13 research outputs found
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Novel postural control algorithm for control of multifunctional myoelectric prosthetic hands.
The myoelectric controller (MEC) remains a technological bottleneck in the development of multifunctional prosthetic hands. Current MECs require physiologically inappropriate commands to indicate intent and lack effectiveness in a clinical setting. Postural control schemes use surface electromyography signals to drive a cursor in a continuous two-dimensional domain that is then transformed into a hand posture. Here, we present a novel algorithm for a postural controller and test the efficacy of the system during two experiments with 11 total subjects. In the first experiment, we found that performance increased when a velocity cursor-control technique versus a position cursor-control technique was used. Also, performance did not change when using 3, 4, or 12 surface electrodes. In the second experiment, subjects commanded a six degree-of-freedom virtual hand into seven functional postures without training, with completion rates of 82 +/- 4%, movement times of 3.5 +/- 0.2 s, and path efficiencies of 45 +/- 3%. Subjects retained the ability to use the postural controller at a high level across days after a single 1 hr training session. Our results substantiate the novel algorithm for a postural controller as a robust and advantageous design for a MEC of multifunction prosthetic hands
Measuring embodiment: A review of methods for prosthetic devices
The development of neural interfaces to provide improved control and somatosensory feedback from prosthetic limbs has initiated a new ability to probe the various dimensions of embodiment. Scientists in the field of neuroprosthetics require dependable measures of ownership, body representation, and agency to quantify the sense of embodiment felt by patients for their prosthetic limbs. These measures are critical to perform generalizable experiments and compare the utility of the new technologies being developed. Here, we review outcome measures used in the literature to evaluate the senses of ownership, body-representation, and agency. We categorize these existing measures based on the fundamental psychometric property measured and whether it is a behavioral or physiological measure. We present arguments for the efficacy and pitfalls of each measure to guide better experimental designs and future outcome measure development. The purpose of this review is to aid prosthesis researchers and technology developers in understanding the concept of embodiment and selecting metrics to assess embodiment in their research. Advances in the ability to measure the embodiment of prosthetic devices have far-reaching implications in the improvement of prosthetic limbs as well as promoting a broader understanding of ourselves as embodied agents
Structural validity and reliability of the patient experience measure: A new approach to assessing psychosocial experience of upper limb prosthesis users
Recent advances in upper limb prosthetics include sensory restoration techniques and osseointegration technology that introduce additional risks, higher costs, and longer periods of rehabilitation. To inform regulatory and clinical decision making, validated patient reported outcome measures are required to understand the relative benefits of these interventions. The Patient Experience Measure (PEM) was developed to quantify psychosocial outcomes for research studies on sensory-enabled upper limb prostheses. While the PEM was responsive to changes in prosthesis experience in prior studies, its psychometric properties had not been assessed. Here, the PEM was examined for structural validity and reliability across a large sample of people with upper limb loss (n = 677). The PEM was modified and tested in three phases: initial refinement and cognitive testing, pilot testing, and field testing. Exploratory factor analysis (EFA) was used to discover the underlying factor structure of the PEM items and confirmatory factor analysis (CFA) verified the structure. Rasch partial credit modeling evaluated monotonicity, fit, and magnitude of differential item functioning by age, sex, and prosthesis use for all scales. EFA resulted in a seven-factor solution that was reduced to the following six scales after CFA: social interaction, self-efficacy, embodiment, intuitiveness, wellbeing, and self-consciousness. After removal of two items during Rasch analyses, the overall model fit was acceptable (CFI = 0.973, TLI = 0.979, RMSEA = 0.038). The social interaction, self-efficacy and embodiment scales had strong person reliability (0.81, 0.80 and 0.77), Cronbach\u27s alpha (0.90, 0.80 and 0.71), and intraclass correlation coefficients (0.82, 0.85 and 0.74), respectively. The large sample size and use of contemporary measurement methods enabled identification of unidimensional constructs, differential item functioning by participant characteristics, and the rank ordering of the difficulty of each item in the scales. The PEM enables quantification of critical psychosocial impacts of advanced prosthetic technologies and provides a rigorous foundation for future studies of clinical and prosthetic interventions
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Measuring embodiment: A review of methods for prosthetic devices
The development of neural interfaces to provide improved control and somatosensory feedback from prosthetic limbs has initiated a new ability to probe the various dimensions of embodiment. Scientists in the field of neuroprosthetics require dependable measures of ownership, body representation, and agency to quantify the sense of embodiment felt by patients for their prosthetic limbs. These measures are critical to perform generalizable experiments and compare the utility of the new technologies being developed. Here, we review outcome measures used in the literature to evaluate the senses of ownership, body-representation, and agency. We categorize these existing measures based on the fundamental psychometric property measured and whether it is a behavioral or physiological measure. We present arguments for the efficacy and pitfalls of each measure to guide better experimental designs and future outcome measure development. The purpose of this review is to aid prosthesis researchers and technology developers in understanding the concept of embodiment and selecting metrics to assess embodiment in their research. Advances in the ability to measure the embodiment of prosthetic devices have far-reaching implications in the improvement of prosthetic limbs as well as promoting a broader understanding of ourselves as embodied agents.
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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.
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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 (0–10 mm), contact (0 N), and force (0–50 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.
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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’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’s learning.
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Development and Validation of a Postural Controller for Advanced Myoelectric Prosthetic Hands
Myoelectric control systems (MECs) remain the technological bottleneck in the development of advanced prosthetic hands. MECs should provide a human machine interface that deciphers user intent in real-time and operates effectively in daily life. Current MECs like finite state machines and pattern recognition systems require physiologically inappropriate commands to indicate intent and/or lack effectiveness in a clinical setting. The work of this dissertation aims to develop and validate a novel MEC architecture, namely postural control, in order to supplant the current state of the art MECs and recreate more of the characteristics of the intact limb. Specifically, the development of the postural control systems builds upon previous work based on principal component analysis of human grasping. Novel attributes of the postural control system were then added to the MEC, empirically tested, and validated with able limbed subjects using a virtual hand interface. Further investigation of the postural controller was performed by comparing it to state of the art commercial and research MECs with able limbed subjects using a physical prosthesis during activities of daily living. The dissertation concludes by verifying the increased effectiveness and robustness of the postural controller compared to other MECs when used by persons with transradial limb loss to perform activities of daily living with a physical prosthesis
Design Of A Myoelectric Controller For A Multi-Dof Prosthetic Hand Based On Principal Component Analysis
The goal of this investigation is to develop a multi-degree of freedom (DOF) prosthesis controller that uses myoelectric signals as control inputs and which has been dimensionally optimized using Principal Component Analysis (PCA). Currently available multi-DOF hand prostheses cannot be fully utilized because there are fewer control inputs than the number of degrees of freedom (i.e. – joints) that need to be controlled. Based on work from the field of neuroscience it has been shown that grasping is a ‘low dimensional’ task. Santello et al. used PCA to quantify the principal components (patterns of joint movements) involved in grasping. It was found that grasping tasks involving a number of everyday items could be described by only two principal components. This implies that multi-DOF hand postures can be controlled using only two degrees of control. Therefore, a PCA-based myoelectric prosthetic hand controller can drive grasping postures with only two independent control sites. This is an encouraging finding since current clinical practice indicates two, or three, independent control sites can be located on the residual limb of a typical person with a transradial amputation.
The following paper discusses the design and development of a PCA-based myoelectric prosthetic hand controller. Also, the results of a validation experiment are shared