Return to Work Following Major Limb Loss

Exploring the need for lower limb prosthetic guidelines in South Africa's private healthcare sector

BACKGROUND

Evidence based guidelines can assist with prosthetic component selection and clinical intervention. There is limited evidence on lower limb prosthetic prescription guidelines in the South African private health care sector.

OBJECTIVE

To explore the need for lower limb prosthetic prescription guidelines in the South African private healthcare sector.

METHODOLOGY

Three main funders of lower limb prosthetics in the South African private healthcare sector (Road Accident Fund (RAF), Workmen's Compensation Fund (WCA), and Council of Medical Schemes (CMS)) were explored using a case study design. Data were collected from six regulatory documents, sixteen purposively sampled prosthetic users, who received services from these funders, and seven key informants. Documents were assessed with the Appraisal of Guidelines for Research & Evaluation II (AGREE II), across six domains. Data from users and key informants were collected with telephonic, semi-structured interviews guided by interview schedules. Interview schedules were self-developed and tailored for each participant group. AGREE II data were analyzed descriptively. Inductive thematic analysis was used for interview data.

FINDINGS

Across cases, the "Scope and Purpose" domain scored the highest: 50% (WCA), 47% (CMS), and 22% (RAF). "Editorial Independence" scored 0% for all three cases. Other challenging domains were "Applicability" (WCA: 17%, CMS: 6%, RAF: 6%) and "Rigour of Development" (WCA: 8%, CMS: 25%, RAF: 0%). The following three cross-case themes emerged from the interviews: "Guideline Availability and Necessity" showed that guidelines were seldom used and that guidelines could be beneficial; "Purpose of a Lower Limb Prosthetic Guideline" indicated that guidelines can support accessible, equitable, ethical, and transparent services; and "Guideline Development Requirements" explained that an evidence based collaborative process, facilitated by an independent body should underscore guideline development.

CONCLUSION

Evidence based, standardized, transparent guidelines will be beneficial to direct prosthetic service delivery in the South African private healthcare sector. The guidelines must be applicable, rigorously developed, and show editorial independence.

A Novel Sensory Feedback Approach to Facilitate Both Predictive and Corrective Control of Grasping Force in Myoelectric Prostheses

Reliable force control is especially important when using myoelectric upper-limb prostheses as the force defines whether an object will be firmly grasped, damaged, or dropped. It is known from human motor control that the grasping of non-disabled subjects is based on a combination of anticipation and feedback correction. Inspired by this insight, the present study proposes a novel approach to provide artificial sensory feedback to the user of a myoelectric prosthesis using vibrotactile stimulation to facilitate both predictive and corrective processes characteristic of grasping in non-disabled people. Specifically, the level of EMG was conveyed to the subjects while closing the prosthesis (predictive strategy), whereas the actual grasping force was transmitted when the prosthesis closed (corrective strategy). To investigate if this combined EMG and force feedback is indeed an effective method to explicitly close the control loop, 16 non-disabled and 3 transradial amputee subjects performed a set of functional tasks, inspired by the "Box and Block" test, with six target force levels, in three conditions: no feedback, only EMG feedback, and combined feedback. The highest overall performance in non-disabled subjects was obtained with combined feedback (79.6±9.9%), whereas the lowest was achieved with no feedback (53±11.5%). The combined feedback, however, increased the task completion time compared to the other two conditions. A similar trend was obtained also in three amputee subjects. The results, therefore, indicate that the feedback inspired by human motor control is indeed an effective approach to improve prosthesis grasping in realistic conditions when other sources of feedback (vision and audition) are not blocked.

Nonlinear Mapping From EMG to Prosthesis Closing Velocity Improves Force Control With EMG Biofeedback

When using EMG biofeedback to control the grasping force of a myoelectric prosthesis, subjects need to activate their muscles and maintain the myoelectric signal within an appropriate interval. However, their performance decreases for higher forces, because the myoelectric signal is more variable for stronger contractions. Therefore, the present study proposes to implement EMG biofeedback using nonlinear mapping, in which EMG intervals of increasing size are mapped to equal-sized intervals of the prosthesis velocity. To validate this approach, 20 non-disabled subjects performed force-matching tasks using Michelangelo prosthesis with and without EMG biofeedback with linear and nonlinear mapping. Additionally, four transradial amputees performed a functional task in the same feedback and mapping conditions. The success rate in producing desired force was significantly higher with feedback (65.4±15.9%) compared to no feedback (46.2±14.9%) as well as when using nonlinear (62.4±16.8%) versus linear mapping (49.2±17.2%). Overall, in non-disabled subjects, the highest success rate was obtained when EMG biofeedback was combined with nonlinear mapping (72%), and the opposite for linear mapping with no feedback (39.6%). The same trend was registered also in four amputee subjects. Therefore, EMG biofeedback improved prosthesis force control, especially when combined with nonlinear mapping, which showed to be an effective approach to counteract increasing variability of myoelectric signal for stronger contractions.

