A survey on the use of machine learning methods in context-aware middlewares for human activity recognition

Human activity recognition (HAR) essentially uses (past) sensor data or complex context information for inferring the activities a user performs in his daily tasks. HAR has been extensively studied using different paradigms, such as different reasoning mechanisms, including probabilistic, rule-based, statistical, logical reasoning, or the machine learning (ML) paradigm, to construct inference models to recognize or predict user activities. ML for HAR allows that activities can be recognized and even anticipated through the analysis of collected data from different sensors, with greater accuracy than the other paradigms. On the other hand, context-aware middlewares (CAMs) can efficiently integrate a large number of different devices and sensors. Moreover, they provide a programmable and auto-configurable infrastructure for streamline the design and construction of software solutions in scenarios where lots of sensors and data are their bases, such as ambient intelligence, smart cities, and e-health domains. In this way, the full integration of ML capabilities as services in CAMs can advance the development of software solutions in these domains when ML is necessary, specially for HAR, which is the basis for many scenarios in these domains. In this work, we present a survey for identifying the state-of-the-art in using ML for HAR in CAMs through a systematic literature review (SLR). In our SLR, we worked to answer four research questions: (i) what are the different types of context reasoners available in CAMs; (ii) what are the ML algorithms and methods used for generating models for context reasoning; (iii) which CAMs support data processing in real time; and (iv) what are the HAR scenarios usually tackled by the research works. In our analysis, we observed that, although ML offers viable approaches to construct inference models for HAR using different ML approaches, including batch learning, adaptive learning and data stream learning, there are yet some gaps and research challenges to be tackled, specially on the use of data stream learning considering concept drift on data, mechanisms for adapting the inference models, and further considering all of this as services in CAMs, specially for HAR.