Reachable Workspace and Proximal Function Measures for Quantifying Upper Limb Motion.

There are a lack of quantitative measures for clinically assessing upper limb function. Conventional biomechanical performance measures are restricted to specialist labs due to hardware cost and complexity, while the resulting measurements require specialists for analysis. Depth cameras are low cost and portable systems that can track surrogate joint positions. However, these motions may not be biologically consistent, which can result in noisy, inaccurate movements. This paper introduces a rigid body modelling method to enforce biological feasibility of the recovered motions. This method is evaluated on an existing depth camera assessment: the reachable workspace (RW) measure for assessing gross shoulder function. As a rigid body model is used, position estimates of new proximal targets can be added, resulting in a proximal function (PF) measure for assessing a subject's ability to touch specific body landmarks. The accuracy, and repeatability of these measures is assessed on ten asymptomatic subjects, with and without rigid body constraints. This analysis is performed both on a low-cost depth camera system and a gold-standard active motion capture system. The addition of rigid body constraints was found to improve accuracy and concordance of the depth camera system, particularly in lateral reaching movements. Both RW and PF measures were found to be feasible candidates for clinical assessment, with future analysis needed to determine their ability to detect changes within specific patient populations

How Does a Robot Know Where to Step? Measuring the Hardness and Roughness of Surfaces

This paper presents an overview of ongoing research on surface exploration at the GRASP Lab. We are investigating the necessary components and modules that must be embedded into a robot for it to have the exploratory capabilities required to recover mechanical properties from a surface given minimal a priori information. Eventually, this information will be used to enable a robot stand and walk stably in an environment that is unknown and unconstrained. The laboratory setup involves a compliant wrist with six degrees of freedom, mounted on a robot arm, and a prototype foot mounted on the wrist. We have successfully designed and implemented exploratory procedures (ep\u27s) to recover penetrability, material hardness and frictional characteristics by exploring the surface

Visual Observation of a Moving Agent

We address the problem of observing a moving agent. In particular, we propose a system for observing a manipulation process, where a robot hand manipulates an object. A discrete event dynamic systems (DEDS) frame work is developed for the hand/object interaction over time and a stabilizing observer is constructed. Low-level modules are developed for recognizing the events that causes state transitions within the dynamic manipulation system. The work examines closely the possibilities for errors, mistakes and uncertainties in the manipulation system, observer construction process and event identification mechanisms. The system utilizes different tracking techniques in order to observe and recognize the task in an active, adaptive and goal-directed manner

Microarray sub-grid detection: A novel algorithm

This is the post print version of the article. The official published version can be obtained from the link below - Copyright 2007 Taylor & Francis LtdA novel algorithm for detecting microarray subgrids is proposed. The only input to the algorithm is the raw microarray image, which can be of any resolution, and the subgrid detection is performed with no prior assumptions. The algorithm consists of a series of methods of spot shape detection, spot filtering, spot spacing estimation, and subgrid shape detection. It is shown to be able to divide images of varying quality into subgrid regions with no manual interaction. The algorithm is robust against high levels of noise and high percentages of poorly expressed or missing spots. In addition, it is proved to be effective in locating regular groupings of primitives in a set of non-microarray images, suggesting potential application in the general area of image processing

Robotic Exploration of Surfaces With a Compliant Wrist Sensor

This paper presents some results of an ongoing research project to investigate the components and modules that are necessary to equip a robot with exploratory capabilities. Of particular interest is the recovery of certain material properties from a surface, given minimal a priori information, with the intent to use this information to enable a robot to stand and walk stably on a surface that is unknown and unconstrained. To this end, exploratory procedures (ep\u27s) have been designed and implemented to recover penetrability, material hardness and surface roughness by exploring the surface using a compliant wrist sensor. A six degree-of-freedom compliant wrist sensor, which combines passive compliance and active sensing, has been developed to provide the necessary flexibility for force and contact control, as well as to provide accurate position control. This paper describes the compliant wrist and sensing mechanism design along with a hybrid control algorithm that utilizes the sensed information from the wrist to adjust the apparent stiffness of the end-effector as desired