A review of user needs to drive the development of lower limb prostheses

Background

The development of bionic legs has seen substantial improvements in the past years but people with lower-limb amputation still suffer from impairments in mobility (e.g., altered balance and gait control) due to significant limitations of the contemporary prostheses. Approaching the problem from a human-centered perspective by focusing on user-specific needs can allow identifying critical improvements that can increase the quality of life. While there are several reviews of user needs regarding upper limb prostheses, a comprehensive summary of such needs for those affected by lower limb loss does not exist.

Methods

We have conducted a systematic review of the literature to extract important needs of the users of lower-limb prostheses. The review included 56 articles in which a need (desire, wish) was reported explicitly by the recruited people with lower limb amputation (N = 8149).

Results

An exhaustive list of user needs was collected and subdivided into functional, psychological, cognitive, ergonomics, and other domain. Where appropriate, we have also briefly discussed the developments in prosthetic devices that are related to or could have an impact on those needs. In summary, the users would like to lead an independent life and reintegrate into society by coming back to work and participating in social and leisure activities. Efficient, versatile, and stable gait, but also support to other activities (e.g., sit to stand), contribute to safety and confidence, while appearance and comfort are important for the body image. However, the relation between specific needs, objective measures of performance, and overall satisfaction and quality of life is still an open question.

Conclusions

Identifying user needs is a critical step for the development of new generation lower limb prostheses that aim to improve the quality of life of their users. However, this is not a simple task, as the needs interact with each other and depend on multiple factors (e.g., mobility level, age, gender), while evolving in time with the use of the device. Hence, novel assessment methods are required that can evaluate the impact of the system from a holistic perspective, capturing objective outcomes but also overall user experience and satisfaction in the relevant environment (daily life).

The effect of calibration parameters on the control of a myoelectric hand prosthesis using EMG feedback

Objective.The implementation of somatosensory feedback in upper limb myoelectric prostheses is an important step towards the restoration of lost sensory-motor functions. EMG feedback is a recently proposed method for closing the control loop wherein the myoelectric signal that drives the prosthesis is also used to generate the feedback provided to the user. Therefore, the characteristics of the myoelectric signal (variability and sensitivity) are likely to significantly affect the ability of the subject to utilize this feedback for online control of the prosthesis.Approach.In the present study, we investigated how the cutoff frequency of the low-pass filter (0.5, 1 and 1.5 Hz) and normalization value (20%, 40% and 60% of the maximum voluntary contraction (MVC)), that are used for the generation of the myoelectric signal, affect the quality of closed-loop control with EMG feedback. Lower cutoff and normalization decrease the intrinsic variability of the EMG but also increase the time lag between the contraction and the feedback (cutoff) as well as the sensitivity of the myoelectric signal (normalization). Ten participants were asked to generate three grasp force levels with a myoelectric prosthetic hand, while receiving five-level vibrotactile EMG feedback, over nine experimental runs (all parameter combinations).Main results.The outcome measure was the success rate (SR) in achieving the appropriate level of myoelectric signal (primary outcome) and grasping force (secondary outcome). Overall, the experiments demonstrated that EMG feedback provided robust control across conditions. Nevertheless, the performance was significantly better for the lowest cutoff (0.5 Hz) and higher normalization (40% and 60%). The highest SR for the EMG was 71.9%, achieved in the condition (40% MVC and 0.5 Hz), and this was 24.1% higher than that in the condition (20% MVC and 1.5 Hz), which resulted in the lowest performance. The SR for the force followed a similar trend.Significance.This is the first study that systematically explored the parameter space for the calibration of EMG feedback, which is a critical step for the future clinical application of this approach.

Economic value of orthotic and prosthetic services among medicare beneficiaries: a claims-based retrospective cohort study, 2011-2014

Background

There are few studies of the economic value of orthotic and prosthetic services. A prior cohort study of orthotic and prosthetic Medicare beneficiaries based on Medicare Parts A and B claims from 2007 to 2010 concluded that patients who received timely orthotic or prosthetic care had comparable or lower total health care costs than a comparison group of untreated patients. This follow-up study reports on a parallel analysis based on Medicare claims from 2011 to 2014 and includes Part D in addition to Parts A and B services and expenditures. Its purpose is to validate earlier findings on the extent to which Medicare patients who received select orthotic and prosthetic services had less health care utilization, lower Medicare payments, and potentially fewer negative outcomes compared to matched patients not receiving these services.

