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approaches must be chosen. These algorithms extract the desired information (smart data) directly from the sensor data (big data). They encompass the full range of machine- learning methods, including linear regression, neural networks, random forest and hidden Markov models. A typical algorithm pipeline for
data-driven approaches that can be implemented in the iCOMOX consist of three components: (1) data pre- processing, (2) feature extraction and dimensionality reduction, and (3) the actual machine learning algorithm; see Figure 3. During pre-processing, downstream
algorithms, especially the machine- learning algorithms, converge the data to an optimum solution within the shortest possible time. Missing data must thereby be replaced using simple interpolation methods, in reference to the time dependence and interdependence between different sensor data. Furthermore, the data is modified by pre-whitening algorithms in such a way that they appear to be mutually independent. As a result, there are no more linear dependencies in time series or between sensors. Principal component analysis (PCA), independent component analysis (ICA) and so-called “whitening filters” are typical algorithms used in pre-whitening. During feature extraction,
characteristics, also known as features, are derived from the pre- processed data. This part of the processing chain strongly depends on the actual application. Due to the limited computing power of embedded platforms, it is not yet possible here to implement computationally- intensive, fully-automated algorithms that evaluate the various features and use specific optimisation criteria to find the best features – genetic algorithms would be included here. Rather, for embedded platforms such as the iCOMOX that have low power consumption, the method used for extracting features must
08 May 2022
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be specified manually for each individual application. The possible methods include transforming data into the frequency domain (fast Fourier transformation), applying a logarithm to the raw sensor data, normalising the accelerometer or gyroscope data, finding the largest eigen vectors in PCA, or performing other calculations on the raw sensor data. Different algorithms for feature extraction can also be selected for different sensors. A large feature vector containing all the relevant features from all of the sensors is then obtained. If the vector’s dimensionality exceeds
a certain size, it must be reduced through dimensionality-reduction algorithms. The minimum and/or maximum values within a certain window can simply be taken, or more complex algorithms such as PCA or self-organising maps (SOM) can be used. Only after the complete pre-
processing of the data and the extraction of features relevant to the respective application can the machine- learning algorithms be employed to extract different information on the embedded platform. As was the case for feature extraction, the selection of the machine-learning algorithm strongly depends on the application. Fully- automated selection of the optimum learning algorithm – for example, via genetic algorithms – is also not possible due to the limited computing
power. However, even somewhat more complex neural networks, including the training phase, can be implemented on embedded platforms such as iCOMOX. The decisive factor here is the limited available memory. For this reason, the machine-learning algorithms, as well as the other algorithms, must be modified to directly process the sensor data. Each data point is used only
once; for example, all of the relevant information is extracted directly, and the memory-intensive collection of large amounts of data and the associated high data transfer and storage costs are eliminated. This type of processing is also known as “streaming analytics”.
Dynamic pose estimation with model-based approaches Another fundamentally different approach is modelling by means of formulas and explicit relationships between the sensor data and the desired information. These approaches require the availability of physical background information or system behaviour in the form of a mathematical description. Such model-based approaches combine sensor data with background information, to yield a more precise result for the desired information. Some of the best-known examples include the Kalman filter (KF) for linear systems and the unscented Kalman filter (UKF), the extended Kalman filter (EKF) and particle filter (PF) for non-linear systems. Filter
Figure 4: Model-based approaches for embedded platforms
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