Decoding Intent With Control Theory: Comparing Muscle Versus Manual Interface Performance

Effect of Handedness on Learned Controllers and Sensorimotor Noise During Trajectory-Tracking

In human-in-the-loop control systems, operators can learn to manually control dynamic machines with either hand using a combination of reactive (feedback) and predictive (feedforward) control. This article studies the effect of handedness on learned controllers and performance during a trajectory-tracking task. In an experiment with 18 participants, subjects perform an assay of unimanual trajectory-tracking and disturbance-rejection tasks through second-order machine dynamics, first with one hand then the other. To assess how hand preference (or dominance) affects learned controllers, we extend, validate, and apply a nonparametric modeling method to estimate the concurrent feedback and feedforward controllers. We find that performance improves because feedback adapts, regardless of the hand used. We do not detect statistically significant differences in performance or learned controllers between hands. Adaptation to reject disturbances arising exogenously (i.e., applied by the experimenter) and endogenously (i.e., generated by sensorimotor noise) explains observed performance improvements.

Towards Richer Assisted Living Environments

This paper describes an ongoing research project which explores the design and use of inexpensive robotics, artificial intelligence techniques, and human-computer interaction methods, to enrich assisted living environments. Such environments provide help to the inhabitants of a home or office, assisting them to perform daily activities, helping them to socialize and interact with others, and to provide enhanced levels of security and safeness. We present the development of an inexpensive robotic solution to help people with disabilities and/or older adults to perform their daily activities. It can be used as a remote controlled surveillance system, and also as a personal assistant. It is able to recognize each inhabitant, his/her emotions, and detect abnormal situations such as falls and health problems. The whole system is designed to operate solely within a local network and special attention is given to the privacy and data protection of the users.