Integrating Upper-Limb Prostheses with the Human Body: Technology Advances, Readiness, and Roles in Human-Prosthesis Interaction.

Significant advances in bionic prosthetics have occurred in the past two decades. The field's rapid expansion has yielded many exciting technologies that can enhance the physical, functional, and cognitive integration of a prosthetic limb with a human. We review advances in the engineering of prosthetic devices and their interfaces with the human nervous system, as well as various surgical techniques for altering human neuromusculoskeletal systems for seamless human-prosthesis integration. We discuss significant advancements in research and clinical translation, focusing on upper limbprosthetics since they heavily rely on user intent for daily operation, although many discussed technologies have been extended to lower limb prostheses as well. In addition, our review emphasizes the roles of advanced prosthetics technologies in complex interactions with humans and the technology readiness levels (TRLs) of individual research advances. Finally, we discuss current gaps and controversies in the field and point out future research directions, guided by TRLs.

Implications of EMG channel count: enhancing pattern recognition online prosthetic testing.

Introduction: Myoelectric pattern recognition systems have shown promising control of upper limb powered prostheses and are now commercially available. These pattern recognition systems typically record from up to 8 muscle sites, whereas other control systems use two-site control. While previous offline studies have shown 8 or fewer sites to be optimal, real-time control was not evaluated. Methods: Six individuals with no limb absence and four individuals with a transradial amputation controlled a virtual upper limb prosthesis using pattern recognition control with 8 and 16 channels of EMG. Additionally, two of the individuals with a transradial amputation performed the Assessment for Capacity of Myoelectric Control (ACMC) with a multi-articulating hand and wrist prosthesis with the same channel count conditions. Results: Users had significant improvements in control when using 16 compared to 8 EMG channels including decreased classification error (p = 0.006), decreased completion time (p = 0.019), and increased path efficiency (p = 0.013) when controlling a virtual prosthesis. ACMC scores increased by more than three times the minimal detectable change from the 8 to the 16-channel condition. Discussion: The results of this study indicate that increasing EMG channel count beyond the clinical standard of 8 channels can benefit myoelectric pattern recognition users.

Evaluating the partial contribution of the P3 event-related potential elicited by auditory oddball stimuli during the Stroop task.

The cognitive load of a precisely timed task, such as the Stroop task, may be measured through the use of event-related potentials (ERPs). To determine the time at which cognitive load is at its peak, oddball tones may be applied at various times surrounding a cognitive task. However, we need to determine whether the simultaneous presentation of auditory and visual stimuli would mask a potential change in P3 in an ERP-producing task. If the contribution of the Stroop stimulus is too large, then Stroop ERP with oddball stimuli occurring at different timepoints may not be directly comparable across the various timepoints due to the contribution of the Stroop ERP. The aim of this study was to measure the magnitude of the difference wave between that of simultaneously presented stimuli and that of linearly added stimuli of separate responses. Participants were fitted with a dry-sensor EEG cap and were presented with a series of Stroop and auditory stimuli. For some Stroop stimuli, auditory stimuli occurred simultaneously or in a close time proximity to the Stroop stimuli. We sought to estimate the linear contribution of the ERP from Stroop and oddball stimuli. We found that the magnitude of the difference waves were 3.07 ± 1.65 µV and 2.82 ± 1.34 µV for congruent and incongruent stimuli, respectively. As the average amplitude in the P3 region for both the congruent and incongruent difference waves was lower than the magnitude of the auditory oddball presented simultaneously with Stroop stimuli (12.13 ± 1.00 µV for congruent and 11.78 ± 1.05 µV for incongruent Stroop), we expect that the contribution of P3 auditory oddball would not mask a potential Stroop effect even if the timing of the auditory oddball stimuli were experimentally manipulated, a direction that we hope to explore in future work. In conclusion, we determine this paradigm is suitable for measuring cognitive load in precisely timed tasks.Clinical Relevance- This study establishes the efficacy of presenting a Stroop task as a proxy for a cognitive challenge that could cause cognitive overload.

Machine Learning-Based Gait Mode Prediction for Hybrid Knee Prosthesis Control.

