## 6 Sensor Data Acquisition and Collection
### Sensor Data Collection by HMM
One venue is related to the improvement of biomechanical simulation and the FEA-based analysis. In further studies, the modeling support can be enhanced with a mixture of heterogeneous friction coefficients as well as more complex hyperelastic material models for the stump. The simulation and testing cases can also be automated for a different load or gait conditions. As our current work is focused on the identification of possible ways for producing physical replicas, a further elaboration of the design would be of interest, especially regarding the tradeoff among replica fidelity, emulation performance, and production cost. Another venue is related to the inclusion of multi-modality in the measurement and classification of prosthetic socket operation by means of higher-dimension tensor for collecting and analyzing the data from multiple sensors. To this end, we believe that VAE (Variational Autoencoder), another variant of Bayesian Inference, could be useful. For example, it could provide effective treatment of more complex probabilistic models for stochastic dynamic processes. Algorithms based on LSTM/RNN can also be integrated with an Autoencoder for sensor data treatment. Overall conceptualization and methodology by D.C. Biomechanical modeling and simulation by S.O., in collaboration with [PERSON], [PERSON] and M.K. ; FEA, physical prototyping with 3-D printing and integrating by S.O. Dynamic testing with Stewart platform by S.O. and K.N.T. The sensor deployment for dynamic testing by [PERSON] and M.K. The design of algorithms for sensor data acquisition and collection, operation condition analysis, and anomaly detection by [PERSON] and [PERSON]. All related software programs implemented by P.S. The case study was done by all authors. [PERSON] was responsible for the research supervision. All authors contributed to writing, reviewing, and editing. All authors have read and agreed to the published version of the manuscript. This work was supported by the research project SocketSense, funded by the European Union's Horizon 2020 research and innovation programme under grant agreement no. 825429 ([[https://www.socketsense.eu/](https://www.socketsense.eu/)]([https://www.socketsense.eu/](https://www.socketsense.eu/))) (accessed on 23 November 2021). The test-subject data such as residual limb geometry, weight, height, and age were provided by Ossur ([[https://www.ossur.com](https://www.ossur.com)]([https://www.ossur.com](https://www.ossur.com))) (accessed on 23 November 2021) based on a pilot study. The authors declare no conflict of interest. ## References

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