The increasing availability of small, precise and easy-to-integrate sensors for measuring different physical quantities allows their use in innovative support systems such as wearables in healthcare and medical technology. The meaningful application of many such sensors requires advanced AI-based evaluation approaches to robustly extract meaningful information from the sensor population of a given support system. This challenge meets the requirements that the aforementioned support systems adapt individually to their users (batch size 1) and that this adaptation is done as far as possible on the algorithmic (AI-) and not on the hardware side. Conversely, this means that the embedded sensor population is chosen in such a way that its distribution is basically suitable for as many users as possible and that the number of individual sensors is restricted in a sensible way. The goal of the project is therefore to design methods that allow the optimization of hardware and AI components of smart sensors under the aspect of robust and efficient individualization.
This project focuses on two scenarios as examples. The first scenario (see illustration above) is directed to novel insoles that are inserted into shoes, for example, in the context of rehabilitation after injury and surgery of the lower extremities and continuously determine the leg load (pressure profile of the foot). Challenges in terms of individualization are weight, body size, foot shape, gait, etc.
The second scenario focuses on the prediction of limb movements based on biosignal measurements (sEMGs) on such muscles that actuate the limbs. Challenges in terms of individualization include electrode placement, body or limb dimensions, loading situations, etc.
The approach uses transfer learning-based feature selection methods- that is, novel feature selection technologies, which optimize the data representation such that it is best suitable for a subsequent individualization by means of software for ALL human partners- and combines them with domain knowledge from biomechanics and biosignal processing. In this way, both AI algorithms in the sense of black-box approaches and hybrid algorithms in the sense of AI-based grey- or white-box approaches can be designed.
- 1.Wolf-Homeyer S, Engelmann J, Schneider A. Application of reduced sensor movement sequences as a precursor for search area partitioning and a selection of discrete EEV-contour-ring fragments for active electrolocation. Bioinspiration & Biomimetics. 2018;13. doi:10.1088/1748-3190/aae23f
- 2.Prahm C, Schulz A, Paaßen B, et al. Counteracting Electrode Shifts in Upper-Limb Prosthesis Control via Transfer Learning. IEEE Transactions on Neural Systems and Rehabilitation Engineering. Published online March 2019. doi:10.1109/TNSRE.2019.2907200
- 3.Pfannschmidt L, Jakob J, Hinder F, Biehl M, Tino P, Hammer B. Feature relevance determination for ordinal regression in the context of feature redundancies and privileged information. Neurocomputing. 2020;416:266-279. doi:https://doi.org/10.1016/j.neucom.2019.12.133
- 4.Basa D, Schneider A. Learning point-to-point movements on an elastic limb using dynamic movement primitives. Robotics and Autonomous Systems. 2015;66:55-63. doi:https://doi.org/10.1016/j.robot.2014.12.011