Draft:PhysiologicalMeasurement

• Comment: Please prorperly disclose your WP:COI with the subject. Sulfurboy (talk) 08:40, 23 July 2026 (UTC)

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Physiological Measurement (PMea) is a monthly peer-reviewed journal published by the Institute of Physics Publishing, on behalf of the Institute of Physics and Engineering in Medicine (IPEM) covering research on sensing, assessing, visualizing, modelling, classifying, predicting, and controlling physiological functions in clinical research and practice. Particular emphasis is put on the development of state-of-the-art methods such as artificial intelligence (AI) and machine learning algorithms, novel applications, and rigorous large-scale validation of existing methods. PMea provides a well-balanced venue to bring together physiologists, clinicians, engineers, computer scientists, and data scientists to support the development and translation of novel ideas and solutions to promote human health (both physical and mental).

PMea supports various types of publications and welcomes both methodological and application-oriented manuscripts, across the following list of topic areas. This list is not meant to be exhaustive, but rather illustrates the breadth of topics covered by PMEA.

Methodological track:

Physiological signal processing, artificial intelligence (AI), and machine learning methods for time series—such as ECG, EEG, EMG, MEG, PPG, blood pressure, intracranial pressure, blood flow, blood flow velocity, BCG, IMU, imaging, radar, voice, etc.—separately or in an integrated way. Advanced, physics-based measurement techniques: electrical, bioimpedance, optical, magnetic, and acoustic. Multimodal machine learning, data integration and understanding, e.g., clinical notes and physiological signal integration. Predictive models using data from electronic health records and medical devices that measure physiologic data in various clinical settings. Hardware, software, and predictive models for wearable, ingestible, video, and radar-based human health/disease monitoring. Physiological modelling, simulation, model identification, and control, using both empirical and physics-based models. Physics- and model-based machine learning. Physiological measurement standards and guidelines. Regulatory sciences and practices related to both traditional medical devices and software as a medical device. Ethical issues such as privacy, biases and fairness in the use of measurement and AI technologies to assess physiological functions and make decisions. Application track:

Monitoring patient behaviour in hospitals, clinics, home, and ambulatory settings. Diagnostics of cardiovascular, neurological, pulmonological, immunological, and infectious diseases. Brain health, including neurodegenerative diseases and psychiatry. Benchmarking existing algorithms. Mobile health. Sleep and circadian rhythms. Maternal, foetal and child health. Physiological measurement in low-resource and extreme environments. Sports medicine, personal fitness tracking and wellness monitoring. Workforce performance monitoring. The effect of the environment on physiology. PMEA will consider all types of review articles (topical reviews, roadmaps, editorials, and perspectives) within the above scope. An invitation to contribute is not necessary but authors are encouraged to have a pre-submission discussion with an editorial board member before undertaking a large endeavour to develop a review article.

The journal encourages submissions from a diverse range of research teams and authors, particularly from the global south, as well as citing authors from these regions.

PMea is an interdisciplinary journal. Authors of each article are therefore asked to ensure that at least the title and abstract of their article are understandable to researchers in other disciplines and to supply suitable keywords as a concise method of describing its general research topic, in both clinical and scientific terms. The editor-in-chief is Xiao Hu (Emory University, USA).