Recently, hybrid prosthetic knees, which can combine the advantages of passive and active prosthetic knees, have been proposed for individuals with a transfemoral amputation. Users could potentially take advantage of the passive knee mechanics during walking and the active power generation during stair ascent. One challenge in controlling the hybrid knees is accurate gait mode prediction for seamless transitions between passive and active modes. However, data imbalance between passive and active modes may impact the performance of a classifier. In this study, we used a dataset collected from nine individuals with a unilateral transfemoral amputation as they ambulated over level ground, inclines, and stairs. We evaluated several machine learning-based classifiers on the prediction of passive (level-ground walking, incline walking, descending stairs, and donning and doffing the prosthesis) and active mode (ascending stairs). In addition, we developed a generative adversarial network (GAN) to create synthetic data for improving classification performance. The results indicated that linear discriminant analysis and random forest might be the best classifiers regarding sensitivity to the active mode and overall accuracy, respectively. Further, we demonstrated that using the GAN-based synthetic data for training improves the sensitivity of classifiers.

Generating synthetic gait patterns based on benchmark datasets for controlling prosthetic legs.

BACKGROUND: Prosthetic legs help individuals with an amputation regain locomotion. Recently, deep neural network (DNN)-based control methods, which take advantage of the end-to-end learning capability of the network, have been proposed. One prominent challenge for these learning-based approaches is obtaining data for the training, particularly for the training of a mid-level controller. In this study, we propose a method for generating synthetic gait patterns (vertical load and lower limb joint angles) using a generative adversarial network (GAN). This approach enables a mid-level controller to execute ambulation modes that are not included in the training datasets. METHODS: The conditional GAN is trained on benchmark datasets that contain the gait data of individuals without amputation; synthetic gait patterns are generated from the user input. Further, a DNN-based controller for the generation of impedance parameters is trained using the synthetic gait pattern and the corresponding synthetic stiffness and damping coefficients. RESULTS: The trained GAN generated synthetic gait patterns with a coefficient of determination of 0.97 and a structural similarity index of 0.94 relative to benchmark data that were not included in the training datasets. We trained a DNN-based controller using the GAN-generated synthetic gait patterns for level-ground walking, standing-to-sitting motion, and sitting-to-standing motion. Four individuals without amputation participated in bypass testing and demonstrated the ambulation modes. The model successfully generated control parameters for the knee and ankle based on thigh angle and vertical load. CONCLUSIONS: This study demonstrates that synthetic gait patterns can be used to train DNN models for impedance control. We believe a conditional GAN trained on benchmark datasets can provide reliable gait data for ambulation modes that are not included in its training datasets. Thus, designing gait data using a conditional GAN could facilitate the efficient and effective training of controllers for prosthetic legs.

Dry EEG measurement of P3 to evaluate cognitive load during sitting, standing, and walking.

Combining brain imaging with dual-task paradigms provides a quantitative, direct metric of cognitive load that is agnostic to the motor task. This work aimed to quantitatively assess cognitive load during activities of daily living-sitting, standing, and walking-using a commercial dry encephalography headset. We recorded participants' brain activity while engaging in a stimulus paradigm that elicited event-related potentials. The stimulus paradigm consisted of an auditory oddball task in which participants had to report the number of oddball tones that were heard during each motor task. We extracted the P3 event-related potential, which is inversely proportional to cognitive load, from EEG signals in each condition. Our main findings showed that P3 was significantly lower during walking compared to sitting (p = .039), suggesting that cognitive load was higher during walking compared to the other activities. There were no significant differences in P3 between sitting and standing. Head motion did not have a significant impact on the measurement of cognitive load. This work validates the use of a commercial dry-EEG headset for measuring cognitive load across different motor tasks. The ability to accurately measure cognitive load in dynamic activities opens new avenues for exploring cognitive-motor interactions in individuals with and without motor impairments. This work highlights the potential of dry EEG for measuring cognitive load in naturalistic settings.

Powered knee and ankle prosthesis use with a K2 level ambulator: a case report.

Powered prosthetic knees and ankles have the capability of restoring power to the missing joints and potential to provide increased functional mobility to users. Nearly all development with these advanced prostheses is with individuals who are high functioning community level ambulators even though limited community ambulators may also receive great benefit from these devices. We trained a 70 year old male participant with a unilateral transfemoral amputation to use a powered knee and powered ankle prosthesis. He participated in eight hours of therapist led in-lab training (two hours per week for four weeks). Sessions included static and dynamic balance activities for improved stability and comfort with the powered prosthesis and ambulation training on level ground, inclines, and stairs. Assessments were taken with both the powered prosthesis and his prescribed, passive prosthesis post-training. Outcome measures showed similarities in velocity between devices for level-ground walking and ascending a ramp. During ramp descent, the participant had a slightly faster velocity and more symmetrical stance and step times with the powered prosthesis compared to his prescribed prosthesis. For stairs, he was able to climb with reciprocal stepping for both ascent and descent, a stepping strategy he is unable to do with his prescribed prosthesis. More research with limited community ambulators is necessary to understand if further functional improvements are possible with either additional training, longer accommodation periods, and/or changes in powered prosthesis control strategies.

