| 1. |
- Clemente, Francesco, et al.
(författare)
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Touch and Hearing Mediate Osseoperception
- 2017
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Ingår i: Scientific Reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 7
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Tidskriftsartikel (refereegranskat)abstract
- Osseoperception is the sensation arising from the mechanical stimulation of a bone-anchored prosthesis. Here we show that not only touch, but also hearing is involved in this phenomenon. Using mechanical vibrations ranging from 0.1 to 6 kHz, we performed four psychophysical measures (perception threshold, sensation discrimination, frequency discrimination and reaction time) on 12 upper and lower limb amputees and found that subjects: consistently reported perceiving a sound when the stimulus was delivered at frequencies equal to or above 400 Hz; were able to discriminate frequency differences between stimuli delivered at high stimulation frequencies (similar to 1500 Hz); improved their reaction time for bimodal stimuli (i.e. when both vibration and sound were perceived). Our results demonstrate that osseoperception is a multisensory perception, which can explain the improved environment perception of bone-anchored prosthesis users. This phenomenon might be exploited in novel prosthetic devices to enhance their control, thus ultimately improving the amputees' quality of life.
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| 2. |
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| 3. |
- Lendaro, Eva, 1989, et al.
(författare)
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Phantom motor execution as a treatment for phantom limb pain: Protocol of an international, double-blind, randomised controlled clinical trial
- 2018
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Ingår i: BMJ Open. - : BMJ. - 2044-6055 .- 2044-6055. ; 8:7
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Tidskriftsartikel (refereegranskat)abstract
- Introduction Phantom limb pain (PLP) is a chronic condition that can greatly diminish quality of life. Control over the phantom limb and exercise of such control have been hypothesised to reverse maladaptive brain changes correlated to PLP. Preliminary investigations have shown that decoding motor volition using myoelectric pattern recognition, while providing real-time feedback via virtual and augmented reality (VR-AR), facilitates phantom motor execution (PME) and reduces PLP. Here we present the study protocol for an international (seven countries), multicentre (nine clinics), double-blind, randomised controlled clinical trial to assess the effectiveness of PME in alleviating PLP. Methods and analysis Sixty-seven subjects suffering from PLP in upper or lower limbs are randomly assigned to PME or phantom motor imagery (PMI) interventions. Subjects allocated to either treatment receive 15 interventions and are exposed to the same VR-AR environments using the same device. The only difference between interventions is whether phantom movements are actually performed (PME) or just imagined (PMI). Complete evaluations are conducted at baseline and at intervention completion, as well as 1, 3 and 6 months later using an intention-to-treat (ITT) approach. Changes in PLP measured using the Pain Rating Index between the first and last session are the primary measure of efficacy. Secondary outcomes include: Frequency, duration, quality of pain, intrusion of pain in activities of daily living and sleep, disability associated to pain, pain self-efficacy, frequency of depressed mood, presence of catastrophising thinking, health-related quality of life and clinically significant change as patient's own impression. Follow-up interviews are conducted up to 6 months after the treatment. Ethics and dissemination The study is performed in agreement with the Declaration of Helsinki and under approval by the governing ethical committees of each participating clinic. The results will be published according to the Consolidated Standards of Reporting Trials guidelines in a peer-reviewed journal.
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| 4. |
- Ortiz Catalan, Max Jair, 1982, et al.
(författare)
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Phantom motor execution facilitated by machine learning and augmented reality as treatment for phantom limb pain: a single group, clinical trial in patients with chronic intractable phantom limb pain
- 2016
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Ingår i: The Lancet. - : Elsevier BV. - 1474-547X .- 0140-6736. ; 388:10062, s. 2885-2894
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Tidskriftsartikel (refereegranskat)abstract
- Background Phantom limb pain is a debilitating condition for which no eff ective treatment has been found. We hypothesised that re-engagement of central and peripheral circuitry involved in motor execution could reduce phantom limb pain via competitive plasticity and reversal of cortical reorganisation. Methods Patients with upper limb amputation and known chronic intractable phantom limb pain were recruited at three clinics in Sweden and one in Slovenia. Patients received 12 sessions of phantom motor execution using machine learning, augmented and virtual reality, and serious gaming. Changes in intensity, frequency, duration, quality, and intrusion of phantom limb pain were assessed by the use of the numeric rating scale, the pain rating index, the weighted pain distribution scale, and a study-specifi c frequency scale before each session and at follow-up interviews 1, 3, and 6 months after the last session. Changes in medication and prostheses were also monitored. Results are reported using descriptive statistics and analysed by non-parametric tests. The trial is registered at ClinicalTrials. gov, number NCT02281539. Findings Between Sept 15, 2014, and April 10, 2015, 14 patients with intractable chronic phantom limb pain, for whom conventional treatments failed, were enrolled. After 12 sessions, patients showed statistically and clinically signifi cant improvements in all metrics of phantom limb pain. Phantom limb pain decreased from pre-treatment to the last treatment session by 47% (SD 39; absolute mean change 1 . 0 [0 . 8]; p= 0 . 001) for weighted pain distribution, 32% (38; absolute mean change 1 . 6 [1 . 8]; p= 0 . 007) for the numeric rating scale, and 51% (33; absolute mean change 9 . 6 [8 . 1]; p= 0 . 0001) for the pain rating index. The numeric rating scale score for intrusion of phantom limb pain in activities of daily living and sleep was reduced by 43% (SD 37; absolute mean change 2 . 4 [2 . 3]; p= 0 . 004) and 61% (39; absolute mean change 2 . 3 [1 . 8]; p= 0 . 001), respectively. Two of four patients who were on medication reduced their intake by 81% (absolute reduction 1300 mg, gabapentin) and 33% (absolute reduction 75 mg, pregabalin). Improvements remained 6 months after the last treatment. Interpretation Our fi ndings suggest potential value in motor execution of the phantom limb as a treatment for phantom limb pain. Promotion of phantom motor execution aided by machine learning, augmented and virtual reality, and gaming is a non-invasive, non-pharmacological, and engaging treatment with no identified side-effects at present.
