Forskning
Engineering and surgical advancements enable more cognitively integrated bionic arms
Integrating tactile and kinesthetic feedback in a bionic arm results in performance closer to able-bodied individuals.
Real-Time and Offline Evaluation of Myoelectric Pattern Recognition for the Decoding of Hand Movements
Pattern recognition algorithms have been widely used to map surface electromyographic signals to target movements as a source for prosthetic control. However, most investigations have been conducted offline by performing the analysis on pre-recorded datasets. While real-time data analysis (i.e., classification when new data becomes available, with limits on latency under 200–300 milliseconds) plays an important role in the control of prosthetics, less knowledge has been gained with respect to real-time performance. Recent literature has underscored the differences between offline classification accuracy, the most common performance metric, and the usability of upper limb prostheses. Therefore, a comparative offline and real-time performance analysis between common algorithms had yet to be performed. In this study, we investigated the offline and real-time performance of nine different classification algorithms, decoding ten individual hand and wrist movements. Surface myoelectric signals were recorded from fifteen able-bodied subjects while performing the ten movements. The offline decoding demonstrated that linear discriminant analysis (LDA) and maximum likelihood estimation (MLE) significantly (p < 0.05) outperformed other classifiers, with an average classification accuracy of above 97%. On the other hand, the real-time investigation revealed that, in addition to the LDA and MLE, multilayer perceptron also outperformed the other algorithms and achieved a classification accuracy and completion rate of above 68% and 69%, respectively.
Analysis of Neural Network based Proportional Myoelectric Hand Prosthesis Control
We show that state-of-the-art advanced artificial intelligence algorithms based on “deep neural networks” achieve superior performance compared to two common algorithms for decoding movement intent for prosthetic control. We performed a rigorous analysis to explain why the state-of-the-art algorithms perform better based on an analysis of 11 able-bodied subjects and 4 prosthesis users. This work shows and explains why state-of-the-art algorithms are better which is important since it has been a challenge to demonstrate that new algorithms are better due to external disturbances.
Hand Temperature Is Not Consistent With Illusory Strength During the Rubber Hand Illusion
Contrary to previous claims, skin temperature is not a biomarker for ownership. We induced the Rubber Hand Illusion (RHI), the gold standard experiment to study the sense of ownership, while monitoring the participants skin temperature with a precise thermal camera. We found that temperature change does not correlate with the ownership score in the RHI paradigm and thus suggest caution in drawing strong inferences of ownership based on the skin temperature.
Mathematical and Computational Models for Pain: A Systematic Review
Mathematical models are one possible avenue for studying the working mechanisms of pain. This is a systematic review of mathematical and computational models for pain. 31 articles were identified and sorted based on their classification algorithm, data collection method, or proposal of a mathematical model.
The rubber hand illusion is a fallible method to study ownership of prosthetic limbs
Not everyone is susceptible to illusions. We showed that the Rubber Hand Illusion (RHI) can be induced with different types of tactile stimulation but about 30% of people are immune to it. This includes four people implanted with neuromusculoskeletal prostheses who otherwise perceive their prostheses as part of their own body when using it in daily life, thus highlighting limitations to the RHI as an experimental paradigm.
Neurophysiological models of phantom limb pain: what can be learnt
This article discusses several existing hypotheses for the origin of phantom limb pain (PLP) and speculates on their respective implications for treatments. While seemingly contradicting, these neurophysiological models of PLP might not be mutually exclusive. All of them involve mechanisms by which an artificial limb could counteract PLP.
Chronic Use of a Sensitized Bionic Hand Does Not Remap the Sense of Touch
After long-term use of a neuromusculoskeletal prosthesis where the location of a force sensor on a prosthetic hand and the location that electrical nerve stimulation corresponding to grasping force was felt did not originally match, we show that the perceived location of the touch does not change to match the sensor location. Our results with three neuromusculoskeletal prosthesis users conform to previous studies that suggest sensory maps in the brain are stable in adulthood and cannot be modified. Although the sensation and prosthesis sensor locations did not match, participants still indicated greater confidence in their prosthesis control and greater embodiment of the bionic limb. Although congruent sensory location is not required for the benefits of sensory feedback to arise, it may still be preferable for prosthesis users and thus other methods of changing sensory locations remains a future area of development.
Out of the Clinic, into the Home: The in-Home Use of Phantom Motor Execution Aided by Machine Learning and Augmented Reality for the Treatment of Phantom Limb Pain
Phantom motor execution (PME) facilitated by augmented/virtual reality (AR/VR) and serious gaming (SG) has been proposed as a treatment for phantom limb pain (PLP). Evidence of the efficacy of this approach was obtained through a clinical trial involving individuals with chronic intractable PLP affecting the upper limb, and further evidence is currently being sought with a multi-sited, international, double blind, randomized, controlled clinical trial in upper and lower limb amputees. All experiments have been conducted in a clinical setting supervised by a therapist. Here, we present a series of case studies (two upper and two lower limb amputees) on the use of PME as a self-treatment. We explore the benefits and the challenges encountered in translation from clinic to home use with a holistic, mixed-methods approach, employing both quantitative and qualitative methods from engineering, medical anthropology, and user interface design. All patients were provided with and trained to use a myoelectric pattern recognition and AR/VR device for PME. Patients took these devices home and used them independently over 12 months. We found that patients were capable of conducting PME as a self-treatment and incorporated the device into their daily life routines. Use patterns and adherence to PME practice were not only driven by the presence of PLP but also influenced by patients’ perceived need and social context. The main barriers to therapy adherence were time and availability of single-use electrodes, both of which could be resolved, or attenuated, by informed design considerations. Our findings suggest that adherence to treatment, and thus related outcomes, could be further improved by considering disparate user types and their utilization patterns. Our study highlights the importance of understanding, from multiple disciplinary angles, the tight coupling and interplay between pain, perceived need, and use of medical devices in patient-initiated therapy.
Neuromusculoskeletal Arm Prostheses: Personal and Social Implications of Living With an Intimately Integrated Bionic Arm
How is it like to live with a permanently implanted prosthetic limb? Three people with an implanted prostheses were interviewed about their experiences of living with a prostheses in their daily lives. The participants expressed positive effects on self-esteem, self-image, and social relations. Besides that, the participants indicated enhanced prosthetic function, increased and more diverse prosthesis use in tasks of daily living, and improved relationships between their prosthesis and phantom limb.