Myoelectric prosthesis hand grasp control following targeted muscle reinnervation in individuals with transradial amputation.

BACKGROUND: Despite the growing availability of multifunctional prosthetic hands, users' control and overall functional abilities with these hands remain limited. The combination of pattern recognition control and targeted muscle reinnervation (TMR) surgery, an innovative technique where amputated nerves are transferred to reinnervate new muscle targets in the residual limb, has been used to improve prosthesis control of individuals with more proximal upper limb amputations (i.e., shoulder disarticulation and transhumeral amputation). OBJECTIVE: The goal of this study was to determine if prosthesis hand grasp control improves following transradial TMR surgery. METHODS: Eight participants were trained to use a multi-articulating hand prosthesis under myoelectric pattern recognition control. All participated in home usage trials pre- and post-TMR surgery. Upper limb outcome measures were collected following each home trial. RESULTS: Three outcome measures (Southampton Hand Assessment Procedure, Jebsen-Taylor Hand Function Test, and Box and Blocks Test) improved 9-12 months post-TMR surgery compared with pre-surgery measures. The Assessment of Capacity for Myoelectric Control and Activities Measure for Upper Limb Amputees outcome measures had no difference pre- and post-surgery. An offline electromyography analysis showed a decrease in grip classification error post-TMR surgery compared to pre-TMR surgery. Additionally, a majority of subjects noted qualitative improvements in their residual limb and phantom limb sensations post-TMR. CONCLUSIONS: The potential for TMR surgery to result in more repeatable muscle contractions, possibly due to the reduction in pain levels and/or changes to phantom limb sensations, may increase functional use of many of the clinically available dexterous prosthetic hands.

Wrist speed feedback improves elbow compensation and reaching accuracy for myoelectric transradial prosthesis users in hybrid virtual reaching task.

BACKGROUND: Myoelectric prostheses are a popular choice for restoring motor capability following the loss of a limb, but they do not provide direct feedback to the user about the movements of the device-in other words, kinesthesia. The outcomes of studies providing artificial sensory feedback are often influenced by the availability of incidental feedback. When subjects are blindfolded and disconnected from the prosthesis, artificial sensory feedback consistently improves control; however, when subjects wear a prosthesis and can see the task, benefits often deteriorate or become inconsistent. We theorize that providing artificial sensory feedback about prosthesis speed, which cannot be precisely estimated via vision, will improve the learning and control of a myoelectric prosthesis. METHODS: In this study, we test a joint-speed feedback system with six transradial amputee subjects to evaluate how it affects myoelectric control and adaptation behavior during a virtual reaching task. RESULTS: Our results showed that joint-speed feedback lowered reaching errors and compensatory movements during steady-state reaches. However, the same feedback provided no improvement when control was perturbed. CONCLUSIONS: These outcomes suggest that the benefit of joint speed feedback may be dependent on the complexity of the myoelectric control and the context of the task.

Toward higher-performance bionic limbs for wider clinical use.

Most prosthetic limbs can autonomously move with dexterity, yet they are not perceived by the user as belonging to their own body. Robotic limbs can convey information about the environment with higher precision than biological limbs, but their actual performance is substantially limited by current technologies for the interfacing of the robotic devices with the body and for transferring motor and sensory information bidirectionally between the prosthesis and the user. In this Perspective, we argue that direct skeletal attachment of bionic devices via osseointegration, the amplification of neural signals by targeted muscle innervation, improved prosthesis control via implanted muscle sensors and advanced algorithms, and the provision of sensory feedback by means of electrodes implanted in peripheral nerves, should all be leveraged towards the creation of a new generation of high-performance bionic limbs. These technologies have been clinically tested in humans, and alongside mechanical redesigns and adequate rehabilitation training should facilitate the wider clinical use of bionic limbs.

User Performance With a Transradial Multi-Articulating Hand Prosthesis During Pattern Recognition and Direct Control Home Use.

With the increasing availability of more advanced prostheses individuals with a transradial amputation can now be fit with single to multi-degree of freedom hands. Reliable and accurate control of these multi-grip hands still remains challenging. This is the first multi-user study to investigate at-home control and use of a multi-grip hand prosthesis under pattern recognition and direct control. Individuals with a transradial amputation were fitted with and trained to use an OSSUR i-Limb Ultra Revolution with Coapt COMPLETE CONTROL system. They participated in two 8-week home trials using the hand under myoelectric direct and pattern recognition control in a randomized order. While at home, participants demonstrated broader usage of grips in pattern recognition compared to direct control. After the home trial, they showed significant improvements in the Assessment of Capacity for Myoelectric Control (ACMC) outcome measure while using pattern recognition control compared to direct control; other outcome measures showed no differences between control styles. Additionally, this study provided a unique opportunity to evaluate EMG signals during home use. Offline analysis of calibration data showed that users were 81.5% [7.1] accurate across a range of three to five grips. Although EMG signal noise was identified during some calibrations, overall EMG quality was sufficient to provide users with control performance at or better than direct control.

