Ergodicity Reveals Assistance and Learning from Physical Human-Robot Interaction.

This paper applies information theoretic principles to the investigation of physical human-robot interaction. Drawing from the study of human perception and neural encoding, information theoretic approaches offer a perspective that enables quantitatively interpreting the body as an information channel, and bodily motion as an information-carrying signal. We show that ergodicity, which can be interpreted as the degree to which a trajectory encodes information about a task, correctly predicts changes due to reduction of a person's existing deficit or the addition of algorithmic assistance. The measure also captures changes from training with robotic assistance. Other common measures for assessment failed to capture at least one of these effects. This information-based interpretation of motion can be applied broadly, in the evaluation and design of human-machine interactions, in learning by demonstration paradigms, or in human motion analysis.

The role of shoe design on the prediction of free torque at the shoe-surface interface using pressure insole technology.

The goal of the current study was to expand on previous work to validate the use of pressure insole technology in conjunction with linear regression models to predict the free torque at the shoe-surface interface that is generated while wearing different athletic shoes. Three distinctly different shoe designs were utilised. The stiffness of each shoe was determined with a material's testing machine. Six participants wore each shoe that was fitted with an insole pressure measurement device and performed rotation trials on an embedded force plate. A pressure sensor mask was constructed from those sensors having a high linear correlation with free torque values. Linear regression models were developed to predict free torques from these pressure sensor data. The models were able to accurately predict their own free torque well (RMS error 3.72 ± 0.74 Nm), but not that of the other shoes (RMS error 10.43 ± 3.79 Nm). Models performing self-prediction were also able to measure differences in shoe stiffness. The results of the current study showed the need for participant-shoe specific linear regression models to insure high prediction accuracy of free torques from pressure sensor data during isolated internal and external rotations of the body with respect to a planted foot.

Leadership and organization development in health-care: lessons from the Cleveland Clinic.

Leaders in health-care today are faced with a wide array of complex issues. This chapter describes an innovative physician leadership development program at the Cleveland Clinic intended to enhance the leadership capacities of individuals and the organization. Propositions regarding the program's impact on organizational innovation, organizational commitment, social capital, and the human element of physician practice are offered for future examination.