Methods

This is a retrospective cohort analysis of 78,707 matched pairs of Medicare beneficiaries with clinical need for orthotic and prosthetic services (N = 157,414) using 2011–2014 Medicare claims data. It uses propensity score matching techniques to control for observable selection bias. Economically, a cost-consequence evaluation over a four-year time horizon was performed.

Results

Patients who received lower extremity orthotics had 18-month episode costs that were $1939 lower than comparable patients who did not receive orthotic treatment ($22,734 vs $24,673). Patients who received spinal orthotic treatment had 18-month episode costs that were $2094 lower than comparable non-treated patients ($23,560 vs $25,655). Study group beneficiaries receiving both types of orthotics had significantly lower Part D spending than those not receiving treatment (p < 0.05). Patients who received lower extremity prostheses had comparable 15-month episode payments to matched beneficiaries not receiving prostheses ($68,877 vs $68,893) despite the relatively high cost of the prosthesis.

Conclusions

These results were consistent with those found in the prior study and suggest that orthotic and prosthetic services provide value to the Medicare program and to the patient.

Resolving the effect of wrist position on myoelectric pattern recognition control

Background

The use of pattern recognition-based methods to control myoelectric upper-limb prostheses has been well studied in individuals with high-level amputations but few studies have demonstrated that it is suitable for partial-hand amputees, who often possess a functional wrist. This study’s objective was to evaluate strategies that allow partial-hand amputees to control a prosthetic hand while allowing retain wrist function.

Methods

EMG data was recorded from the extrinsic and intrinsic hand muscles of six non-amputees and two partial-hand amputees while they performed 4 hand motions in 13 different wrist positions. The performance of 4 classification schemes using EMG data alone and EMG data combined with wrist positional information was evaluated. Using recorded wrist positional data, the relationship between EMG features and wrist position was modeled and used to develop a wrist position-independent classification scheme.

Results

A multi-layer perceptron artificial neural network classifier was better able to discriminate four hand motion classes in 13 wrist positions than a linear discriminant analysis classifier (p = 0.006), quadratic discriminant analysis classifier (p < 0.0001) and a linear perceptron artificial neural network classifier (p = 0.04). The addition of wrist position data to EMG data significantly improved performance (p < 0.001). Training the classifier with the combination of extrinsic and intrinsic muscle EMG data performed significantly better than using intrinsic (p < 0.0001) or extrinsic muscle EMG data alone (p < 0.0001), and training with intrinsic muscle EMG data performed significantly better than extrinsic muscle EMG data alone (p < 0.001). The same trends were observed for amputees, except training with intrinsic muscle EMG data, on average, performed worse than the extrinsic muscle EMG data. We propose a wrist position–independent controller that simulates data from multiple wrist positions and is able to significantly improve performance by 48–74% (p < 0.05) for non-amputees and by 45–66% for partial-hand amputees, compared to a classifier trained only with data from a neutral wrist position and tested with data from multiple positions.

Conclusions

Sensor fusion (using EMG and wrist position information), non-linear artificial neural networks, combining EMG data across multiple muscle sources, and simulating data from different wrist positions are effective strategies for mitigating the wrist position effect and improving classification performance.

Evaluating EMG Feature and Classifier Selection for Application to Partial-Hand Prosthesis Control

Pattern recognition-based myoelectric control of upper-limb prostheses has the potential to restore control of multiple degrees of freedom. Though this control method has been extensively studied in individuals with higher-level amputations, few studies have investigated its effectiveness for individuals with partial-hand amputations. Most partial-hand amputees retain a functional wrist and the ability of pattern recognition-based methods to correctly classify hand motions from different wrist positions is not well studied. In this study, focusing on partial-hand amputees, we evaluate (1) the performance of non-linear and linear pattern recognition algorithms and (2) the performance of optimal EMG feature subsets for classification of four hand motion classes in different wrist positions for 16 non-amputees and 4 amputees. Our results show that linear discriminant analysis and linear and non-linear artificial neural networks perform significantly better than the quadratic discriminant analysis for both non-amputees and partial-hand amputees. For amputees, including information from multiple wrist positions significantly decreased error (p < 0.001) but no further significant decrease in error occurred when more than 4, 2, or 3 positions were included for the extrinsic (p = 0.07), intrinsic (p = 0.06), or combined extrinsic and intrinsic muscle EMG (p = 0.08), respectively. Finally, we found that a feature set determined by selecting optimal features from each channel outperformed the commonly used time domain (p < 0.001) and time domain/autoregressive feature sets (p < 0.01). This method can be used as a screening filter to select the features from each channel that provide the best classification of hand postures across different wrist positions.