The human hand is critically important for performance of many activities of daily living (ADL), including self- feeding, tool use, and recreation. Therefore, loss of the hand due to traumatic injury or disease significantly limits persons with limb loss' ability to perform ADL and work, thus greatly affecting overall quality of life. Despite advances in hand prostheses design and research, several barriers remain against widespread acceptance of prosthetic hands by persons with limb loss. The two most significant deterrents to prosthetic use are prostheses' limited functionality, e.g., limited ability to perfor ADL, and comfort, which includes fit and weight of prosthesis. Additional factors associated with abandonment of hand prostheses are durability of the prosthetic hand; lack of sensory feedback; cost, which can be significant when considering both the initial and maintenance costs of the more sophisticated hand prostheses, i.e., myoelectric models; and the aesthetic appearance of the prosthetic hand. Therefore, today's commercially available hand prostheses fail to address the needs of persons with limb loss, i.e., regaining some degree of autonomy, functionality, and re-entering the work force. To address the above gaps in hand prosthetic research and access to persons with limb loss, we propose to design a new prosthetic hand based on an artificial hand designed by the University of Pisa and the Italian Institute of Technology (IIT), the Pisa/IIT SoftHand (SH). The design of the SH is based on the recently proposed approach of soft synergies that capitalizes on the combination of the concept of human hand synergies and novel soft robotics technologies. The SH was designed and implemented for robotics applications, and therefore its potential for prosthetic applications on patients with transradial amputation remains to be demonstrated. However, preliminary data suggest that healthy controls using the myoelectric version of the SH can grasp and manipulate a wide variety of objects with minimal training. The proposed studies were designed to pursue two aims: (1) to quantify the functional capabilities of the SH for prosthetic applications on intat individuals, and (2) to quantify the functional capabilities of the modified SH on persons with limb loss. The results of Aim 1 (year 1) will be used to implement design modifications to the SH (socket, EMG-based controller, force feedback interface) to be tested on patients (Aim 2, year 2). For both aims, we will use tasks used by clinicians for functional assessment of persons with upper limb loss, as well as biomechanical tests. We hypothesize that patients will learn to use the SH and perform grasp and manipulation tasks to a greater level than that allowed by their current terminal devices. The long-term objectives of this exploratory study are to provide an important foundation for a larger study focusing on (1) the design of a low- cost and high-performance hand prosthesis that will be accepted by persons with limb loss, and (2) enable performance of a wider range of ADL tasks than allowed by today's commercially available prostheses.

Public Health Relevance

It is estimated that upper limb amputation involves approximately 41,000 persons or about 3% of the United States population. Upper limb loss can be devastating to an individual performing activities of daily living and his/her quality of life. n the last half-century, great improvements have been made in body-powered devices and prostheses actuated by electrical activity from forearm muscles (myolectric prostheses). However, rejection rates among adult users of prostheses remain high. Limited functionality of hand prostheses and discomfort are two of the most common reasons for rejection. We propose to design, build, and test on controls and patients with transradial amputation a new myoelectric prosthetic hand. This new prosthetic hand will be based on the design of a biologically-inspired robotic hand, the Pisa/Italian Institute of Technology SoftHand. We propose that the combination of (1) having five individual fingers that can mold around objects and (2) a simple myoelectric control mechanism combined with force feedback will improve functionality and acceptance by amputees compared to current single degree of freedom myoelectric prostheses.

Agency
National Institute of Health (NIH)
Institute
Eunice Kennedy Shriver National Institute of Child Health & Human Development (NICHD)
Type
Exploratory/Developmental Grants (R21)
Project #
5R21HD081938-02
Application #
8919829
Study Section
Special Emphasis Panel (ZRG1-BBBP-X (02))
Program Officer
Quatrano, Louis A
Project Start
2014-08-31
Project End
2016-07-31
Budget Start
2015-08-01
Budget End
2016-07-31
Support Year
2
Fiscal Year
2015
Total Cost
$211,329
Indirect Cost
$34,298
Name
Arizona State University-Tempe Campus
Department
Engineering (All Types)
Type
Schools of Engineering
DUNS #
943360412
City
Tempe
State
AZ
Country
United States
Zip Code
85287
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Gailey, Alycia; Artemiadis, Panagiotis; Santello, Marco (2017) Proof of Concept of an Online EMG-Based Decoding of Hand Postures and Individual Digit Forces for Prosthetic Hand Control. Front Neurol 8:7
Valero-Cuevas, Francisco J; Santello, Marco (2017) On neuromechanical approaches for the study of biological and robotic grasp and manipulation. J Neuroeng Rehabil 14:101
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Godfrey, Sasha B; Bianchi, Matteo; Bicchi, Antonio et al. (2016) Influence of force feedback on grasp force modulation in prosthetic applications: a preliminary study. Conf Proc IEEE Eng Med Biol Soc 2016:5439-5442
Santello, Marco; Bianchi, Matteo; Gabiccini, Marco et al. (2016) Towards a synergy framework across neuroscience and robotics: Lessons learned and open questions. Reply to comments on: ""Hand synergies: Integration of robotics and neuroscience for understanding the control of biological and artificial hands"". Phys Life Rev 17:54-60
Fani, Simone; Bianchi, Matteo; Jain, Sonal et al. (2016) Assessment of Myoelectric Controller Performance and Kinematic Behavior of a Novel Soft Synergy-Inspired Robotic Hand for Prosthetic Applications. Front Neurorobot 10:11