Single degree-of-freedom exoskeleton mechanism design for finger rehabilitation

This paper presents the kinematic design of a single degree-of-freedom exoskeleton mechanism: a planar eight-bar mechanism for finger curling. The mechanism is part of a fingerthumb robotic device for hand therapy that will allow users to practice key pinch grip and finger-thumb opposition, allowing discrete control inputs for playing notes on a musical gaming interface. This approach uses the mechanism to generate the desired grasping trajectory rather than actuating the joints of the fingers and thumb independently. In addition, the mechanism is confined to the back of the hand, so as to allow sensory input into the palm of the hand, minimal size and apparent inertia, and the possibility of placing multiple mechanisms side-by-side to allow control of individual fingersPeer ReviewedPostprint (author’s final draft

Monitoring and diagnosis of a multi-stage manufacturing process using Bayesian networks

This thesis describes the application of Bayesian networks for monitoring and diagnosis of a multi-stage manufacturing process, specifically a high speed production part at Hewlett Packard. Bayesian network "part models" were designed to represent individual parts in-process. These were combined to form a "process model", which is a Bayesian network model of the entire manufacturing process. An efficient procedure is designed for managing the "process network". Simulated data is used to test the validity of diagnosis made from this method. In addition, a critical analysis of this method is given, including computation speed concerns, accuracy of results, and ease of implementation. Finally, a discussion on future research in the area is given

Design method for a reconfigurable mechanism for finger rehabilitation

This paper presents a design method for a reconfigurable single degree-of-freedom mechanism for robotic assisted finger therapy following a stroke. The mechanism is a four-bar linkage that in combination with variable link lengths is capable of reproducing a power grasp finger motion for a wide variety of finger sizes. This is accomplished through an optimization procedure that determines the parameters of the four-bar linkage needed to fit the sampled range of finger trajectories. The linkage is located behind the hand and attaches to the medial phalanx of the finger just above the distal interphalangeal joint. In addition, the mechanism is designed so that it does not interfere with finger motion and so that the subject‘s fingertips and palm are free to touch real objects and experience tactile feedback. In future implementations, the mechanism could be used for a single finger or in parallel with other similar mechanisms to exercise multiple fingers simultaneously. Although the specific application presented here is the four-bar mechanism and finger power grasp motion, the developed design methods may be applied to a much broader range of mechanisms and applications where scalability for human-machine interface is required.Postprint (published version

A Task-based Design Methodology for Robotic Exoskeletons

This study is aimed at developing a task-based methodology for the design of robotic exoskeletons. This is in contrast to prevailing research efforts, which attempt to mimic the human limb, where each human joint is given an exoskeleton counter-joint. Rather, we present an alternative systematic design approach for the design of exoskeletons that can follow the complex three-dimensional motions of the human body independent of anatomical measures and landmarks. With this approach, it is not necessary to know the geometry of the targeted limb but rather to have a description of its motion at the point of attachment.Peer ReviewedObjectius de Desenvolupament Sostenible::3 - Salut i BenestarPostprint (published version

InnoVoting: How Democracy Is Being Reshaped By Women\u27s Innovative Voting Activism & Candidacy

Non

Real-time computer modeling of weakness following stroke optimizes robotic assistance for movement therapy

This paper describes the development of a novel control system for a robotic arm orthosis for assisting patients in motor training following stroke. The robot allows naturalistic motion of the arm and is as mechanically compliant as a human therapist's arms. This compliance preserves the connection between effort and error that appears essential for motor learning, but presents a challenge: accurately creating desired movements requires that the robot form a model of the patient's weakness, since the robot cannot simply stiffly drive the arm along the desired path. We show here that a standard model-based adaptive controller allows the robot to form such a model of the patient and complete movements accurately. However, we found that the human motor system, when coupled to such an adaptive controller, reduces its own participation, allowing the adaptive controller to take over the performance of the task. This presents a problem for motor training, since active engagement by the patient is important for stimulating neuroplasticity. We show that this problem can be solved by making the controller continuously attempt to reduce its assistance when errors are small. The resulting robot successfully assists stroke patients in moving in desired patterns with very small errors, but also encourages intense participation by the patient. Such robot assistance may optimally provoke neural plasticity, since it intensely engages both descending and ascending motor pathways. © 2007 IEEE

Do robotic and non-robotic arm movement training drive motor recovery after stroke by a common neural mechanism? Experimental evidence and a computational model.

Different dose-matched, upper extremity rehabilitation training techniques, including robotic and non-robotic techniques, can result in similar improvement in movement ability after stroke, suggesting they may elicit a common drive for recovery. Here we report experimental results that support the hypothesis of a common drive, and develop a computational model of a putative neural mechanism for the common drive. We compared weekly motor control recovery during robotic and unassisted movement training techniques after chronic stroke (n = 27), as assessed with quantitative measures of strength, speed, and coordination. The results showed that recovery in both groups followed an exponential time course with a time constant of about 4-5 weeks. Despite the greater range and speed of movement practiced by the robot group, motor recovery was very similar between the groups. The premise of the computational model is that improvements in motor control are caused by improvements in the ability to activate spared portions of the damaged corticospinal system, as learned by a biologically plausible search algorithm. Robot-assisted and unassisted training would in theory equally drive this search process

Descriptive and Substantive Representation in Congress: Evidence from 80,000 Congressional Inquiries

A vast literature debates the efficacy of descriptive representation in legislatures. Though studies argue it influences how communities are represented through constituency service, they are limited since legislators’ service activities are unobserved. Using Freedom of Information Act (FOIA) requests, we collected 88,000 records of communication between members of the U.S. Congress and federal agencies during the 108th–113th Congresses. These legislative interventions allow us to examine members’ “follow‐through” with policy implementation. We find that women, racial/ethnic minorities, and veterans are more likely to work on behalf of constituents with whom they share identities. Including veterans offers leverage in understanding the role of political cleavages and shared experiences. Our findings suggest that shared experiences operate as a critical mechanism for representation, that a lack of political consensus is not necessary for substantive representation, and that the causal relationships identified by experimental work have observable implications in the daily work of Congress.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/150606/1/ajps12443-sup-0001-SuppMat.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/150606/2/ajps12443.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/150606/3/ajps12443_am.pd