You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
Human Activity Recognition (HAR) is a powerful tool for understanding human behaviour. Pervasive sensors, such as wearable devices, have an increasing market penetration and generate a tremendous amount of data. The myriad of available clinical and consumer-grade wearables generate a continuous time series of a person's daily physical exertion and rest. Applying HAR to the activity time series can provide new insights by enriching the feature set in health studies, and enhancing the personalisation and effectiveness of health, wellness, and fitness applications. The analyses of complex health behaviours such as sleep, traditionally require a time-consuming manual interpretation by experts. This manual work is necessary due to the erratic periodicity and persistent noisiness of human behaviour. In this paper, we present a robust automated human activity recognition algorithm, which we call RAHAR. We test our algorithm in the application area of sleep research by providing a novel framework for evaluating sleep quality and examining the correlation between the aforementioned and an individual's physical activity. Our results improve the state-of-the-art procedure in sleep research by 15 for area under ROC and by 30 for F1 score on average. However, application of RAHAR is not limited to sleep analysis and can be used for understanding other health problems such as obesity, diabetes, and cardiac diseases.

Cited Abstract(s):
[1]: Research on sensor-based activity recognition has, recently, made significant progress and is attracting growing attention in a number of disciplines and application domains. However, there is a lack of high-level overview on this topic that can inform related communities of the research state of the art. In this paper, we present a comprehensive survey to examine the development and current status of various aspects of sensor-based activity recognition. We first discuss the general rationale and distinctions of vision-based and sensor-based activity recognition. Then, we review the major approaches and methods associated with sensor-based activity monitoring, modeling, and recognition from which strengths and weaknesses of those approaches are highlighted. We make a primary distinction in this paper between data-driven and knowledge-driven approaches, and use this distinction to structure our survey. We also discuss some promising directions for future research.

[2]: The last 20 years have seen ever-increasing research activity in the field of human activity recognition. With activity recognition having considerably matured, so has the number of challenges in designing, implementing, and evaluating activity recognition systems. This tutorial aims to provide a comprehensive hands-on introduction for newcomers to the field of human activity recognition. It specifically focuses on activity recognition using on-body inertial sensors. We first discuss the key research challenges that human activity recognition shares with general pattern recognition and identify those challenges that are specific to human activity recognition. We then describe the concept of an Activity Recognition Chain (ARC) as a general-purpose framework for designing and evaluating activity recognition systems. We detail each component of the framework, provide references to related research, and introduce the best practice methods developed by the activity recognition research community. We conclude with the educational example problem of recognizing different hand gestures from inertial sensors attached to the upper and lower arm. We illustrate how each component of this framework can be implemented for this specific activity recognition problem and demonstrate how different implementations compare and how they impact overall recognition performance.

[3]: 

[4]: In this paper we address the problem of human activity recognition based only on acoustic modality. The ultimate goal is continuous acoustic monitoring of public places like parks and bus stops for detecting littering activities so that the people involved in such acts can be prompted to bin appropriately. We exploit the fact that when human interacts with objects, a characteristic sound is produced, and this sound can be used to recognize the activity. We propose a method based on perceptual features and ensemble of fuzzy rule-based one-class classifiers for activity recognition. The method is validated using real data and compared with support vector machine classifier. The results show that the classifier has very low false alarm rate and potentially well suited for incremental learning.

[5]: 

[6]: Wearable technologies are becoming the main interface between human and surrounding environment for a variety of context-aware and autonomous applications. Ubiquitous, small-size, and low-cost smartphones carried by everyone nowadays are equipped with a host of embedded sensors that provide groundbreaking opportunities to collect and use multimodal data in data-driven decision support systems. Simulation models are one of the most widely used decision support tools in project management that can highly benefit from the integration of contextual knowledge with the model design. In this paper, a discrete event simulation (DES) model of construction operations involving human activities is designed, enriched with wearable sensor data using smartphones, and validated. The model parameters are defined using 1) a data-driven activity recognition and 2) a static engineering estimation method for comparison. Results show that the output of the data-driven simulation model is in a closer agreement with the values observed in the real system.

[7]: The recently developed extended belief rule-based inference methodology (RIMER+) recognizes the need of modeling different types of information and uncertainty that usually coexist in real environments. A home setting with sensors located in different rooms and on different appliances can be considered as a particularly relevant example of such an environment, which brings a range of challenges for sensor- based activity recognition. Although RIMER+ has been designed as a generic decision model that could be applied in a wide range of situations, this paper discusses how this methodology can be adapted to recognize human activities using binary sensors within smart environments. The evaluation of RIMER+ against other state- of-the-art classifiers in terms of accuracy, efficiency and applicability was found to be significantly relevant, specially in situations of input data incompleteness, and it demonstrates the potential of this methodology and underpins the basis to develop further research on the topic. I. INTRODUCTION

[8]: Approaches and algorithms for activity recognition have recently made substantial progress due to advancements in pervasive and mobile computing, smart environments and ambient assisted living. Nevertheless, it is still difficult to achieve real-time continuous activity recognition as sensor data segmentation remains a challenge. This paper presents a novel approach to real-time sensor data segmentation for continuous activity recognition. Central to the approach is a dynamic segmentation model, based on the notion of varied time windows, which can shrink and expand the segmentation window size by using temporal information of sensor data and activities as well as the state of activity recognition. The paper first analyzes the characteristics of activities of daily living from which the segmentation model that is applicable to a wide range of activity recognition scenarios is motivated and developed. It then describes the working mechanism and relevant algorithms of the model in the context of knowledge-driven activity recognition based on ontologies. The presented approach has been implemented in a prototype system and evaluated in a number of experiments. Results have shown average recognition accuracy above 83 in all experiments for real time activity recognition, which proves the approach and the underlying model.

[9]:
Unlike vision-based HAR systems, sensor-based HAR technologies commonly deal with time series of state changes and or various parameter values collected from a wide range of sensors such as contact sensors, accelerometers, audio and motion detectors, etc. [1] and [2] present comprehensive reviews of sensor-based activity recognition literature. The most recent work in this domain includes knowledge-based inference [7] [3] , ensemble methods [4] [9] , data-driven approaches [6] [5] , and ontology-based techniques [8] .