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Advancements in robotics and AI have increased the demand for interactive robots in healthcare and assistive applications. However, ensuring safe and effective physical human-robot interactions (pHRIs) remains challenging due to the complexities of human motor communication and intent recognition. Traditional physics-based models struggle to capture the dynamic nature of human force interactions, limiting robotic adaptability. To address these limitations, neural networks (NNs) have been explored for force-movement intention prediction. While multi-layer perceptron (MLP) networks show potential, they struggle with temporal dependencies and generalization. Long Short-Term Memory (LSTM) networks effectively model sequential dependencies, while Convolutional Neural Networks (CNNs) enhance spatial feature extraction from human force data. Building on these strengths, this study introduces a hybrid LSTM-CNN framework to improve force-movement intention prediction, increasing accuracy from 69% to 86% through effective denoising and advanced architectures. The combined CNN-LSTM network proved particularly effective in handling individualized force-velocity relationships and presents a generalizable model paving the way for more adaptive strategies in robot guidance. These findings highlight the importance of integrating spatial and temporal modeling to enhance robot precision, responsiveness, and human-robot collaboration. Index Terms —- Physical Human-Robot Interaction, Intention Detection, Machine Learning, Long-Short Term Memory (LSTM) 

This work challenges the common assumption in physical human-robot interaction (pHRI) that the movement intention of a human user can be simply modeled with dynamic equations relating forces to movements, regardless of the user. Studies in physical human-human interaction (pHHI) suggest that interaction forces carry sophisticated information that reveals motor skills and roles in the partnership and even promotes adaptation and motor learning. In this view, simple force-displacement equations often used in pHRI studies may not be sufficient. To test this, this work measured and analyzed the interaction forces (F) between two humans as the leader guided the blindfolded follower on a randomly chosen path. The actual trajectory of the follower was transformed to the velocity commands (V) that would allow a hypothetical robot follower to track the same trajectory. Then, possible analytical relationships between F and V were obtained using neural network training. Results suggest that while F helps predict V, the relationship is not straightforward, that seemingly irrelevant components of F may be important, that force-velocity relationships are unique to each human follower, and that human neural control of movement may affect the prediction of the movement intent. It is suggested that user-specific, stereotype-free controllers may more accurately decode human intent in pHRI. 

Abstract. In late summer 2019 and 2020 bottom waters in southern Cape Cod Bay (CCB) became depleted of dissolved oxygen (DO), with documented benthicmortality in both years. Hypoxic conditions formed in relatively shallow water where the strong seasonal thermocline intersected the sea floor, bothlimiting vertical mixing and concentrating biological oxygen demand (BOD) over a very thin bottom boundary layer. In both 2019 and 2020, anomalouslyhigh sub-surface phytoplankton blooms were observed, and the biomass from these blooms provided the fuel to deplete sub-pycnocline waters of DO. Theincreased chlorophyll fluorescence was accompanied by a corresponding decrease in sub-pycnocline nutrients, suggesting that prior to 2019 physicalconditions were unfavorable for the utilization of these deep nutrients by the late-summer phytoplankton community. It is hypothesized thatsignificant alteration of physical conditions in CCB during late summer, which is the result of regional climate change, has favored the recentincrease in sub-surface phytoplankton production. These changes include rapidly warming waters and significant shifts in summer wind direction, bothof which impact the intensity and vertical distribution of thermal stratification and vertical mixing within the water column. These changes inwater column structure are not only more susceptible to hypoxia but also have significant implications for phytoplankton dynamics, potentiallyallowing for intense late-summer blooms of Karenia mikimotoi, a species new to the area. K. mikimotoi had not been detected in CCBor adjacent waters prior to 2017; however, increasing cell densities have been reported in subsequent years, consistent with a rapidly changingecosystem.