A Multiagent System for Intelligent Material Handling

The goal of our research is to investigate manipulation, mobility, sensing, control and coordination for a multiagent robotic system employed in the task of material handling, in an unstructured, indoor environment. In this research, manipulators, observers, vehicles, sensors, and human operator(s) are considered to be agents. Alternatively, an agent can be a general-purpose agent (for example, a six degree of freedom manipulator on a mobile platform with visual force, touch and position sensors). Possible applications for such a system includes handling of waste and hazardous materials, decontamination of nuclear plants, and interfacing between special purpose material handling devices in warehouses. The fundamental research problems that will be studied are organization, or the decomposition of the task into subtasks and configuring the multiple agents with appropriate human interaction, exploration, or the process of exploring geometric, material and other properties about the environment and other agents, and coordination, or the dynamic control of multiple agents for manipulation and transportation of objects to a desired destination

Gigabit Telerobotics: Applying Advanced Information Infrastructure

Advanced manufacturing concepts such as Virtual Factories use an information infrastructure to tie together changing groups of specialized facilities into agile manufacturing systems. A necessary element of such systems is the ability to teleoperate machines, for example telerobotic systems with full-capability sensory feedback loops. We have identified three network advances needed for splitting robotic control from robotic function: increased bandwidth, decreased error rates, and support for isochronous traffic. These features are available in the Gigabit networks under development at Penn and elsewhere. A number of key research questions are posed by gigabit telerobotics. There are issues in network topology, robot control and distributed system software, packaging and transport of sensory data (including wide-area transport), and performance implications of architectural choices using measures such as cost, response time, and network utilization. We propose to explore these questions experimentally in a joint research effort combining the Distributed Systems Laboratory (DSL) and the General Robotics and Sensory Perception (GRASP) Laboratory at the University of-Pennsylvania. The proposed experiments should provide important early results. A detailed research program is described

Comparison of Artificial Intelligence based approaches to cell function prediction

Predicting Retinal Pigment Epithelium (RPE) cell functions in stem cell implants using non-invasive bright field microscopy imaging is a critical task for clinical deployment of stem cell therapies. Such cell function predictions can be carried out using Artificial Intelligence (AI) based models. In this paper we used Traditional Machine Learning (TML) and Deep Learning (DL) based AI models for cell function prediction tasks. TML models depend on feature engineering and DL models perform feature engineering automatically but have higher modeling complexity. This work aims at exploring the tradeoffs between three approaches using TML and DL based models for RPE cell function prediction from microscopy images and at understanding the accuracy relationship between pixel-, cell feature-, and implant label-level accuracies of models. Among the three compared approaches to cell function prediction, the direct approach to cell function prediction from images is slightly more accurate in comparison to indirect approaches using intermediate segmentation and/or feature engineering steps. We also evaluated accuracy variations with respect to model selections (five TML models and two DL models) and model configurations (with and without transfer learning). Finally, we quantified the relationships between segmentation accuracy and the number of samples used for training a model, segmentation accuracy and cell feature error, and cell feature error and accuracy of implant labels. We concluded that for the RPE cell data set, there is a monotonic relationship between the number of training samples and image segmentation accuracy, and between segmentation accuracy and cell feature error, but there is no such a relationship between segmentation accuracy and accuracy of RPE implant labels

Laser-noise-induced correlations and anti-correlations in Electromagnetically Induced Transparency

High degrees of intensity correlation between two independent lasers were observed after propagation through a rubidium vapor cell in which they generate Electromagnetically Induced Transparency (EIT). As the optical field intensities are increased, the correlation changes sign (becoming anti-correlation). The experiment was performed in a room temperature rubidium cell, using two diode lasers tuned to the $^{85}$Rb $D_2$ line ($\lambda = 780$nm). The cross-correlation spectral function for the pump and probe fields is numerically obtained by modeling the temporal dynamics of both field phases as diffusing processes. We explored the dependence of the atomic response on the atom-field Rabi frequencies, optical detuning and Doppler width. The results show that resonant phase-noise to amplitude-noise conversion is at the origin of the observed signal and the change in sign for the correlation coefficient can be explained as a consequence of the competition between EIT and Raman resonance processes.Comment: Accepted for publication in EPJ