Corrigendum: Powered knee and ankle prosthesis use with a K2 level ambulator: a case report.

[This corrects the article DOI: 10.3389/fresc.2023.1203545.].

The impact of added mass placement on metabolic and temporal-spatial characteristics of transfemoral prosthetic gait.

BACKGROUND: Despite prosthetic technology advancements, individuals with transfemoral amputation have compromised temporal-spatial gait parameters and high metabolic requirements for ambulation. It is unclear how adding mass at different locations on a transfemoral prosthesis might affect these outcomes. Research question Does walking with mass added at different locations on a transfemoral prosthesis affect temporal-spatial gait parameters and metabolic requirements compared to walking with no additional mass? METHODS: Fourteen participants with unilateral transfemoral amputations took part. A 1.8 kg mass was added to their prostheses in three locations: Knee, just proximal to the prosthetic knee; Shank, mid-shank on the prosthesis; or Ankle, just proximal to the prosthetic foot. Temporal-spatial gait parameters were collected as participants walked over a GAITRite® walkway and metabolic data were collected during treadmill walking for each of these conditions and with no mass added, the None condition. Separate linear mixed effects models were created and post-hoc tests to compare with the control condition of None were performed with a significance level of 0.05. RESULTS: Overground self-selected walking speed for Ankle was significantly slower than for None (p < 0.05) (None: 1.16 ± 0.24; Knee: 1.15 ± 0.19; Shank: 1.14 ± 0.24; Ankle 0.99 ± 0.20 m/s). Compared to None, Ankle showed significantly increased oxygen consumption during treadmill walking (p < 0.05) (None: 13.82 ± 2.98; Knee: 13.83 ± 2.82; Shank: 14.30 ± 2.89; Ankle 14.56 ± 2.99 ml O2/kg/min). Other metabolic outcomes (power, cost of transport, oxygen cost) showed similar trends. Knee and Shank did not have significant negative effects on any metabolic or temporal-spatial parameters, as compared to None (p > 0.05). Significance Results suggest that additional mass located mid-shank or further proximal on a transfemoral prosthesis may not have negative temporal-spatial or metabolic consequences. Clinicians, researchers, and designers may be able to utilize heavier components, as long as the center of mass is not further distal than mid-shank, without adversely affecting gait parameters or metabolic requirements.

Corrigendum: Functional mobility training with a powered knee and ankle prosthesis.

[This corrects the article DOI: 10.3389/fresc.2022.790538.].

Ambulation Control System Design for a Hybrid Knee Prosthesis.

Prosthetic knees available to individuals with transfemoral amputation seek to restore functional ability to the user. Passive prosthetic knees are lightweight but can restore only limited, dissipative ambulation activities whereas active knees can provide energy to restore additional ambulation activities such as stair climbing and standing up from a chair. Semi-active prosthetic devices aim to only power a subset of activities and use passive components and control when that power is not necessary. Here, we outline an ambulation control system for a lightweight Hybrid Knee prosthesis that is powered for climbing stairs and passive for other ambulation activities (level-ground walking, walking on an incline, and stair descent). We include preliminary offline and online intent recognition system results for one able-bodied user and one individual with a transfemoral amputation demonstrating low error rates in predicting between active and passive control.

Functional Mobility Training with a Powered Knee and Ankle Prosthesis.

Limb loss at the transfemoral or knee disarticulation level results in a significant decrease of mobility. Powered lower limb prostheses have the potential to provide increased functional mobility and return individuals to activities of daily living that are limited due to their amputation. Providing power at the knee and/or ankle, new and innovative training is required for the amputee and the clinician to understand the capabilities of these advanced devices. This protocol for functional mobility training with a powered knee and ankle prosthesis was developed while training 30 participants with a unilateral transfemoral or knee disarticulation amputation at a nationally ranked physical medicine and rehabilitation research hospital. Participants received instruction for level ground walking, stair climbing, incline walking and sit to stand transitions. A therapist provided specific training for each mode including verbal, visual and tactile cueing along with patient education on the functionality of the device. The primary outcome measure was the ability of each participant to demonstrate independence with walking and sit to stand transitions along with modified independence for stair climbing and incline walking due to use of a handrail. Every individual was successful in comfortable ambulation of level ground walking and 27 out of 30 were successful in all other functional modes after participating in 1-3 sessions of 1-2 hours in length (3 terminated their participation prior to attempting all activities). As these prosthetic devices continue to advance, therapy techniques must advance as well and this paper serves as an education on new training techniques that can provide amputees with the best possible tools to take advantage of these powered devices in order to achieve their desired clinical outcomes.

