Control of a Powered Prosthetic Hand Via a Tracked Glove 1

Although commercially available robotic prosthetic limbs now provide fingers with multiple degrees of freedom (DOFs), closely resembling the human hand, the amount of control channels provided by typical biological signals (electromyography or neural electrodes) Thus, by combining the two technologies, we developed a system to teleoperate the TouchBionics RoboLimb TM device with a custom-built 6DOF glove worn on the sound hand. The RoboLimb hand is a 6DOF prosthetic device which weighs under 500 g and can be controlled via a protocol based on the controller area network (CAN) The purpose of this system is to support on-going work in our laboratory to benefit unilateral amputees who have a sound hand with which they can teleoperate or otherwise interact with a robotic prosthetic device. Specifically, we are investigating the use of this system in (1) The performance of activities of daily living that will benefit from the mirroring of movements between the sound hand and the prosthetic device (e.g., folding a towel, moving a table, picking up a laundry basket, etc.). (2) Allow the user to pose the prosthetic hand in any desired configuration before performing a task. (3) Enable the recording of task specific grasping motions that can be defined and played back by the user in the field. Methods Tracking Glove. We have built a tracking glove using six SparkFun 2.2 00 flexion sensors (SparkFun, Inc.), a glove, an Arduino [4] Uno microcontroller, and Velcro TM to hold the Arduino device on the glove. The flexion sensors were attached to the glove through the use of custom-sewn sleeves which reduce the stress on the sensor attachment point by allowing the sensor to slide during movement. The flexion sensors were soldered to flexible multistrand wires, and the contact areas were encased in moldable thermoplastic (InstaMorph TM ) in order to prevent the soldered connections from failing during use. The resulting glove prototype is shown in The thermoplastic encased ends of the sensors were also molded into sewable buttons by a process involving laser-cut acrylic molds described in Healthy Limb Adaptor. In order to allow nonimpaired volunteers to wear and test the mirrored teleoperation of the system, a healthy limb adapter was created incorporating an Otto Bock QuickConnect ring molded onto the end of a 5 in. diameter polyvinyl chloride pipe using thermoplastic (InstaMorph T

Grip pressure measurements during activities of daily life

Research has expanded human-machine communication methods past direct programming and standard hand- held joystick control. Individual force sensors have been used as a simple means of providing environmental information to a robot and research has shown that more advanced sensitive skins can be viable input devices. These touch sensitive surfaces allow for additional modes of interaction between machines in open, undefined environments. These interactions include object detection for navigation and safety but can also be used for recognition of users command gestures by their machine partner. Key to successful implementation of these gestures is the understanding of varied strategies used for communication and interaction and the development of performance limits. Data of dominant hand grip forces was collected using a Tekscan Grip VersaTek Pressure Measurement System during opening of a door. Analysis of data from 10 male and female subjects is presented. The results of qualitative and quantitative analysis of these data show variability in hand configurations between users. Average data over the cohort is reported. These data will be used in future work to provide human metrology constraints and limits for use in simulation and design of new, physical human-robot interaction systems. © 2014 SPIE

Grip Pressure and Wrist Joint Angle Measurement during Activities of Daily Life

The goal of this study is to improve future physical human-robot interaction and rehabilitation systems. Experiments were conducted to collect dominant hand grip pressure and joint-angle data during activities of daily life. Representative actions chosen as part of this study were: pushing a weighted cylinder along a flat surface, pulling a weighted cylinder across a flat surface, and lifting a weighted cylinder from a flat surface to shoulder height. Three separate weighted cylinders were used, 3lbs., 5lbs., and 10lbs., and the representative motions were repeated five times for each cylinder. A Tekscan Grip VersaTek Pressure Measurement System and Motion Analysis Cortex System were utilized to collect data. Each subject was outfitted with 18 separate sensorized piezo-resistive tiles placed on their dominant hand and 33 reflective markers at representative locations on their body. The motion of each cylinder was tracked via the placement of seven retro-reflective markers on the cylinder\u27s surface. Analysis of cohort data from five male and five female volunteers, aged between 23 and 51 years, is presented. A Moving Average Filter was implemented to automatically determine contact between the subject\u27s hand and the weighted cylinder. Once contact was determined during an action cycle, maximum detected pressure from each of 18 sensing areas was found. Results report wrist angle during the action- cycle as well as maximum applied pressure during each action across the cohort. Average wrist angle per action-cycle, by action, is also reported for the cohort. These data, along with results from a previous study, will be used improve and verify human intent models for use in future pHRI and rehabilitation systems

Efficient Evaluation and Learning in Multilevel Parallel Constraint Grammars

In multilevel parallel Optimality Theory grammars, the number of candidates (possible paths from the input to the output level) increases exponentially with the number of levels of representation. The problem with this is that with the customary strategy of listing all candidates in a tableau, the computation time for evaluation (i.e., choosing the winning candidate) and learning (i.e., reranking the constraints on the basis of language data) increases exponentially with the number of levels as well. This article proposes instead to collect the candidates in a graph in which the number of nodes and the number of connections increase only linearly with the number of levels of representation. As a result, there exist procedures for evaluation and learning that increase only linearly with the number of levels. These efficient procedures help to make multilevel parallel constraint grammars more feasible as models of human language processing. We illustrate visualization, evaluation, and learning with a toy grammar for a traditional case that has already previously been analyzed in terms of parallel evaluation, namely, French liaison