Control Strategies and Performance Assessment of Upper-Limb TMR Prostheses: A Review

A National Analysis of Targeted Muscle Reinnervation following Major Upper Extremity Amputation

BACKGROUND

Postamputation pain is a debilitating sequela of upper extremity (UE) amputation. Targeted muscle reinnervation (TMR) is a relatively novel treatment that can help prevent pain and improve quality of life. The purpose of this study was to evaluate national trends in the application of immediate TMR following UE amputations.

METHODS

An analysis of the Nationwide Inpatient Sample database was conducted from 2016 to 2019. International Classification of Diseases, 10th Revision, codes were used to identify encounters involving UE amputation with and without TMR. Nationwide Inpatient Sample weights were used to estimate national estimates of incidence. Patient-specific and hospital-specific factors were analyzed to assess associations with use of TMR.

RESULTS

A total of 8945 weighted encounters underwent UE amputation, and of those, only 310 (3.5%) received TMR. The majority of TMR occurred in urban hospitals (>95%). Younger patients (47 years versus 54 years; P = 0.008) and patients located in New England were significantly more likely to undergo TMR. There was no difference in total cost of hospitalization among patients who underwent TMR ($55,241.0 versus $59,027.8; P = 0.683) but significantly shorter lengths of hospital stay when undergoing TMR versus other management (10.6 days versus 14.8 days; P = 0.012).

CONCLUSIONS

TMR has purported benefits of pain reduction, neuroma prevention, and increased prosthetic control. Access to this beneficial procedure following UE amputation varies by demographics and geographic region. Given that TMR has not been shown to increase cost while simultaneously decreasing patient length of stay, increased efforts to incorporate this procedure into training and practice will help to ensure equitable care for amputation patients.

Agonist-antagonist myoneural interface surgery on the proprioceptive reconstruction of rat hind limb

Currently, prosthesis users rely on visual cues to control their prosthesis. One reason for this is that prostheses cannot provide users with proprioceptive functional signals. For this reason, we propose an agonist-antagonist myoneural interface (AMI) surgery. We examined how this surgery affects the restoration of motor function and proprioceptive reconstruction in the hind limb of Sprague-Dawley rats. The procedure entails grafting the soleus muscle, suturing the two tendon ends of the soleus muscle, and anastomosing the tibial and common peroneal nerves to the soleus muscle. We found that, following surgery, AMI rats exhibited improved neurological repair, shorter walking swings, braking, propulsion, and stance times, and greater compound action potentials than control rats. This means that in rats with neurological impairment of the hind limb, the proposed AMI surgical method significantly improves postoperative walking stability and muscle synergy. AMI surgery may become an option for regaining proprioception in the lost limb.

Comparing online wrist and forearm EMG-based control using a rhythm game-inspired evaluation environment

Objective.The use of electromyogram (EMG) signals recorded from the wrist is emerging as a desirable input modality for human-machine interaction (HMI). Although forearm-based EMG has been used for decades in prosthetics, there has been comparatively little prior work evaluating the performance of wrist-based control, especially in online, user-in-the-loop studies. Furthermore, despite different motivating use cases for wrist-based control, research has mostly adopted legacy prosthesis control evaluation frameworks.Approach.Gaining inspiration from rhythm games and the Schmidt's law speed-accuracy tradeoff, this work proposes a new temporally constrained evaluation environment with a linearly increasing difficulty to compare the online usability of wrist and forearm EMG. Compared to the more commonly used Fitts' Law-style testing, the proposed environment may offer different insights for emerging use cases of EMG as it decouples the machine learning algorithm's performance from proportional control, is easily generalizable to different gesture sets, and enables the extraction of a wide set of usability metrics that describe a users ability to successfully accomplish a task at a certain time with different levels of induced stress.Main results.The results suggest that wrist EMG-based control is comparable to that of forearm EMG when using traditional prosthesis control gestures and can even be better when using fine finger gestures. Additionally, the results suggest that as the difficulty of the environment increased, the online metrics and their correlation to the offline metrics decreased, highlighting the importance of evaluating myoelectric control in real-time evaluations over a range of difficulties.Significance.This work provides valuable insights into the future design and evaluation of myoelectric control systems for emerging HMI applications.

