Upper limb activity in myoelectric prosthesis users is biased towards the intact limb and appears unrelated to goal-directed task performance

Chadwell, Alix; Kenney, Laurence; Granat, Malcolm H.; Thies, Sibylle B.; Head, John; Galpin, Adam; Baker, Richard; Kulkarni, Jai

doi:10.1038/s41598-018-29503-6

Cited by 50 publications

(90 citation statements)

References 24 publications

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“…58 In line with the findings of the lower-limb papers, the fourth upper-limb paper highlighted the complementary nature of the real-world assessment, emphasising how measures of everyday upper-limb symmetry and prosthesis wear time appear unrelated to performance measured using in-lab approaches. 46 This is a pre-print of the paper 'Technology for monitoring everyday prosthesis use: a systematic review', by Chadwell, Diment, Mico Amigo, Morgado Ramirez et al…”

Section: Figure 1 Flow Chart Of Selection and Sorting Methodsmentioning

confidence: 99%

“…All 4 upper-limb papers which related to the population category were output as part of the same research study; these papers compared the symmetry of upper-limb activity between those with limb-absence and anatomically intact control participants. 33,46,91,92 The results showed that the upper-limb activity of prosthesis users is heavily biased towards the anatomical limb, but in anatomically intact controls activity is quite evenly distributed between the dominant and non-dominant limbs.…”

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confidence: 99%

“…The upper-limb studies varied in the types of sensor used for monitoring activity. These methods of assessing activity were grouped into four topics: (1) use of head-mounted video cameras to generate grasp taxonomies, 32,41 (2) use of wrist-worn accelerometers to measure aspects of symmetry in upper-limb activity and prosthesis wear time, 33,46,91,92 (3) use of on-board sensing to evaluate choice of grasp, 58 and (4) use of on-board sensing to evaluate the use of a sensory feedback system and the number of grasp events. 47,71 During the review process, five other studies evaluating the upper-limb activity of prosthesis users were identified, however these were excluded from the main review due to assessing all upper-limb activity (as opposed to just the use of the prosthesis), 102,103 or because they were only undertaken as lab-based studies.…”

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Technology for monitoring everyday prosthesis use: a systematic review

Chadwell¹,

Diment²,

Amigo³

et al. 2020

Preprint

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BackgroundUnderstanding how prostheses are used in everyday life is central to the design, provision and evaluation of prosthetic devices and associated services. This paper reviews the scientific literature on methodologies and technologies that have been used to assess the daily use of both upper- and lower-limb prostheses. It discusses the types of studies that have been undertaken, the technologies used to monitor physical activity, the benefits of monitoring daily living and the barriers to long-term monitoring.MethodsA systematic literature search was conducted in PubMed, Web of Science, Scopus, CINAHL and EMBASE of studies that monitored the activity of prosthesis-users during daily-living.Results60 lower-limb studies and 9 upper-limb studies were identified for inclusion in the review. The first studies in the lower-limb field date from the 1990s and the number has increased steadily since the early 2000s. In contrast, the studies in the upper-limb field have only begun to emerge over the past few years. The early lower-limb studies focused on the development or validation of actimeters, algorithms and/or scores for activity classification. However, most of the recent lower-limb studies used activity monitoring to compare prosthetic components. The lower-limb studies mainly used step-counts as their only measure of activity, focusing on the amount of activity, not the type and quality of movements. In comparison, the small number of upper-limb studies were fairly evenly spread between development of algorithms, comparison of everyday activity to clinical scores, and comparison of different prosthesis user populations. Most upper-limb papers reported the degree of symmetry in activity levels between the arm with the prosthesis and the intact arm.ConclusionsActivity monitoring technology used in conjunction with clinical scores and user feedback, offers significant insights into how prostheses are used and whether they meet the user’s requirements. However, the cost, limited battery-life and lack of availability in many countries mean that using sensors to understand the daily use of prostheses and the types of activity being performed has not yet become a feasible standard clinical practice. This review provides recommendations for the research and clinical communities to advance this area for the benefit of prosthesis users.

