UniMiB SHAR: a new dataset for human activity recognition using acceleration data from smartphones (1611.07688v5)

Abstract: Smartphones, smartwatches, fitness trackers, and ad-hoc wearable devices are being increasingly used to monitor human activities. Data acquired by the hosted sensors are usually processed by machine-learning-based algorithms to classify human activities. The success of those algorithms mostly depends on the availability of training (labeled) data that, if made publicly available, would allow researchers to make objective comparisons between techniques. Nowadays, publicly available data sets are few, often contain samples from subjects with too similar characteristics, and very often lack of specific information so that is not possible to select subsets of samples according to specific criteria. In this article, we present a new dataset of acceleration samples acquired with an Android smartphone designed for human activity recognition and fall detection. The dataset includes 11,771 samples of both human activities and falls performed by 30 subjects of ages ranging from 18 to 60 years. Samples are divided in 17 fine grained classes grouped in two coarse grained classes: one containing samples of 9 types of activities of daily living (ADL) and the other containing samples of 8 types of falls. The dataset has been stored to include all the information useful to select samples according to different criteria, such as the type of ADL, the age, the gender, and so on. Finally, the dataset has been benchmarked with four different classifiers and with two different feature vectors. We evaluated four different classification tasks: fall vs no fall, 9 activities, 8 falls, 17 activities and falls. For each classification task we performed a subject-dependent and independent evaluation. The major findings of the evaluation are the following: i) it is more difficult to distinguish between types of falls than types of activities; ii) subject-dependent evaluation outperforms the subject-independent one

Evaluating Declarative Disclosures of Potential Conflicts of Interest in Academic Submissions

The paper presented focuses on the ethical standards and practices surrounding the declaration of potential conflicts of interest by authors in academic submissions. Specifically, it examines a submission disclosing the authors' affirmation of no conflicting interests. The submission includes standard procedural elements such as authors' names, date, and location, but notably asserts the absence of any conflicts.

This paper is highly relevant to the academic and research community, emphasizing the importance of transparency and integrity in scholarly publications. The explicit declaration of "no conflict of interest" in the submission suggests an adherence to ethical guidelines and reflects the growing trend toward increased accountability in research practices. Such declarations are pivotal as they influence peer review processes and the broader perception of research credibility.

While the document under review might seem procedural, it indirectly highlights crucial issues in research integrity, including the mechanisms for verifying such declarations and the potential implications of undisclosed conflicts. The paper implicitly prompts a critical discourse on the efficacy of current compliance measures in identifying and managing conflicts of interest.

Beyond reiterating standard ethical practice, the paper raises questions regarding the sufficiency of mere declarations. It underscores the need for a robust framework to assess and verify declared interests to prevent any compromising of research objectivity. Future developments could include implementing more comprehensive guidelines and compliance checks to ensure that declarations are not only made but substantiated through rigorous vetting processes.

In theoretical and practical terms, ensuring declarations are accurate and truthful would contribute significantly to the trustworthiness of scientific research. Additionally, it could set a precedent for emerging policies that mandate proactive disclosures accompanied by supporting documentation or third-party audits.

The paper, by focusing on a procedural element common across many academic disciplines, offers insights into how ethical declarations are indispensable in maintaining the integrity and reliability of academic research. Researchers and institutions alike should be encouraged to refine their policies to adapt to evolving standards of transparency and accountability in scholarly pursuits.