performance, a clinical trial was conducted consisting of four different socket configurations across several participants, one of which was with no pad inserted and three of which incorporated a silicone pad to modify the dynamic pressure profiles. With data from multiple participants including specific dynamic pressure features extracted from FSR sensors, and subjective feedback of comfort, a Multi-Layer Perceptron (MLP) model is trained to establish predictive relationships between intra-socket pressure and appropriate rectification action. The findings suggest that the MLP agent is more accurate at suggesting rectification actions to prosthetists when compared to simpler classification algorithms such as Random Forest, XGBoost and Logistic regression, laying the foundation for future advancements in prosthetic design.

Flexible wearable sensor electronics, combined with advanced software functions, pave the way toward increasingly intelligent healthcare devices. One important application area is limb prosthesis, where printed flexible sensor solutions enable efficient monitoring and assessing of the actual intra-socket dynamic operation conditions in clinical and other more natural environments. However, the data collected by such sensors suffer from variations and errors, leading to difficulty in perceiving the actual operational conditions. This paper proposes a novel method for detecting anomalies in the data that are collected for measuring the intra-socket dynamic operation conditions by printed flexible wearable sensors. A discrete generative model based on Variational AutoEncoder (VAE) is used first to encode the collected multi-variant time-series data in terms of latent states. After that, a clustering method based on the Self-Organizing Map (SOM) is used to acquire discrete and interpretable representations of the VAE encoded latent states. An adaptive Markov chain is utilized to detect anomalies by quantifying state transitions and revealing temporal dependencies. The contributions of the proposed architecture conclude as follows: (1) Using the VAE-SOM hybrid model to regularize the continues data as discrete states, supporting interpreting the operational data to analytic models. (2) Employing adaptive Markov chains to generalize the transitions of these states, allowing to model the complex operational conditions. Compared with benchmark methods, our architecture is validated via two public datasets and achieves the best F1 scores. Moreover, we measure the run-time performance of this lightweight architecture. The results indicate that the proposed method performs low computational complexity, facilitating the applications on real-life productions.

A Stewart platform is a six-degree-of-freedom parallel manipulator widely used as the motion base for flight simulators, antenna positioning systems, machine tool technology, etc. This work presents a novel mechatronics-twin framework that integrates such a manipulator with advanced biomechanical models and simulations for effective exploration of operational behaviors of prosthetic sockets with amputees. By means of the biomechanical models and simulations, the framework allows the users to first analyze the fundamental operational characteristics of individual amputees according to their specific body geometries, pelvis-femur structures, sizes of transfemoral sockets, etc. Such operational characteristics are then fed to one Stewart platform as the reference control signals for the generation of dynamic loads and behaviors of prosthetic sockets that are otherwise difficult to observe or realize with the real amputees. Experiments in form of integration testing show that the proposed control strategy is capable of generating expected dynamic operational conditions. Currently, the mechatronics-twin framework supports a wide range of biomechanical configurations and the quantification of the respective intra-socket load conditions for socket design optimization and anomaly detection.

Lower limb prostheses offer a solution to restore the ambulation and self-esteem of amputees. One key component is the prosthetic socket that serves as the interface between prosthetic device and amputee stump and thereby has a wide range of impacts on efficient fitting, appropriate load transmission, operational stability, and control. For the design and optimization of a prosthetic socket, an understanding of the actual intra-socket operational conditions becomes therefore necessary. This is however a difficult task due to the inherent complexity and restricted observability of socket operation. In this study, an innovative mechatronics-twin framework that integrates advanced biomechanical models and simulations with physical prototyping and dynamic operation testing for effective exploration of operational behaviors of prosthetic sockets with amputees is proposed. Within this framework, a specific Stewart manipulator is developed to enable dynamic operation testing, in particular for a well-managed generation of dynamic intra-socket loads and behaviors that are otherwise difficult to observe or realize with the real amputees. A combination of deep learning and Bayesian Inference algorithms is then employed for analyzing the intra-socket load conditions and revealing possible anomalous. 

A decision support system (DSS) was developed that outputs suggestions for socket-rectification actions to the prosthetist, aiming at improving the fitness of transfemoral prosthetic socket design and reducing the time needed for the final socket design. For this purpose, the DSS employs a fuzzy-logic inference engine (IE) which combines a set of rectification rules with pressure measurements generated by sensors embedded in the socket, for deciding the rectification actions. The latter is then processed by an algorithm that receives, manipulates and modifies a 3D digital socket model as a triangle mesh formatted inside an STL file. The DSS results were validated and tested in an FEA simulation environment, by simulating and comparing the donning process among a good-fitting socket, a loose socket (poor-fit) and several rectified sockets produced by the proposed DSS. The simulation results indicate that volume reduction improves the pressure distribution over the stump. However, as the intensity of socket rectification increases, i.e., as volume reduction increases, high pressures appear in other parts of the socket which generate discomfort. Therefore, a trade-off is required between the amount of rectification and the balance of the pressure distributions experienced at the stump.