## 2 Basic Principles
### Challenges  
Besides the numerous successful applications of machine-learning methods in human activity recognition, there are still topics that have yet to be investigated or at least, have only been partially addressed. In general, these challenges can be divided into source-related problems and methodological problems, which are presented in the following subsections. The first source-related problem deals with the fact that related works pursue different aspects of human activity recognition and rely on their own data acquisition, which depends on the activities the authors focus on. The use of different datasets with varying activities of different scales constitutes a major problem, as the conditions for comparability are simply not provided, e.g., [40; 41; 42; 48; 50; 53; 57; 59] used coarse-grained activities in their investigations, whereas [26; 46] used fine-grained activities as a basis for their works. This is especially problematic since the movements are linked to weaker micro-Doppler signatures in terms of power for fine-grained activities. Another problem is that many activities in both coarse-grained and fine-grained classes have a certain similarity, which has been proven, e.g., by [40], where data collected from coarse-grained activities were used for an SVM-based binary classification problem, and activities like punching were confused with running. Among other factors, every activity has a unique micro-Doppler signature, so machine learning-based classification models are trained to distinguish between the specific activities but not the transitions between them, which leads to performance losses, especially in online applications. Human activities can be broadly classified into two main categories: coarse-grained and fine-grained activities. Given constant configurations regarding data acquisition, this leads to different magnitudes and distributions of local variations, which can lead to different classification accuracies. As humans have individual physical properties due to genetics, age, sex, fitness, disabilities, consequences of illnesses or surgeries, etc., which change over time, datasets will also have variances in the amplitude or time domain, which leads to individual, temporal micro-Doppler signatures. In general, micro-Doppler signatures contain information on a person's activity characteristics. Besides the difficulties mentioned above, the complexity of the classification task is severely affected by the number of subjects when the classification is not broken down into subordinate, composite classification tasks based on datasets for each individual. This problem is exacerbated by different activities being performed simultaneously. Many human activities consist of sequential, subdivided activities, e.g., lifting a blanket, rotating from a horizontal into a sitting position, and standing up together connote the wake-up process. As the whole sequence is required to form the dataset for that specific activity, segmentation plays an important role in data preprocessing. As single activities lead to similar datasets for each repetition, the complexity of the classification task is increased when the datasets are collected from concurrent activities. Signatures containing smeared patterns lead to datasets with ambiguous characteristics and high variance. Models for classification problems rely on large amounts of data for training and validation, which require consistent annotations. While in experimental conditions this is not the case, data collections from public sources for an adaptive online application have to be labeled. Due to clutter, the data quality is strongly degraded by the presence of nearby objects that reflect fractions of the emitted power to the receiver through multipath propagation. For mitigation, environmental data are collected and used for preprocessing. In mobile applications, this is a crucial topic, as the surroundings do not remain constant.