Key result
Novel zero-dimensional feature extraction on skeleton sequences enables medical action classification for remote patient monitoring.
Why the study?
Remote patient assessment requires medical action recognition and biomarker identification, but each action often requires different features and portable, accurate data collection.
The proposed zero-dimensional feature extraction method using human pose estimation offers a novel approach for explainable AI in remote patient monitoring and medical action recognition.
May aid remote monitoring with explainable biomarkers; leaves open prospective clinical validation.
Medical action recognition is an increasingly necessary task as healthcare has shifted towards digital and more remote methods of patient monitoring. To remotely assess a patient with computer-aided diagnosis it is first necessary to identify different actions. In tandem with action recognition, this remote monitoring also requires biomarker identification. This allows meaningful features to be extracted from the patient’s actions, however, each action often requires a different set of features to be monitored. Finally, the mode of data collection requires both portability and accuracy to be used in the healthcare industry. To combine each of these, one of the best solutions for condition-related action recognition is using videos in combination with human pose estimation to create spatio-temporal skeleton data for the patients which allows further analysis to be fast and accurate. By first using manual feature extraction on the skeleton sequences, it is possible to utilise machine learning to both classify different actions and identify latent features as the biomarkers that distinguish each of the action classes. Thus, this new method proposes a new zero-dimensional feature extraction method to classify skeleton sequences and extract medically meaningful, explainable, and objective biomarkers that can be used for the diagnosis and monitoring of patients in a remote setting.
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Armstrong et al. (2023) studied Remote patient monitoring. Zero-dimensional feature extraction method on skeleton sequences was evaluated on Classification of skeleton sequences and extraction of biomarkers. A new zero-dimensional feature extraction method on skeleton sequences enables the classification of medical actions and extraction of explainable biomarkers for remote patient monitoring.
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