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| 5. |
- Ortiz-Catalan, Max, et al.
(författare)
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Phantom motor execution facilitated by machine learning and augmented reality as treatment for phantom limb pain : a single group, clinical trial in patients with chronic intractable phantom limb pain
- 2016
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Ingår i: The Lancet. - : Elsevier. - 0140-6736 .- 1474-547X. ; 388:10062, s. 2885-2894
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Tidskriftsartikel (refereegranskat)abstract
- Background: Phantom limb pain is a debilitating condition for which no eff ective treatment has been found. We hypothesised that re-engagement of central and peripheral circuitry involved in motor execution could reduce phantom limb pain via competitive plasticity and reversal of cortical reorganisation.Methods: Patients with upper limb amputation and known chronic intractable phantom limb pain were recruited at three clinics in Sweden and one in Slovenia. Patients received 12 sessions of phantom motor execution using machine learning, augmented and virtual reality, and serious gaming. Changes in intensity, frequency, duration, quality, and intrusion of phantom limb pain were assessed by the use of the numeric rating scale, the pain rating index, the weighted pain distribution scale, and a study-specifi c frequency scale before each session and at follow-up interviews 1, 3, and 6 months after the last session. Changes in medication and prostheses were also monitored. Results are reported using descriptive statistics and analysed by non-parametric tests. The trial is registered at ClinicalTrials. gov, number NCT02281539.Findings: Between Sept 15, 2014, and April 10, 2015, 14 patients with intractable chronic phantom limb pain, for whom conventional treatments failed, were enrolled. After 12 sessions, patients showed statistically and clinically signifi cant improvements in all metrics of phantom limb pain. Phantom limb pain decreased from pre-treatment to the last treatment session by 47% (SD 39; absolute mean change 1 . 0 [0 . 8]; p= 0 . 001) for weighted pain distribution, 32% (38; absolute mean change 1 . 6 [1 . 8]; p= 0 . 007) for the numeric rating scale, and 51% (33; absolute mean change 9 . 6 [8 . 1]; p= 0 . 0001) for the pain rating index. The numeric rating scale score for intrusion of phantom limb pain in activities of daily living and sleep was reduced by 43% (SD 37; absolute mean change 2 . 4 [2 . 3]; p= 0 . 004) and 61% (39; absolute mean change 2 . 3 [1 . 8]; p= 0 . 001), respectively. Two of four patients who were on medication reduced their intake by 81% (absolute reduction 1300 mg, gabapentin) and 33% (absolute reduction 75 mg, pregabalin). Improvements remained 6 months after the last treatment.Interpretation: Our fi ndings suggest potential value in motor execution of the phantom limb as a treatment for phantom limb pain. Promotion of phantom motor execution aided by machine learning, augmented and virtual reality, and gaming is a non-invasive, non-pharmacological, and engaging treatment with no identifi ed side-eff ects at present.
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| 6. |
- Abbaspour Asadollah, Sara, et al.
(författare)
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Evaluation of surface EMG-based recognition algorithms for decoding hand movements
- 2019
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Ingår i: Medical and Biological Engineering and Computing. - : Springer. - 0140-0118 .- 1741-0444. ; 58:1, s. 83-100
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Tidskriftsartikel (refereegranskat)abstract
- Myoelectric pattern recognition (MPR) to decode limb movements is an important advancement regarding the control of powered prostheses. However, this technology is not yet in wide clinical use. Improvements in MPR could potentially increase the functionality of powered prostheses. To this purpose, offline accuracy and processing time were measured over 44 features using six classifiers with the aim of determining new configurations of features and classifiers to improve the accuracy and response time of prosthetics control. An efficient feature set (FS: waveform length, correlation coefficient, Hjorth Parameters) was found to improve the motion recognition accuracy. Using the proposed FS significantly increased the performance of linear discriminant analysis, K-nearest neighbor, maximum likelihood estimation (MLE), and support vector machine by 5.5%, 5.7%, 6.3%, and 6.2%, respectively, when compared with the Hudgins’ set. Using the FS with MLE provided the largest improvement in offline accuracy over the Hudgins feature set, with minimal effect on the processing time. Among the 44 features tested, logarithmic root mean square and normalized logarithmic energy yielded the highest recognition rates (above 95%). We anticipate that this work will contribute to the development of more accurate surface EMG-based motor decoding systems for the control prosthetic hands. [Figure not available: see fulltext.].
