No abstract

Nowadays, the advancements of wearable consumer devices have become a predominant role in healthcare gadgets. There is always a demand to obtain robust recognition of heterogeneous human activities in complicated IoT environments. The knowledge attained using these recognition models will be then combined with healthcare applications. In this way, the paper proposed a novel deep learning framework to recognize heterogeneous human activities using multimodal sensor data. The proposed framework is composed of four phases: employing dataset and processing, implementation of deep learning model, performance analysis, and application development. The paper utilized the recent KU-HAR database with eighteen different activities of 90 individuals. After preprocessing, the hybrid model integrating Extreme Learning Machine (ELM) and Gated Recurrent Unit (GRU) architecture is used. An attention mechanism is then included for further enhancing the robustness of human activity recognition in the IoT environment. Finally, the performance of the proposed model is evaluated and comparatively analyzed with conventional CNN, LSTM, GRU, ELM, Transformer and Ensemble algorithms. To the end, an application is developed using the Qt framework which can be deployed on any consumer device. In this way, the research sheds light on monitoring the activities of critical patients by healthcare professionals remotely. The proposed ELM-GRUaM model achieved supreme performance in recognizing multimodal human activities with an overall accuracy of 96.71% as compared with existing models.

This paper examines the use of visual methods in studies of inclusive education. Visual methods have been applied to/with pupils with special educational needs (SEN) in the past but the application has tended to be outside rather than inside schools. We argue that understanding contextual reflexivity is important if visual methods are to be successfully adapted to meet the needs of inclusive research. A case study is used to provide an insight into the strengths, weaknesses and difficulties of mixed method visual research. The final section of the paper explores potential applications, strategies and techniques for including visual methods in inclusivity research.

Wireless sensor networks have become incredibly popular due to the Internet of Things' (IoT) rapid development. IoT routing is the basis for the efficient operation of the perception-layer network. As a popular type of machine learning, reinforcement learning techniques have gained significant attention due to their successful application in the field of network communication. In the traditional Routing Protocol for lowpower and Lossy Networks (RPL) protocol, to solve the fairness of control message transmission between IoT terminals, a fair broadcast suppression mechanism, or Drizzle algorithm, is usually used, but the Drizzle algorithm cannot allocate priority. Moreover, the Drizzle algorithm keeps changing its redundant constant k value but never converges to the optimal value of k. To address this problem, this paper uses a combination based on reinforcement learning (RL) and trickle timer. This paper proposes an RL Intelligent Adaptive Trickle-Timer Algorithm (RLATT) for routing optimization of the IoT awareness layer. RLATT has triple-optimized the trickle timer algorithm. To verify the algorithm's effectiveness, the simulation is carried out on Contiki operating system and compared with the standard trickling timer and Drizzle algorithm. Experiments show that the proposed algorithm performs better in terms of packet delivery ratio (PDR), power consumption, network convergence time, and total control cost ratio.

The ethical regulation of social research in the UK has been steadily increasing over the last decade or so and comprises a form of audit to which all researchers in Higher Education are subject. Concerns have been raised by social researchers using visual methods that such ethical scrutiny and regulation will place severe limitations on visual research developments and practice. This paper draws on a qualitative study of social researchers using visual methods in the UK. The study explored their views, the challenges they face and the practices they adopt in relation to processes of ethical review. Researchers reflected on the variety of strategies they adopted for managing the ethical approval process in relation to visual research. For some this meant explicitly ‘making the case’ for undertaking visual research, notwithstanding the ethical challenges, while for others it involved ‘normalising’ visual methods in ways which delimited the possible ethical dilemmas of visual approaches. Researchers only rarely identified significant barriers to conducting visual research from ethical approval processes, though skilful negotiation and actively managing the system was often required. Nevertheless, the climate of increasing ethical regulation is identified as having a potential detrimental effect on visual research practice and development, in some instances leading to subtle but significant self-censorship in the dissemination of findings.