Eliciting Force and Slippage in Upper Limb Amputees Through Transcutaneous Electrical Nerve Stimulation (TENS)

Upper limb amputation severely affects the quality of life of individuals. Therefore, developing closed-loop upper-limb prostheses would enhance the sensory-motor capabilities of the prosthetic user. Considering design priorities based on user needs, the restoration of sensory feedback is one of the most desired features. This study focuses on employing Transcutaneous Electrical Nerve Stimulation (TENS) as a non-invasive somatotopic stimulation technique for restoring somatic sensations in upper-limb amputees. The aim of this study is to propose two encoding strategies to elicit force and slippage sensations in transradial amputees. The former aims at restoring three different levels of force through a Linear Pulse Amplitude Modulation (LPAM); the latter is devoted to elicit slippage sensations through Apparent Moving Sensation (AMS) by means of three different algorithms, i.e. the Pulse Amplitude Variation (PAV), the Pulse Width Variation (PWV) and Inter-Stimulus Delay Modulation (ISDM). Amputees had to characterize perceived sensations and to perform force and slippage recognition tasks. Results demonstrates that amputees were able to correctly identify low, medium and high levels of force, with an accuracy above the 80% and similarly, to also discriminate the slippage moving direction with a high accuracy above 90%, also highlighting that ISDM would be the most suitable method, among the three AMS strategies to deliver slippage sensations. It was demonstrated for the first time that the developed encoding strategies are effective methods to somatotopically reintroduce in the amputees, by means of TENS, force and slippage sensations.

A compact solution for vibrotactile proprioceptive feedback of wrist rotation and hand aperture

BACKGROUND

Closing the control loop between users and their prostheses by providing artificial sensory feedback is a fundamental step toward the full restoration of lost sensory-motor functions.

METHODS

We propose a novel approach to provide artificial proprioceptive feedback about two degrees of freedom using a single array of 8 vibration motors (compact solution). The performance afforded by the novel method during an online closed-loop control task was compared to that achieved using the conventional approach, in which the same information was conveyed using two arrays of 8 and 4 vibromotors (one array per degree of freedom), respectively. The new method employed Gaussian interpolation to modulate the intensity profile across a single array of vibration motors (compact feedback) to convey wrist rotation and hand aperture by adjusting the mean and standard deviation of the Gaussian, respectively. Ten able-bodied participants and four transradial amputees performed a target achievement control test by utilizing pattern recognition with compact and conventional vibrotactile feedback to control the Hannes prosthetic hand (test conditions). A second group of ten able-bodied participants performed the same experiment in control conditions with visual and auditory feedback as well as no-feedback.

RESULTS

Conventional and compact approaches resulted in similar positioning accuracy, time and path efficiency, and total trial time. The comparison with control condition revealed that vibrational feedback was intuitive and useful, but also underlined the power of incidental feedback sources. Notably, amputee participants achieved similar performance to that of able-bodied participants.

CONCLUSIONS

The study therefore shows that the novel feedback strategy conveys useful information about prosthesis movements while reducing the number of motors without compromising performance. This is an important step toward the full integration of such an interface into a prosthesis socket for clinical use.

Experience with ultrasound neurography for postoperative evaluation of targeted muscle reinnervation

Targeted muscle reinnervation (TMR) was originally developed as a means for increasing intuitive prosthesis control, though later found to play a role in phantom limb pain and neuroma prevention. There is a paucity of literature describing the clinical course of patients with poor TMR surgical outcomes and the value of imaging in the postoperative recovery period. This report will illustrate the potential utility of ultrasound neurography to accurately differentiate TMR surgical outcomes in two patients that received upper extremity amputation and subsequent reconstruction with TMR. Ultrasound evaluation of TMR sites in patient 1 confirmed successful reinnervation, evident by nerve fascicle continuity and eventual integration of the transferred nerve into the target muscle. Conversely, the ultrasound of patient 2 showed discontinuity of the nerve fascicles, neuroma formation, and muscle atrophy in all three sites of nerve transfer, suggesting an unsuccessful procedure and poor functional recovery. Ultrasound neurography is uniquely able to capture the longitudinal trajectory of rerouted nerves to confirm continuity and eventual reinnervation into muscle. Therefore, the application of ultrasound in a postoperative setting can correctly identify instances of failed TMR before this information would become available through clinical evaluation. Early identification of poor TMR outcomes may benefit future patients by fostering the discovery of failure mechanisms and aiding in further surgical planning to improve functional outcomes.

High Performance Wearable Ultrasound as a Human-Machine Interface for Wrist and Hand Kinematic Tracking

OBJECTIVE

Non-invasive human machine interfaces (HMIs) have high potential in medical, entertainment, and industrial applications. Traditionally, surface electromyography (sEMG) has been used to track muscular activity and infer motor intention. Ultrasound (US) has received increasing attention as an alternative to sEMG-based HMIs. Here, we developed a portable US armband system with 24 channels and a multiple receiver approach, and compared it with existing sEMG- and US-based HMIs on movement intention decoding.

