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Sensing technology has significantly advanced in automating systems that reflect human movement, particularly in robotics and healthcare, where it is used to automatically detect target movements. This study develops a method to automatically count exercise repetitions by analyzing IMU signals, with a focus on a universal exercise repetition counting task that counts all types of exercise movements, including novel exercises not seen during training, using a single model. Since peak patterns can vary significantly between different exercises as well as between individuals performing the same exercise, the model needs to learn a complex embedding space of sensor data to generalize effectively. To address this challenge,we propose a repetition counting technique utilizing a deep metric-based few-shot learning approach, designed to handle both existing and novel exercises. By redefining the counting task as a few-shot classification problem, the method is capable of detecting peak repetition patterns in exercises not seen during training. The approach employs a Siamese network with triplet loss, optimizing the embedding space to distinguish between peak and non-peak frames. Evaluation results demonstrate the effectiveness of the proposed approach, showing an 86.8% probability of accurately counting ten or more repetitions within a single set across 28 different exercises. This performance highlights the model's ability to generalize across various exercise types, including those not present in the training data. Such robustness and adaptability make the system a strong candidate for real-time implementation in fitness and healthcare applications.

Appropriate medication dosages in the intensive care unit (ICU) are critical for patient survival. Heparin, used to treat thrombosis and inhibit blood clotting in the ICU, requires careful administration due to its complexity and sensitivity to various factors, including patient clinical characteristics, underlying medical conditions, and potential drug interactions. Incorrect dosing can lead to severe complications such as strokes or excessive bleeding. To address these challenges, this study proposes a reinforcement learning (RL)-based personalized optimal heparin dosing policy that guides dosing decisions reliably within the therapeutic range based on individual patient conditions. A batch-constrained policy was implemented to minimize out-of-distribution errors in an offline RL environment and effectively integrate RL with existing clinician policies. The policy's effectiveness was evaluated using weighted importance sampling, an off-policy evaluation method, and the relationship between state representations and Q-values was explored using t-SNE. Both quantitative and qualitative analyses were conducted using the Medical Information Mart for Intensive Care III (MIMIC-III) database, demonstrating the efficacy of the proposed RL-based medication policy. Leveraging advanced machine learning techniques and extensive clinical data, this research enhances heparin administration practices and establishes a precedent for the development of sophisticated decision-support tools in medicine.

Accurate diagnosis of individual patient conditions and appropriate medication dosing strategies are core elements of personalized medical decision-making processes. This therapeutic procedure, which entails recursively assessing the patient's condition and administering suitable medications, can effectively be modeled as a reinforcement learning (RL) problem. Crucially, the success of RL in this context depends on the establishment of a well-defined reward function that accurately represents the optimal treatment strategy. However, defining the learning direction in RL with only a limited set of explicit indicators complicates the task due to the inherent complexity of the required domain knowledge. This approach may also increase the likelihood that the RL policy does not adequately reflect the clinician's treatment intentions, which are determined by considering various situations and indicators. In this study, we focus on developing a reward function that reflects the clinician's intentions and introduce Offline Model-based Guided Reward Learning (OMG-RL), which performs offline inverse reinforcement learning (IRL) aligned with the offline RL environment. Through OMG-RL, we learn a parameterized reward function that includes the expert's intentions from limited data, thereby enhancing the agent's policy. We validate the proposed approach on the heparin dosing task. The results demonstrate that policy learning through OMG-RL is meaningful and confirm that the learned policy is positively reinforced in terms of activated partial thromboplastin time (aPTT), a key indicator for monitoring the effects of heparin. This approach can be broadly utilized not only for the heparin dosing problem but also for RL-based medication dosing tasks in general.