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| 7. |
- Abbaspour, S., et al.
(författare)
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Real-Time and Offline Evaluation of Myoelectric Pattern Recognition for the Decoding of Hand Movements
- 2021
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Ingår i: Sensors. - : MDPI AG. - 1424-8220. ; 21:16
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 8. |
- Ackerley, Rochelle, 1980, et al.
(författare)
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Case Studies in Neuroscience: Sensations elicited and discrimination ability from nerve cuff stimulation in an amputee over time
- 2018
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Ingår i: Journal of Neurophysiology. - : American Physiological Society. - 0022-3077 .- 1522-1598. ; 120:1, s. 291-295
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Tidskriftsartikel (refereegranskat)abstract
- The present case study details sensations elicited by electrical stimulation of peripheral nerve axons using an implanted nerve cuff electrode, in a participant with a transhumeral amputation. The participant uses an osseointegrated electromechanical interface, which enables skeletal attachment of the prosthesis and long-term, stable, bidirectional communication between the implanted electrodes and prosthetic arm. We focused on evoking somatosensory percepts, where we tracked and quantified the evolution of perceived sensations in the missing hand. which were evoked from electrical stimulation of the nerve, for over 2 yr. These sensations included small, pointlike areas of either vibration or pushing, to larger sensations over wider areas, indicating the recruitment of a few and many afferents, respectively. Furthermore, we used a two-alternative forced choice paradigm to measure the level of discrimination between trains of brief electrical stimuli, to gauge what the participant could reliably distinguish between. At best, the participant was able to distinguish a 05-Hz difference and on average acquired a 3.8-Hz just-noticeable difference at a more stringent psychophysical level. The current work shows the feasibility for long-term sensory feedback in prostheses, via electrical axonal stimulation, where small and relatively stable percepts were felt that may be used to deliver graded sensory feedback. This opens up opportunities for signaling feedback during movements (e.g., for precision grip), but also for conveying more complex cutaneous sensations. such as texture. NEW & NOTEWORTHY We demonstrate the long-term stability and generation of sensations from electrical peripheral nerve stimulation in an amputee. through an osseointegrated implant. We find that perceived tactilelike sensations could be generated for over 2 yr. in the missing hand. This is useful for prosthetic development and the implementation of feedback in artificial body parts.
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| 9. |
- Ahkami, Bahareh, et al.
(författare)
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Electromyography-Based Control of Lower Limb Prostheses: A Systematic Review
- 2023
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Ingår i: IEEE Transactions on Medical Robotics and Bionics. - 2576-3202. ; 5:3, s. 547-562
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Tidskriftsartikel (refereegranskat)abstract
- Most amputations occur in lower limbs and despite improvements in prosthetic technology, no commercially available prosthetic leg uses electromyography (EMG) information as an input for control. Efforts to integrate EMG signals as part of the control strategy have increased in the last decade. In this systematic review, we summarize the research in the field of lower limb prosthetic control using EMG. Four different online databases were searched until June 2022: Web of Science, Scopus, PubMed, and Science Direct. We included articles that reported systems for controlling a prosthetic leg (with an ankle and/or knee actuator) by decoding gait intent using EMG signals alone or in combination with other sensors. A total of 1,331 papers were initially assessed and 121 were finally included in this systematic review. The literature showed that despite the burgeoning interest in research, controlling a leg prosthesis using EMG signals remains challenging. Specifically, regarding EMG signal quality and stability, electrode placement, prosthetic hardware, and control algorithms, all of which need to be more robust for everyday use. In the studies that were investigated, large variations were found between the control methodologies, type of research participant, recording protocols, assessments, and prosthetic hardware.
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| 10. |
- Ahkami, Bahareh, 1994, et al.
(författare)
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Extra-neural signals from severed nerves enable intrinsic hand movements in transhumeral amputations
- 2022
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Ingår i: Scientific Reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 12:1
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Tidskriftsartikel (refereegranskat)abstract
- Robotic prostheses controlled by myoelectric signals can restore limited but important hand function in individuals with upper limb amputation. The lack of individual finger control highlights the yet insurmountable gap to fully replacing a biological hand. Implanted electrodes around severed nerves have been used to elicit sensations perceived as arising from the missing limb, but using such extra-neural electrodes to record motor signals that allow for the decoding of phantom movements has remained elusive. Here, we showed the feasibility of using signals from non-penetrating neural electrodes to decode intrinsic hand and finger movements in individuals with above-elbow amputations. We found that information recorded with extra-neural electrodes alone was enough to decode phantom hand and individual finger movements, and as expected, the addition of myoelectric signals reduced classification errors both in offline and in real-time decoding.
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