METHODS

US and motion capture data was recorded while participants performed wrist and hand movements of four degrees of freedom (DoFs) and their combinations. A linear regression model was used to offline predict hand kinematics from the US (or sEMG, for comparison) features. The method was further validated in real-time for a 3-DoF target reaching task.

RESULTS

In the offline analysis, the wearable US system achieved an average [Formula: see text] of 0.94 in the prediction of four DoFs of the wrist and hand while sEMG reached a performance of [Formula: see text]= 0.60. In online control, the participants achieved an average 93% completion rate of the targets.

CONCLUSION

When tailored for HMIs, the proposed US A-mode system and processing pipeline can successfully regress hand kinematics both in offline and online settings with performances comparable or superior to previously published interfaces.

SIGNIFICANCE

Wearable US technology may provide a new generation of HMIs that use muscular deformation to estimate limb movements. The wearable US system allowed for robust proportional and simultaneous control over multiple DoFs in both offline and online settings.

Exploring the EMG transient: the muscular activation sequences used as novel time-domain features for hand gestures classification

Introduction

Muscular activation sequences have been shown to be suitable time-domain features for classification of motion gestures. However, their clinical application in myoelectric prosthesis control was never investigated so far. The aim of the paper is to evaluate the robustness of these features extracted from the EMG signal in transient state, on the forearm, for classifying common hand tasks.

Methods

The signal associated to four hand gestures and the rest condition were acquired from ten healthy people and two persons with trans-radial amputation. A feature extraction algorithm allowed for encoding the EMG signals into muscular activation sequences, which were used to train four commonly used classifiers, namely Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), Non-linear Logistic Regression (NLR) and Artificial Neural Network (ANN). The offline performances were assessed with the entire sample of recruited people. The online performances were assessed with the amputee subjects. Moreover, a comparison of the proposed method with approaches based on the signal envelope in the transient state and in the steady state was conducted.

Results

The highest performance were obtained with the NLR classifier. Using the sequences, the offline classification accuracy was higher than 93% for healthy and amputee subjects and always higher than the approach with the signal envelope in transient state. As regards the comparison with the steady state, the performances obtained with the proposed method are slightly lower (<4%), but the classification occurred at least 200 ms earlier. In the online application, the motion completion rate reached up to 85% of the total classification attempts, with a motion selection time that never exceeded 218 ms.

Discussion

Muscular activation sequences are suitable alternatives to the time-domain features commonly used in classification problems belonging to the sole EMG transient state and could be potentially exploited in control strategies of myoelectric prosthesis hands.

A Review on Polymers for Biomedical Applications on Hard and Soft Tissues and Prosthetic Limbs

In the past decades, there has been a significant increase in the use of polymers for biomedical applications. The global medical polymer market size was valued at USD 19.92 billion in 2022 and is expected to grow at a CAGR of 8.0% from 2023 to 2030 despite some limitations, such as cost (financial limitation), strength compared to metal plates for bone fracture, design optimization and incorporation of reinforcement. Recently, this increase has been more pronounced due to important advances in synthesis and modification techniques for the design of novel biomaterials and their behavior in vitro and in vivo. Also, modern medicine allows the use of less invasive surgeries and faster surgical sutures. Besides their use in the human body, polymer biomedical materials must have desired physical, chemical, biological, biomechanical, and degradation properties. This review summarizes the use of polymers for biomedical applications, mainly focusing on hard and soft tissues, prosthetic limbs, dental applications, and bone fracture repair. The main properties, gaps, and trends are discussed.

Hierarchical strategy for sEMG classification of the hand/wrist gestures and forces of transradial amputees

Introduction

The myoelectric control strategy, based on surface electromyographic signals, has long been used for controlling a prosthetic system with multiple degrees of freedom. Several methods classify gestures and force levels but the simultaneous real-time control of hand/wrist gestures and force levels did not yet reach a satisfactory level of effectiveness.

Methods

In this work, the hierarchical classification approach, already validated on 31 healthy subjects, was adapted for the real-time control of a multi-DoFs prosthetic system on 15 trans-radial amputees. The effectiveness of the hierarchical classification approach was assessed by evaluating both offline and real-time performance using three algorithms: Logistic Regression (LR), Non-linear Logistic Regression (NLR), and Linear Discriminant Analysis (LDA).

