Overview

Robotic manipulators that are not attached directly to an individual’s arm can loosely be organized into 3 categories: those that connect to a wheelchair ; desktop-mounted arms embedded in activity-specific workstations ; or arms that move about the room on their own mobile base. The same devices, moreover, can be organized according to the range of their functionality. Some devices are designed for a single purpose, like self-feeding, whereas others are capable of performing a variety of tasks within a particular activity context, like the office. Finally, general purpose robotic arms exist, and hold the potential to perform a wide range of functional manipulations in many contexts, including factory environments and homes.

Many externally mounted manipulation aids are now commercially available; their cost is governed, at least in part, by their functionality. Commercially available tools include the ARM (Assistive Robotic Manipulator, Exact Dynamics; formerly the MANUS) ( Fig. 1 ) and Raptor robotic arms, the MASTER/EPI-RAID system (sold as the AFMaster, Afma Robots, France ), the DeVAR system (sold by The Tolfa Corporation, Palo Alto, CA ) and various desktop-mounted feeders (examples include HANDY, Rehab Robotics, UK ; Neater Eater, Neater Solutions Ltd, UK ; My Spoon, SECOM Co. Ltd, Tokyo ; and Winsford Feeder, Winsford Products Inc, Pennington, NJ ) ( Fig. 2 ). As is perhaps to be expected, the more functionality provided by a particular system, the higher its cost. Feeders start at roughly $2500. The HANDY costs $6300 yet supports a range of functional activities in addition to eating, like shaving, painting, and applying make-up. The Raptor and ARM arms, which are intended for general-purpose manipulation, retail at $12,000 and $35,000, respectively. Finally, workstation robots that include a general-purpose robotic arm tend to be most costly. In 2004, the AFMaster retailed for $50,000. Costs of several devices, drawn from Hillman, are indicated in Table 1 .

The Assistive Robotic Manipulator (ARM, formerly MANUS), a wheelchair-mounted robotic arm. ( Courtesy of Exact Dynamics, from Assistive Robotic Manipulator (ARM) aka Manus mobile rehabilitation robot. 2004. Available at: http://www.exactdynamics.nl/english/index.html . Accessed June 15, 2009.)

MySpoon, an example of a feeder robot. ( Courtesy of SECOM Co. Ltd, Tokyo, from SECOM: Meal-assistance robot MySpoon. 2009. Available at: http://www.secom.co.jp/english/myspoon/index.html . Accessed June 22, 2009.)

Widespread access to commercially available robotic manipulators is governed partly by recommendations of health care service providers and partly by reimbursement policies. For costly devices, reimbursement potential exists only for limited groups of devices and users. The ARM robotic arm, for example, has received reimbursement support from the government of the Netherlands; between 2001 and 2004 this was provided through an exploratory government grant and since 2004 it has been provided formally by the country’s health care system. There are now more than 150 ARM arms owned by individual users. Comparable reimbursement support from United States insurance providers, however, does not exist at present. Evidence that might support reimbursement in the United States for similarly priced systems has been mixed; in 1994, for example, evaluators from the United States Veterans Affairs found the DeVAR desktop workstation insufficiently robust and overly task limited to be considered medically prescribable.

In the United States, use of robotic manipulation aids has additionally been limited by a lack of standardized prescription protocols among service providers. A 1996 study of 12 United States care facilities, for example, found occupational therapists received little or no training in the use of robotic feeders, yet they felt the devices to be too expensive, difficult to use, unattractive, unreliable, inconvenient, or time consuming to set up and install. In the surveyed facilities, 920 individuals were found to be completely unable to feed themselves, yet only 3 were using a powered feeding device. These aids had been acquired as a result of factors like device availability and the motivation of individuals and their therapists. None of the centers had a standardized procedure by which clients’ needs or appropriateness for robotic feeders was evaluated.

Manipulator Control

Unlike prosthetic arms, control of externally mounted robotic arms is rarely driven by myoelectric signals (see Kyberd and Chappell for an exception). Instead, users must supply control signals to the robots using a variety of possible input devices, which may include voice input, head-mounted switches, track balls, joysticks, mice, keyboards, or touchscreen devices. The robotic device El-E additionally allows users to input control commands using a laser pointer, which may be mounted to the ear or controlled with the hand.

