An apparatus comprising: means for generating statistical channel correlation matrices at each of a plurality of time instances from a plurality of multi-dimensional arrays or tensors, wherein each of the plurality of arrays or tensors describes a channel impulse or frequency response of a multiple-input, multi-output wireless transmission system at a respective time instance; means for eigen-decomposing said correlation matrices; means for determining a signal subspace extent at each time instance by distinguishing between a signal subspace and a noise subspace; means for extracting eigenvectors from the eigen-decomposed correlation matrices at each time instance for at least the determined signal subspace; means for generating signal subspace differential unitarity measures using at least the signal subspace eigenvectors, wherein generating signal subspace differential unitarity measures comprises generating an indication of changes in unitarity of eigenbases with respect to previous corresponding eigenbases consisting of the signal subspace eigenvectors; and means for training a machine learning model for generating an output indicative of presence or activity within a defined physical space using at least the determined signal subspace extents and the signal subspace eigenvectors and/or means for generating an output indicative of presence or activity within a defined physical space based on at least the determined signal subspace extents and said signal subspace eigenvectors; wherein: generating the output indicative of presence or activity based on at least the signal subspace eigenvectors comprises generating the output dependent on the signal subspace differential unitarity measures and/or training the machine learning model for generating the output indicative of presence or activity within a defined physical space further comprises training the machine learning module using the determined signal subspace differential unitarity measures. means for generating statistical channel correlation matrices at each of a plurality of time instances from a plurality of multi-dimensional arrays or tensors, wherein each of the plurality of arrays or tensors describes a channel impulse or frequency response of a multiple-input, multi-output wireless transmission system at a respective time instance; means for eigen-decomposing said correlation matrices; means for determining a signal subspace extent at each time instance by distinguishing between a signal subspace and a noise subspace; means for extracting eigenvectors from the eigen-decomposed correlation matrices at each time instance for at least the determined signal subspace; means for generating signal subspace differential unitarity measures using at least the signal subspace eigenvectors, wherein generating signal subspace differential unitarity measures comprises generating an indication of changes in unitarity of eigenbases with respect to previous corresponding eigenbases consisting of the signal subspace eigenvectors; and means for training a machine learning model for generating an output indicative of presence or activity within a defined physical space using at least the determined signal subspace extents and the signal subspace eigenvectors and/or means for generating an output indicative of presence or activity within a defined physical space based on at least the determined signal subspace extents and said signal subspace eigenvectors; wherein: generating the output indicative of presence or activity based on at least the signal subspace eigenvectors comprises generating the output dependent on the signal subspace differential unitarity measures and/or training the machine learning model for generating the output indicative of presence or activity within a defined physical space further comprises training the machine learning module using the determined signal subspace differential unitarity measures.