Results

The results of this study showed the offline performance of amputees was promising and comparable to healthy subjects, with mean F1 scores of over 90% for the “Hand/wrist gestures classifier” and 95% for the force classifiers, implemented with the three algorithms with features extraction (FE). Another significant finding of this study was the feasibility of using the hierarchical classification strategy for real-time applications, due to its ability to provide a response time of 100 ms while maintaining an average online accuracy of above 90%.

Discussion

A possible solution for real-time control of both hand/wrist gestures and force levels is the combined use of the LR algorithm with FE for the "Hand/wrist gestures classifier", and the NLR with FE for the Spherical and Tip force classifiers.

The Role of Fascial Tissue Layer in Electric Signal Transmission from the Forearm Musculature to the Cutaneous Layer as a Possibility for Increased Signal Strength in Myoelectric Forearm Exoprosthesis Development

Myoelectric exoprostheses serve to aid in the everyday activities of patients with forearm or hand amputations. While electrical signals are known key factors controlling exoprosthesis, little is known about how we can improve their transmission strength from the forearm muscles as to obtain better sEMG. The purpose of this study is to evaluate the role of the forearm fascial layer in transmitting myoelectrical current. We examined the sEMG signals in three individual muscles, each from six healthy forearms (Group 1) and six amputation stumps (Group 2), along with their complete biometric characteristics. Following the tests, one patient underwent a circumferential osteoneuromuscular stump revision surgery (CONM) that also involved partial removal of fascia and subcutaneous fat in the amputation stump, with re-testing after complete healing. In group 1, we obtained a stronger sEMG signal than in Group 2. In the CONM case, after surgery, the patient’s data suggest that the removal of fascia, alongside the fibrotic and subcutaneous fat tissue, generates a stronger sEMG signal. Therefore, a reduction in the fascial layer, especially if accompanied by a reduction of the subcutaneous fat layer may prove significant for improving the strength of sEMG signals used in the control of modern exoprosthetics.

Ascertaining the optimal myoelectric signal recording duration for pattern recognition based prostheses control

Introduction

Electromyogram-based pattern recognition (EMG-PR) has been widely considered an essentially intuitive control method for multifunctional upper limb prostheses. A crucial aspect of the scheme is the EMG signal recording duration (SRD) from which requisite motor tasks are characterized per time, impacting the system's overall performance. For instance, lengthy SRD inevitably introduces fatigue (that alters the muscle contraction patterns of specific limb motions) and may incur high computational costs in building the motion intent decoder, resulting in inadequate prosthetic control and controller delay in practical usage. Conversely, relatively shorter SRD may lead to reduced data collection durations that, among other advantages, allow for more convenient prosthesis recalibration protocols. Therefore, determining the optimal SRD required to characterize limb motion intents adequately that will aid intuitive PR-based control remains an open research question.

Method

This study systematically investigated the impact and generalizability of varying lengths of myoelectric SRD on the characterization of multiple classes of finger gestures. The investigation involved characterizing fifteen classes of finger gestures performed by eight normally limb subjects using various groups of EMG SRD including 1, 5, 10, 15, and 20 s. Two different training strategies including Between SRD and Within-SRD were implemented across three popular machine learning classifiers and three time-domain features to investigate the impact of SRD on EMG-PR motion intent decoder.

Result

The between-SRD strategy results which is a reflection of the practical scenario showed that an SRD greater than 5 s but less than or equal to 10 s (>5 and < = 10 s) would be required to achieve decent average finger gesture decoding accuracy for all feature-classifier combinations. Notably, lengthier SRD would incur more acquisition and implementation time and vice-versa. In inclusion, the study's findings provide insight and guidance into selecting appropriate SRD that would aid inadequate characterization of multiple classes of limb motion tasks in PR-based control schemes for multifunctional prostheses.

Compound motion decoding based on sEMG consisting of gestures, wrist angles, and strength

This study aimed to highlight the demand for upper limb compound motion decoding to provide a more diversified and flexible operation for the electromyographic hand. In total, 60 compound motions were selected, which were combined with four gestures, five wrist angles, and three strength levels. Both deep learning methods and machine learning classifiers were compared to analyze the decoding performance. For deep learning, three structures and two ways of label encoding were assessed for their training processes and accuracies; for machine learning, 24 classifiers, seven features, and a combination of classifier chains were analyzed. Results show that for this relatively small sample multi-target surface electromyography (sEMG) classification, feature combination (mean absolute value, root mean square, variance, 4th-autoregressive coefficient, wavelength, zero crossings, and slope signal change) with Support Vector Machine (quadric kernel) outstood because of its high accuracy, short training process, less computation cost, and stability (p &lt; 0.05). The decoding result achieved an average test accuracy of 98.42 ± 1.71% with 150 ms sEMG. The average accuracy for separate gestures, wrist angles, and strength levels were 99.35 ± 0.67%, 99.34 ± 0.88%, and 99.04 ± 1.16%. Among all 60 motions, 58 showed a test accuracy greater than 95%, and one part was equal to 100%.

