The Point Digit II: Mechanical Design and Testing of a Ratcheting Prosthetic Finger

Introduction People with partial hand loss represent the largest population of upper limb amputees by a factor of 10. The available prosthetic componentry for people with digit loss provide various methods of control, kinematic designs, and functional abilities. Here, the Point Digit II is empirically tested and a discussion is provided comparing the Point Digit II with the existing commercially available prosthetic fingers. Materials and Methods Benchtop mechanical tests were performed using prototype Point Digit II prosthetic fingers. The battery of tests included a static load test, a static mounting tear-out test, a dynamic load test, and a dynamic cycle test. These tests were implemented to study the mechanisms within the digit and the ability of the device to withstand heavy-duty use once out in the field. Results The Point Digit II met or exceeded all geometric and mechanical specifications. The device can withstand over 300 lbs of force applied to the distal phalange and was cycled over 250,000 times without an adverse event representing 3 years of use. Multiple prototypes were utilized across all tests to confirm the ability to reproduce the device in a reliable manner. Conclusions The Point Digit II presents novel and exciting features to help those with partial hand amputation return to work and regain ability. The use of additive manufacturing, unique mechanism design, and clinically relevant design features provides both the patient and clinician with a prosthetic digit, which improves upon the existing devices available. &nbsp;</p

Multi-modal prosthetic fingertip sensor with proximity, contact, and force localization capabilities

The lack of sensory feedback provided by prosthetic hands dramatically limits the utility of the device. Peripheral nerve interfaces are now able to produce stable somatosensory percepts for upper limb amputees. Sensors must be able to detect forces across the fingers of the prosthesis in a repeatable and reliable fashion. We solved this concern with a novel multi-modal tactile sensor which consists of an infrared proximity sensor and a barometric pressure sensor embedded in an elastomer layer with potential use in prosthetic devices. Signals from both sensors measure proximity (0&ndash;10 mm), contact (0 N), and force (0&ndash;50 N) and are combined to localize impact at five spatial locations and three angles of incidence. Here, we describe the sensor design, its characterization, and data analysis. We use Gaussian process regression to fuse the signals from both sensors to obtain calibrated force in Newton with an R2 value of 0.99. We use supervised learning to localize probe position and direction with classification accuracies of 96% and 89%, respectively. The complementary nature of both sensors leads to several sensing modalities that no one sensor can provide on its own and the repeatable, reliable, and compact form of the sensor enables use in multi-functional prosthetic hands. </div

Combination of Simultaneous Artificial Sensory Percepts to Identify Prosthetic Hand Postures: A Case Study

Multiple sources of sensory information are combined to develop hand posture percepts in the intact system, but the combination of multiple artificial somatosensory percepts by human prosthesis users has not been studied. Here, we report on a case study in which a person with transradial amputation identified prosthetic hand postures using artificial somatosensory feedback. He successfully combined five artificial somatosensory percepts to achieve above-chance performance of 95.0% and 75.7% in identifying four and seven postures, respectively. We studied how artificial somatosensation and the extant hand representation are combined in the decision-making process by providing two mappings between the prosthetic sensor and the location of the sensory percept: (1) congruent, and (2) incongruent. The participant&rsquo;s ability to combine and engage with the sensory feedback significantly differed between the two conditions. The participant was only able to successfully generalize prior knowledge to novel postures in the congruent mapping. Further, he learned postures more accurately and quickly in the congruent mapping. Finally, he developed an understanding of the relationships between postures in the congruent mapping instead of simply memorizing each individual posture. These experimental results are corroborated by a Bayesian decision-making model which tracked the participant&rsquo;s learning. </div

On the development of optical peripheral nerve interfaces

Limb loss and spinal cord injury are two debilitating conditions that continue to grow in prevalence. Prosthetic limbs and limb reanimation present two ways of providing affected individuals with means to interact in the world. These techniques are both dependent on a robust interface with the peripheral nerve. Current methods for interfacing with the peripheral nerve tend to suffer from low specificity, high latency and insufficient robustness for a chronic implant. An optical peripheral nerve interface may solve some of these problems by decreasing invasiveness and providing single axon specificity. In order to implement such an interface three elements are required: (1) a transducer capable of translating light into a neural stimulus or translating neural activity into changes in fluorescence, (2) a means for delivering said transducer and (3) a microscope for providing the stimulus light and detecting the fluorescence change. There are continued improvements in both genetically encoded calcium and voltage indicators as well as new optogenetic actuators for stimulation. Similarly, improvements in specificity of viral vectors continue to improve expression in the axons of the peripheral nerve. Our work has recently shown that it is possible to virally transduce axons of the peripheral nerve for recording from small fibers. The improvements of these components make an optical peripheral nerve interface a rapidly approaching alternative to current methods

NEUROFUZZY LOGIC AS A CONTROL ALGORITHM FOR AN EXTERNALLY POWERED MULTIFUNCTIONAL HAND PROSTHESIS

Figure 1 : Seasons of the year according to classical set theory (top) and fuzzy set theory (bottom) [8]. NEUROFUZZY LOGIC AS A CONTROL ALGORITHM FOR AN EXTERNALLY POWERED MULTIFUNCTIONAL HAND PROSTHESIS We propose an algorithm based upon neurofuzzy technology. We believe that because of the inherent &quot;fuzziness&quot; of human activity, a control algorithm based on fuzzy logic may have advantages for multifunctional prosthesis control. We seek an acceptable compromise between the number of electrode sites used and processing complexity, and thereby desire not more than three to four control sites to control three to four DOF. This approach delivers more information to the system and, by using fuzzy logic, reduces the complexity of the processing. BACKGROUND Fuzzy set theory maintains that all elements have a degree of membership (DOM) in all sets ranging from 0 to 1 inclusively. Artificial neural networks (ANN) allow for system training based upon sample input data. As sample data is fed into the system, outputs are calculated by associating a degree-of-strength (DOS) to each input. The error difference between the desired and actual output is then back propagated through the system, and the DOS are adjusted accordingly. This learning process is characterized by the choice of learning method and the number of winner neurons, where th