Haptic interface research benefits from accurate human arm models for control and system design. The literature contains many human arm dynamic models but lacks detailed variability analyses. Without accurate measurements, variability is modeled in a very conservative manner, leading to less than optimal controller and system designs. This paper not only presents models for human arm dynamics but also develops inter- and intrasubject variability models for a stylus-based haptic device. Data from 15 human subjects (nine male, six female, ages 20-32) were collected using a Phantom Premium 1.5a haptic device for system identification. In this paper, grip-force-dependent models were identified for 1-3-N grip forces in the three spatial axes. Also, variability due to human subjects and grip-force variation were modeled as both structured and unstructured uncertainties. For both forms of variability, the maximum variation, 95 %, and 67 % confidence interval limits were examined. All models were in the frequency domain with force as input and position as output. The identified models enable precise controllers targeted to a subset of possible human operator dynamics.

Given the ease that humans have with using a keyboard and mouse in typical, non-colocated computer interaction, many studies have investigated the value of colocating the visual field and haptic workspaces using immersive virtual reality (VR) modalities. Significant understanding has been gained by previous work comparing physical tasks against VR tasks, visuo-haptic co-location versus non-colocation, and even visuo-haptic rotational misalignments in VR. However, few studies have explored all of these paradigms in context with each other and it is difficult to do inter-study comparisons because of the variation in tested motor tasks. Therefore, the goal for the current study was to characterize human performance of Fitts' point-to-point reaching task - an established test of manual performance - in the physical, co-located/non-colocated VR, and rotated VR visualization conditions. A key finding was the significant decrease observed in end-point error for tasks performed in a co-located virtual reality environment. The results also showed cyclic performance degradations due to rotational visuo-haptic misalignments that were consistent with trends reported by the literature.

Human-computer/machine interface research benefits from accurate human arm models for stability analysis, control, and system design. The current study developed models for human arm dynamics and variability specific to stylus-based kinesthetic haptic interfaces. Data from nine human subjects (5 male, 4 female, ages 20–30) were collected using a three degree-of-freedom haptic device in the X, Y, and Z axes along with a range of grip forces (1–3N) for parametric system identification of the human arm and hand. Variability models that accounted for subject and grip force variation were also identified. The arm and hand model structure consisted of a third-order linear parametric transfer function that was paired with a previously derived second-order model for the haptic robot. The variability was modeled as multiplicative unstructured uncertainty using transfer functions. All of the model parameters were identified in the frequency domain and have force as input and position as output.

We are developing a personal micronavigation system that uses high-resolution gait-corrected inertial measurement units. The goal of this project is to develop a navigation system that use secondary inertial variables, such as velocity, to enable long-term precise navigation in the absence of Global Positioning System (GPS) and beacon signals. In this scheme, measured zero velocity durations from the ground reaction sensors are used to reset the accumulated integration errors from the accelerometers and gyroscopes in position calculation. We achieved an average position error of 4 meters at the end of half-hour walks.

It is unknown whether electroencephalography (EEG) signal characteristics in stroke survivors with motor deficits register enough activity for use with brain-computer interfaces (BCIs). This research studied pre-movement EEG from shoulder-elbow movement in stroke survivors to identify signal characteristics potentially useful for robot-assisted stroke rehabilitation. Pre-movement event-related desynchronization (ERD) was examined in the alpha band mu rhythm for control (n = 7) and stroke subjects (n = 11). Subjects were all right-hand dominant; stroke subjects used their impaired arm and controls were assigned a side to match stroke subjects. Both non-dominant-arm-tested stroke and control subjects exhibited greater ERD intensity vs. those using their dominant arm (p < 0.05). Also, pre-movement ERD was detected in stroke survivors, which suggests at the possibility of using ERD as a BCI system control signal. However, the peak ERD of stroke survivors was significantly lower than that of healthy subjects (p < 0.05), which brings doubt to whether the intensity of ERD in stroke survivors is large enough to be used as a BCI system control signal