The choice of input device depends on users’ comfort and physical capacity. In the study by Tijsma and colleagues, prototypical development of a wide range of potential input devices, like large lapboard mounted push buttons and individually molded hand-held grips, are described in detail. User trials with El-E found input device preference among users with amyotrophic lateral sclerosis (ALS) to be partially explained by levels of functional arm impairment recorded on the Revised ALS Functional Rating Scale (ALSFRS-R). Those with higher “handwriting” scores on the ALSFRS-R were more likely to prefer controlling a laser pointer input with the hand; those with lower scores were more likely to prefer a pointer mounted to the ear.

Inputs to robotic manipulators may also be constrained by cognitive factors. The standard interface to the ARM, for example, requires an individual to activate a switch to select which degree of freedom (DOF) he or she would like to manipulate; after selection, this DOF may be manipulated independently of the others. The ARM may alternatively be controlled in Cartesian coordinates, using a similar “switching” form of control. Using either switching method, performing a complex task like grasping and lifting a glass of water requires many DOF switches and an ability to plan the order of these switches, which typically results in lengthy task times. In the study by Mokhtari and colleagues, for example, a user’s effort to grasp a remote control was found to take more than 9 minutes with the ARM. Task performance may be further complicated by the fact that, like most prosthetic arms, externally mounted robotic arms typically do not provide force-feedback to the user. Tijsma and colleagues designed several training tasks in an effort to demonstrate to users the vertical forces they were applying to objects with the robot. The ability to translate what was learned during these training tasks to novel tasks, however, depended on the cognitive abilities of the user.

To reduce physical and cognitive demands associated with device control, many systems employ varying degrees of abstraction in their command structure. El-E, for example, takes commands in the form of a gesture to an object on the floor, and this gesture can be made using a laser pointer or touch screen device. Robot navigation to the object and grasp are then entirely automated. Even for devices that must currently be manually controlled, some degree of abstraction may be preferred by users. Eftring and colleagues found that roughly 50% of task time with the ARM, on average, related not to moving the robot’s end effector but to the execution of grasping. Users indicated they would prefer the device with an automated grasp, were it to exist.

ProVAR, MASTER/EPI-RAID, and Movaid all feature task-level degrees of abstraction in the control of a robotic arm. Using MASTER/EPI-RAID, for example, a user may grasp a book from the bookshelf by selecting prerecorded locations of books from a pull-down menu; alternatively, he or she may select a book by title. Movaid similarly allows users to control a moving robot to execute tasks, like operating a microwave, with abstract and task-level commands. ProVAR not only allows users to invoke prerecorded activities, but to visually draft new task “plans” by manipulating a virtual arm via click and drag operations or keyboard inputs of joint angles. Actions on the virtual arm can be refined until they meet a user’s needs, then stored and played on the physical arm at any point in the future. Visualizing 3-dimensional kinematics of the virtual arm on a 2-dimensional screen has, however, proven to be difficult for some users.

Trends in Manipulator Evaluation

Opportunities for Future Research

Several robotic manipulators are on the market and are reasonably affordable (several feeders cost less than $5000) and at least one general-purpose robotic arm retails for less than $15,000. Along with increasingly moderate price tags, there is evidence to suggest that devices like robotic feeders and general-purpose arms reduce the amount of care required by an individual and increase his or her independence. Nevertheless, robotic manipulation devices have yet to see widespread use in North America.

Comprehensive evaluation and effective dissemination of information to health care practitioners may promote increased use. Cost savings associated with the ARM device, for example, have been demonstrated in the European Union, but have yet to be translated into a North American context. Device evaluations that span longer time periods can also provide information needed to gauge costs associated with device maintenance and repair.

Consumer perception of factors that effect long-term use of robotic arms have been identified and prioritized; these may prove to be valid predictors of device acceptance and reduce the rate of device abandonment. For robotic arms, the 5 most important factors are device effectiveness, operability, dependability, affordability, and flexibility.