Edge Intelligence in Wearable Healthcare IoT: A Systematic Review

Approved

Relevance and Originality

The manuscript presents a review of the integration of edge intelligence within wearable healthcare IoT systems, which is a significant and rapidly developing area in smart healthcare research. The increasing demand for real-time monitoring and decentralized data processing has made edge-based healthcare architectures an important topic for both researchers and practitioners. The paper compiles literature from recent years and attempts to present a structured taxonomy of architectures, sensors, machine learning techniques, and communication frameworks used in wearable healthcare systems. While the paper provides a broad overview of the field, its contribution mainly lies in summarizing previously published research rather than introducing new theoretical or methodological innovations.

Methodology

The manuscript indicates that a systematic literature review approach was followed and that several academic databases were used to identify relevant research articles. The defined inclusion and exclusion criteria help ensure that the review remains focused on studies related to edge intelligence in wearable healthcare IoT. This approach contributes to the methodological credibility of the work. However, the review process would benefit from additional clarification regarding how the final set of studies was selected, including the number of records initially identified, filtered, and retained for analysis.

Validity and Reliability

The technical discussion presented in the manuscript demonstrates a solid understanding of the key components of wearable healthcare IoT systems, including sensing technologies, machine learning techniques, and distributed computing architectures. The discussion of technical limitations such as energy consumption, security vulnerabilities, and interoperability challenges adds balance to the analysis. These observations are consistent with issues frequently reported in the literature. Nevertheless, the reliability of the review could be enhanced by including more explicit comparisons between the performance outcomes reported across different studies.

Clarity and Structure

The manuscript is generally well structured, with sections arranged in a logical sequence that guides the reader from the introduction of the research problem to the discussion of future research directions. The use of tables to summarize architectural categories, sensor types, and AI methods improves the readability of the manuscript. However, certain sections contain dense textual explanations that could be condensed to improve clarity and maintain a more concise presentation.

Results and Analysis

The review effectively identifies several key trends shaping the development of wearable healthcare IoT systems, including the transition toward edge-based processing, the growing use of federated learning for privacy preservation, and the increasing adoption of multimodal sensing technologies. The manuscript also outlines several research gaps and challenges that remain unresolved in this field. While these observations are useful, the analytical discussion could be strengthened by providing clearer cross-study comparisons and more critical evaluation of the reviewed systems.