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Home arrow Computer Science arrow Robotic Assistive Technologies: Principles and Practice

Review of User Studies, Outcomes, and Clinical Evidence

Acceptance, Use Cases, and Clinical Successes Upper Limb

Development of advanced upper limb technology has been dramatic in the last decade, but parallel improvements in clinical usage and success have not been thoroughly documented yet. As noted, rates of abandonment in the past have been reported to be from 25% to 50% (Biddiss and Chau 2007a,b), with concerns over poor control, limited dexterity, discomfort, poor durability, weight, cost, and limited sensory feedback commonly cited as reasons for rejection (Atkins 1996; Biddiss and Chau 2007a,b). It is presumed that those who continue to use their prosthesis do so because they have attained a level of control that improves their function, although cosmesis may also play a role. In general, this is an understudied area, and the rates of acceptance may change with more recent technological advances. This has led to increasing focus on a “user-centered” approach to design and development (Resnik 2011). Usability research has only recently incorporated end users in the device development stages in an attempt to overcome barriers to clinical use and meet the needs of consumers. From an ergonomic human factors approach, this would seem an essential component in the deployment of robotic prostheses, for which usability hinges on acceptable human interaction with the device.

An additional challenge is lack of agreement on the best methods of measuring clinical success in the application of robotic technology. Substantial work has been done by the working group on Upper Limb Prosthetic Outcome Measures (ULPOM) (Hill et al. 2009). They recommended the use of the World Health Organization’s (2001) International Classification of Functioning, Disability, and Health (ICF) as a framework for selection of outcome measures. However, a clear limitation of existing metrics is that no one measure covers the range of potential outcomes of interest; therefore, a range of metrics covering the elements of the ICF is advised, including measurement of body structure and function (performance of the prosthesis), activity (carrying out tasks), and participation (use of the prosthesis in real-life situations). In addition, standard metrics were developed for standard technology and basic function and do not always take into account the types of improvement expected with advanced robotic devices that add dexterity and feedback. The measurement of higher cognitive functions, such as embodiment and cognitive load/visual attention, are not traditionally considered as prosthetic outcomes but need to be incorporated into future assessments of effectiveness of robotic devices from a human-machine interaction perspective. This will be especially important as strides are made in neural machine interfacing, improved communication protocols, and other advances in limb attachment (detailed in the material that follows) that improve the function of advanced devices past research settings into the clinical environment.

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