## 1 Introduction  
The development and large scale deployment of IoT monitoring devices has made significant amounts of data available, which may be used for improving the care of elders. In this context, the identification of daily living activities out of monitoring data is very important for understanding the elders behaviour and assessing deviations from daily routines, which may signal illness progression and timely planning of intervention processes. For example, in the project ReMIND [1] the enhancement of the quality of life of the elders with mild neurocognitive impairments is considered, and one major challenge addressed by the ReMIND project is the stimulating of the physical activity through music using James robots. Therefore, the challenge of the daily living activities (DLAs) classification in real-time using low computational resources is critical as those elders might suffer unexpected falls caused by their critical health condition or sudden switches from one DLA to another DLA caused by memory problems. One way to increase the classification speed is to reduce the number of features of the data samples; however, that challenge is even more complex when the data is imbalanced and heterogeneous for each monitored person. In [2] the reduction of the number of features was addressed by proposing a novel bio-inspired algorithm. However, in that approach, the experimental DLAs datasets were balanced and normalized prior to the application of the feature selection. The challenges of the aging population [3] have been addressed by various types of information and communication technology (ICT) systems in the last years, but few of them evaluated the challengesrelated to the acceptance and to the usability of the ambient assisted living (AAL) systems [4]. Those challenges are even more complex in particular cases, such as the ones where the elders have symptoms of dementia [5] or symptoms of Parkinson's disease [6]. The recent developments for the internet of things (IoT) [7] and wearable computing led to the development of various types of health monitoring systems, which use data collected from various sources [8]. Moreover, these systems enable the continuous monitoring of elders; however, there are still challenges that must be considered, such as the need for high processing power and the low latency. This challenge is even more complex in the case of imbalanced data with many features and samples. The classification of the DLAs using data that is generated by wearable devices has various applications in healthcare, such as falls detection [9], falls prediction [10], the classification of the activities of the elders that live alone [11], the classification of the DLAs of the elders that live in free-living conditions [12], the detection of anomalies in DLAs [13] and behaviour recognition for robust healthcare [14]. In the literature there are many datasets generated by sensors [15], which are used in the prediction of DLAs; however, the identification of the most appropriate techniques that return the best results, in various contexts, is still challenging due to the many dimensions that must be considered, such as the selection of the features, the balancing of the samples and the selection of the classifiers applied in the classification of the DLAs. Real time data can be collected using various types of monitoring sensors, such as gyroscope sensors, orientation sensors, accelerometer sensors, and pressure sensors. The data generated by monitoring sensors are very useful in research, especially in the health research domain, as a thorough and complex analysis of this data can reveal a great deal of useful information, such as patterns in the daily living behaviour of the persons that are monitored, which might indicate particular health conditions, adherence to a particular type of routine, and anomalies in the normal daily behaviour.