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The introduction and ever-growing size of the transformer deep-learning architecture have had a tremendous impact not only in the field of natural language processing but also in other fields. The transformer-based language models have contributed to a renewed interest in commonsense knowledge due to the abilities of deep learning models. Recent literature has focused on analyzing commonsense embedded within the pre-trained parameters of these models and embedding missing commonsense using knowledge graphs and fine-tuning. We base our current work on the empirically proven language understanding of very large transformer-based language models to expand a limited commonsense knowledge graph, initially generated only on visual data. The few-shot-prompted pre-trained language models can learn the context of an initial knowledge graph with less bias than language models fine-tuned on a large initial corpus. It is also shown that these models can offer new concepts that are added to the vision-based knowledge graph. This two-step approach of vision mining and language model prompts results in the auto-generation of a commonsense knowledge graph well equipped with physical commonsense, which is human commonsense gained by interacting with the physical world. To prompt the language models, we adapted the chain-of-thought method of prompting. To the best of our knowledge, it is a novel contribution to the domain of the generation of commonsense knowledge, which can result in a five-fold cost reduction compared to the state-of-the-art. Another contribution is assigning fuzzy linguistic terms to the generated triples. The process is end to end in the context of knowledge graphs. It means the triples are verbalized to natural language, and after being processed, the results are converted back to triples and added to the commonsense knowledge graph.

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Biomedical applications in general, and health monitoring in particular, extensively involve on-body as well as implantable wireless communications devices to enable viable end-user solutions. While technologies to wirelessly transmit data from implanted devices have already been reported, they fall short of being able to support the needs of emerging next-generation biomedical applications. In order to translate state-of-theart wireless technologies into solutions fitting body area network applications (BANs), a key challenge to overcome is the strictly limited power budget. This paper attempts to review design challenges and proposes a viable solution for wireless telemetry to meet the targets for next-generation BANs.

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