On the Applications of EMG Sensors and Signals

The ability to execute limb motions derives from composite command signals (or efferent signals) that stem from the central nervous system through the highway of the spinal cord and peripheral nerves to the muscles that drive the joints [...].

Hierarchical Human-Inspired Control Strategies for Prosthetic Hands

The abilities of the human hand have always fascinated people, and many studies have been devoted to describing and understanding a mechanism so perfect and important for human activities. Hand loss can significantly affect the level of autonomy and the capability of performing the activities of daily life. Although the technological improvements have led to the development of mechanically advanced commercial prostheses, the control strategies are rather simple (proportional or on/off control). The use of these commercial systems is unnatural and not intuitive, and therefore frequently abandoned by amputees. The components of an active prosthetic hand are the mechatronic device, the decoding system of human biological signals into gestures and the control law that translates all the inputs into desired movements. The real challenge is the development of a control law replacing human hand functions. This paper presents a literature review of the control strategies of prosthetics hands with a multiple-layer or hierarchical structure, and points out the main critical aspects of the current solutions, in terms of human's functions replicated with the prosthetic device. The paper finally provides several suggestions for designing a control strategy able to mimic the functions of the human hand.

A parallel classification strategy to simultaneous control elbow, wrist, and hand movements

Background

In the field of myoelectric control systems, pattern recognition (PR) algorithms have become always more interesting for predicting complex electromyography patterns involving movements with more than 2 Degrees of Freedom (DoFs). The majority of classification strategies, used for the prosthetic control, are based on single, hierarchical and parallel linear discriminant analysis (LDA) classifiers able to discriminate up to 19 wrist/hand gestures (in the 3-DoFs case), considering both combined and discrete motions. However, these strategies were introduced to simultaneously classify only 2 DoFs and their use is limited by the lack of online performance measures. This study introduces a novel classification strategy based on the Logistic Regression (LR) algorithm with regularization parameter to provide simultaneous classification of 3 DoFs motion classes.

Methods

The parallel PR-based strategy was tested on 15 healthy subjects, by using only six surface EMG sensors. Twenty-seven discrete and complex elbow, hand and wrist motions were classified by keeping the number of electromyographic (EMG) electrodes to a bare minimum and the classification error rate under 10 %. To this purpose, the parallel classification strategy was implemented by using three classifiers one for each DoF: the “Elbow classifier”, the “Wrist classifier”, and the “Hand classifier” provided the simultaneous control of the elbow, hand, and wrist joints, respectively.

Results

Both the offline and real-time performance metrics were evaluated and compared with the LDA parallel classification results. The real-time recognition results were statistically better with the LR classifier with respect to the LDA classifier, for all motion classes (elbow, hand and wrist).

Conclusions

In this paper, a novel parallel PR-based strategy was proposed for classifying up to 3 DoFs: three joint classifiers were employed simultaneously for classifying 27 motion classes related to the elbow, wrist, and hand and promising results were obtained.

Challenges and Potential in Targeted Muscle Reinnervation in Pediatric Amputees

Targeted muscle reinnervation (TMR) is a powerful new tool in preventing and treating residual limb and phantom limb pain. In the adult population, TMR is rapidly becoming standard of care; however, there is a paucity of literature regarding indications and outcomes of TMR in the pediatric population. We present 2 cases of pediatric patients who sustained amputations and the relevant challenges associated with TMR in their cases. One is a 7-year-old patient who developed severe phantom and residual limb pain after a posttraumatic above-knee amputation. He failed pharmacologic measures and underwent TMR. He obtained complete relief of his symptoms and is continuing to do well 1.5 years postoperatively. The other is a 2-year-old boy with bilateral wrist and below-knee amputations as sequelae of sepsis. TMR was not performed because the patient never demonstrated evidence of phantom limb pain or symptomatic neuroma formation. We use these 2 cases to explore the challenges particular to pediatric patients when considering treatment with TMR, including capacity to report pain, risks of anesthesia, and cortical plasticity. These issues will be critical in determining how TMR will be applied to pediatric patients.