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Section: Figure 1 Flow Chart Of Selection and Sorting Methodsmentioning

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Technology for monitoring everyday prosthesis use: a systematic review

Chadwell¹,

Diment²,

Amigo³

et al. 2020

Preprint

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“…With this equipment, they were required to perform a set of bimanual activities of daily living (ADLs) in a simulated domestic environment. We intentionally selected bilateral prostheses and activities to avoid the pitfall of users over-relying on their unaffected limb to execute the activities [17]. Furthermore, this also ensures that our study applies equally to a teleoperation scenario.…”

Section: Introductionmentioning

confidence: 99%

Natural Myocontrol in a Realistic Setting: a Comparison Between Static and Dynamic Data Acquisition

Gigli

Gijsberts

Castellini

2019

2019 IEEE 16th International Conference on Rehabilitation Robotics (ICORR)

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Natural myocontrol employs pattern recognition to allow users to control a robotic limb intuitively using their own voluntary muscular activations. The reliability of myocontrol strongly depends on the signals initially collected from the users, which must appropriately capture the variability encountered later on during operation. Since myoelectric signals can vary based on the position and orientation of the limb, it has become best practice to gather data in multiple body postures. We hereby concentrate on this acquisition protocol and investigate the relative merits of collecting data either statically or dynamically. In the static case, data for a desired hand configuration is collected while the users keep their hand still in certain positions, whereas in the dynamic case, data is collected while users move their limbs, passing through the required positions with a roughly constant velocity.Fourteen able-bodied subjects were asked to naturally control two dexterous hand prostheses mounted on splints, performing a set of complex, realistic bimanual activities of daily living. We could not find any significant difference between the protocols in terms of the total execution times, although the dynamic data acquisition was faster and less tiring. This would indicate that dynamic data acquisition should be preferred over the static one.

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“…Individuals with limb loss may be further disadvantaged by motor cortex reorganization following amputation (26), as well as muscle atrophy due to disuse of the residual limb and/or increased reliance on the intact limb (27). Given these difficulties, it is unsurprising that first attempts to use pattern recognition are often error-prone.…”

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confidence: 99%

Individuals With Upper Limb Loss Require Minimal Training to Achieve Robust Motion Classification Using Sonomyography

Engdahl¹,

Dhawan²,

Bashatah³

et al. 2020

Preprint

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Background: Although surface electromyography is commonly used as a sensing strategy for upper limb prostheses, it remains difficult to reliably decode the recorded signals for controlling multi-articulated hands. Sonomyography, or ultrasound-based sensing of muscle deformation, overcomes some of these issues and allows individuals with upper limb loss to reliably perform multiple motion patterns. The purposes of this study were to determine 1) the effect of training on classification performance with sonomyographic control and 2) the effect of training on the underlying muscle deformation patterns.Methods: A series of motion pattern datasets were collected from five individuals with transradial limb loss. Each dataset contained five ultrasound images corresponding each of the following five motions: power grasp, wrist pronation, key grasp, tripod, point. Participants initially performed the motions for the datasets without receiving feedback on their performance (baseline phase), then with visual and verbal feedback (feedback phase), and finally again without feedback (retention phase). Cross-validation accuracy and metrics describing the consistency and separability of the muscle deformation patters were computed for each dataset. Changes in classification performance over the course of the study were assessed using linear mixed models. Associations between classification performance and the consistency and separability metrics were evaluated using Pearson correlations.Results: The average cross-validation accuracy for each phase was 92% or greater and there was no significant change in cross-validation accuracy throughout training. Misclassifications of one motion as another did not persist systematically across datasets. Few of the correlations were significant, although many were moderate or greater in strength and showed a positive association between accuracy and improved consistency and separability metrics.Conclusions: Participants were able to achieve high classification rates upon their initial exposure to sonomyography and training did not affect their performance. Thus, motion classification using sonomyography may be highly intuitive and is unlikely to require a structured training protocol to gain proficiency.

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Upper limb activity in myoelectric prosthesis users is biased towards the intact limb and appears unrelated to goal-directed task performance

Cited by 50 publications

References 24 publications

Technology for monitoring everyday prosthesis use: a systematic review

Technology for monitoring everyday prosthesis use: a systematic review

Natural Myocontrol in a Realistic Setting: a Comparison Between Static and Dynamic Data Acquisition

Individuals With Upper Limb Loss Require Minimal Training to Achieve Robust Motion Classification Using Sonomyography

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