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Abstract PurposeMeasurable results of efforts to teach empathy to engineering students are sparse and somewhat mixed. This study’s objectives are (O1) to understand how empathy training affects students’ professional development relative to other educational experiences, (O2) to track empathy changes due to training over multiple years, and (O3) to understand how and what students learn in empathy training environments. MethodsStudents in a multiple-semester empathy course completed surveys ranking the career development impact of the empathy program against other college experiences (O1), rating learning of specific empathy skills (O2), and ranking program elements’ impact on empathy skills (O3). Intervention and control groups completed the Interpersonal Reactivity Index and Jefferson Scale of Empathy at four time points (O2). Cohort students participated in post-program interviews (O1, O3). ResultsO1: Empathy training impacted career development more than several typical college activities but less than courses in major. O2: Students reported gains in four taught empathy skills. Cohort students showed significant increases in the Jefferson Scale while the control group did not. There were no significant changes in Interpersonal Reactivity Index scores. O3: interactive exercises had a significant effect on students’ learning all empathy skills while interactions with people with disabilities had significant effect on learning to encounter others with genuineness. Students valued building a safe in-class community facilitating their success in experiential environments. ConclusionsThis study highlights empathy skills’ importance in engineering students’ development, shows gains in empathy with training, and uncovers key factors in students’ learning experience that can be incorporated into engineering curricula. 

Abstract This paper explores the use of Gaussian process regression for system identification in control engineering. It introduces two novel approaches that utilize the data from a measured global system error. The paper demonstrates these approaches by identifying a simulated system with three subsystems, a one degree of freedom mass with two antagonist muscles. The first approach uses this whole-system error data alone, achieving accuracy on the same order of magnitude as subsystem-specific data ( $9.28\pm 0.87\text{N\hspace{0.17em}}\text{vs.\hspace{0.17em}}6.96\pm 0.32\text{N}$of total model errors). This is significant, as it shows that the same data set can be used to identify unique subsystems, as opposed to requiring a set of data descriptive of only a single subsystem. The second approach demonstrated in this paper mixes traditional subsystem-specific data with the whole system error data, achieving up to 98.71% model improvement. 

Individuals with paralyzed limbs due to spinal cord injuries lack the ability to perform the reaching motions necessary to every day life. Functional electrical stimulation (FES) is a promising technology for restoring reaching movements to these individuals by reanimating their paralyzed muscles. We have proposed using a quasi-static model-based control strategy to achieve reaching controlled by FES. This method uses a series of static positions to connect the starting wrist position to the goal. As a first step to implementing this controller, we have completed a simulated study using a MATLAB based dynamic model of the arm in order to determine the suitable parameters for the quasi-static controller. The selected distance between static positions in the path was 6 cm, and the amount of time between switching target positions was 1.3 s. The final controller can complete reaches of over 30 cm with a median accuracy of 6.8 cm.