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Abstract In this work, we explore multiplex graph (networks with different types of edges) generation with deep generative models. We discuss some of the challenges associated with multiplex graph generation that make it a more difficult problem than traditional graph generation. We propose T
en GAN, the first neural network for multiplex graph generation, which greatly reduces the number of parameters required for multiplex graph generation. We also propose 3 different criteria for evaluating the quality of generated graphs: a graph-attribute-based, a classifier-based, and a tensor-based method. We evaluate its performance on 4 datasets and show that it generally performs better than other existing statistical multiplex graph generative models. We also adapt HGEN, an existing deep generative model for heterogeneous information networks, to work for multiplex graphs and show that our method generally performs better. -
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Abstract Background A robotic rehabilitation gym can be defined as multiple patients training with multiple robots or passive sensorized devices in a group setting. Recent work with such gyms has shown positive rehabilitation outcomes; furthermore, such gyms allow a single therapist to supervise more than one patient, increasing cost-effectiveness. To allow more effective multipatient supervision in future robotic rehabilitation gyms, we propose an automated system that could dynamically assign patients to different robots within a session in order to optimize rehabilitation outcome.
Methods As a first step toward implementing a practical patient-robot assignment system, we present a simplified mathematical model of a robotic rehabilitation gym. Mixed-integer nonlinear programming algorithms are used to find effective assignment and training solutions for multiple evaluation scenarios involving different numbers of patients and robots (5 patients and 5 robots, 6 patients and 5 robots, 5 patients and 7 robots), different training durations (7 or 12 time steps) and different complexity levels (whether different patients have different skill acquisition curves, whether robots have exit times associated with them). In all cases, the goal is to maximize total skill gain across all patients and skills within a session.
Results Analyses of variance across different scenarios show that disjunctive and time-indexed optimization models significantly outperform two baseline schedules: staying on one robot throughout a session and switching robots halfway through a session. The disjunctive model results in higher skill gain than the time-indexed model in the given scenarios, and the optimization duration increases as the number of patients, robots and time steps increases. Additionally, we discuss how different model simplifications (e.g., perfectly known and predictable patient skill level) could be addressed in the future and how such software may eventually be used in practice.
Conclusions Though it involves unrealistically simple scenarios, our study shows that intelligently moving patients between different rehabilitation robots can improve overall skill acquisition in a multi-patient multi-robot environment. While robotic rehabilitation gyms are not yet commonplace in clinical practice, prototypes of them already exist, and our study presents a way to use intelligent decision support to potentially enable more efficient delivery of technologically aided rehabilitation.
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Back injuries and other occupational injuries are common in workers who engage in long, arduous physical labor. The risk of these injuries could be reduced using assistive devices that automatically detect an object lifting motion and support the user while they perform the lift; however, such devices must be able to detect the lifting motion as it occurs. We thus developed a system to detect the start and end of a lift (performed as a stoop or squat) in real time based on pelvic angle and the distance between the user's hands and the user's center of mass. The measurements were input to an algorithm that first searches for hand-center distance peaks in a sliding window, then checks the pelvic displacement angle to verify lift occurrence. The approach was tested with 5 participants, who performed a total of 100 lifts of four different types. The times of actual lifts were determined by manual video annotation. The median time error (absolute difference between detected and actual occurrence time) for lifts that were not false negatives was 0.11 s; a lift was considered a false negative if it was not detected within two seconds of it actually occurring. Furthermore, 95% of lifts that were detected occurred within 0.28 s of actual occurrence. This shows that it is possible to reliably detect lifts in real time based on the pelvic displacement angle and the distance between the user's hands and their center of mass.more » « less
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Trunk exoskeletons are wearable devices that support wearers during physically demanding tasks by reducing biomechanical loads and increasing stability. In this paper, we present a prototype sensorized passive trunk exoskeleton, which includes five motion processing units (3-axis accelerometers and gyroscopes with onboard digital processing), four one-axis flex sensors along the exoskeletal spinal column, and two one-axis force sensors for measuring the interaction force between the wearer and exoskeleton. A pilot evaluation of the exoskeleton was conducted with two wearers, who performed multiple everyday tasks (sitting on a chair and standing up, walking in a straight line, picking up a box with a straight back, picking up a box with a bent back, bending forward while standing, bending laterally while standing) while wearing the exoskeleton. Illustrative examples of the results are presented as graphs. Finally, potential applications of the sensorized exoskeleton as the basis for a semi-active exoskeleton design or for audio/haptic feedback to guide the wearer are discussed.more » « less
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