Challenges in Sensor-based Human Activity Recognition and a Comparative Analysis of Benchmark Datasets: A Review

Human Activity Recognition using embedded sensors has lately made renowned development and is drawing growing attention in numerous application domains including machine learning, pattern recognition, context awareness, and human-centric sensing. Due to the lacking of a prominent analysis of this topic that can acquaint concomitant communities of the research avant-garde, there are still vital perspectives that, if pleaded, would create a vital turn in the way of interaction among people and mobile devices. In this paper, we have presented a comprehensive survey along with the prevailing state of various challenges of human activity recognition based on wearable, environmental, and smartphone sensors. Firstly, we have shown numerous factors to be considered for the data pre-processing part regarding noise filtering and segmentation methods. Besides, we have made a list of sensing devices, sensors, and applications that can be used for collecting activity data along with a discussion on sensor position and requirements. Moreover, we have made a comprehensive analysis of some benchmark datasets, which includes information about sensors, attributes, activity classes, etc. Finally, we have shown an analysis of activity recognition approaches on some of the benchmark datasets based on existing works.