Knee extensor power predicts six-minute walk test performance in people with transfemoral amputations.

BACKGROUND: Lower-limb amputees have increased metabolic costs during walking that may be mitigated by maintaining quadriceps strength and power following amputation. However, there are no current studies investigating the relationship between thigh strength and walking performance in individuals with transfemoral amputation. OBJECTIVE: To quantify the relationship between intact limb quadriceps strength in transfemoral amputees and six-minute walk test (6MWT) performance. DESIGN: Descriptive laboratory study. SETTING: Laboratory. PARTICIPANTS: Eleven participants with unilateral transfemoral amputations from trauma or osteosarcoma (4 women/7 men, 46.21 ± 12.68 years old, 28.24 ± 20.57 years following amputation). INTERVENTIONS: Strength and power testing on the intact limb followed by 6MWT with a flowmeter to measure oxygen uptake (VO2 ). MAIN OUTCOME MEASURES: Strength included mass-normalized peak torque, average torque, and average power. 6MWT measures included total distance traveled and VO2 normalized to distance and mass. Significant correlations (P ≤ .05) were retained for a regression analysis. RESULTS: Peak isokinetic knee extensor torque was correlated with total VO2 (r = -.60, P = .05) and distance traveled (r = .84, P = .001). Average isokinetic knee extensor torque was correlated with total VO2 (r = -.61, P = .046) and distance traveled (r = .85, P = .001). Average knee extensor power was correlated with total VO2 (r = -.67, P = .026) and distance traveled (r = .88, P < .001). Peak isometric knee extensor torque was correlated with distance traveled (r = .69, P = .019). Average power explained 77.2% of the variance in distance traveled during the 6MWT (P < .001) and average power explained 44.2% of the variance in total VO2 during the 6MWT (P = .026). CONCLUSIONS: Knee extensor strength was correlated with performance on the 6MWT in individuals with unilateral transfemoral amputation. The strongest relationship was between isokinetic quadriceps power and distance traveled, which suggests that developing quadriceps power in the intact limb following amputation may be an important factor to reduce metabolic cost of walking and support a return to an active lifestyle.

Deep-Learning to Map a Benchmark Dataset of Non-amputee Ambulation for Controlling an Open Source Bionic Leg.

Powered lower-limb prosthetic devices may be becoming a promising option for amputation patients. Although various methods have been proposed to produce gait trajectories similar to those of non-disabled individuals, implementing these control methods is still challenging. It remains unclear whether these methods provide appropriate, safe, and intuitive locomotion as intended. This paper proposes the direct mapping of the voluntary movement of a residual limb (i.e., thigh) to the desired impedance parameters for amputated limbs (i.e., knee and ankle). The proposed model was learned from the gait trajectories of intact limb individuals from a publicly available biomechanics dataset, and was applied to control the prosthetic leg without post-tuning the network. Thus, the proposed method does not require training time with individuals with amputation nor configuration time for its use, and it provides a closely resembling gait trajectory of the intact limb. For preliminary testing, three able-bodied subjects participated in bypass tests. The proposed model accomplished intuitive and reliable level-ground walking at three different step lengths: self-selected, long-, and short-step lengths. The results indicate that intact benchmark data with different sensor configurations can be directly used to train the model to control prosthetic legs.

Seamless and intuitive control of a powered prosthetic leg using deep neural network for transfemoral amputees.

Powered prosthetic legs are becoming a promising option for amputee patients. However, developing safe, robust, and intuitive control strategies for powered legs remains one of the greatest challenges. Although a variety of control strategies have been proposed, creating and fine-tuning the system parameters is time-intensive and complicated when more activities need to be restored. In this study, we developed a deep neural network (DNN) model that facilitates seamless and intuitive gait generation and transitions across five ambulation modes: level-ground walking, ascending/descending ramps, and ascending/descending stairs. The combination of latent and time sequence features generated the desired impedance parameters within the ambulation modes and allowed seamless transitions between ambulation modes. The model was applied to the open-source bionic leg and tested on unilateral transfemoral users. It achieved the overall coefficient of determination of 0.72 with the state machine-based impedance parameters in the offline testing session. In addition, users were able to perform in-laboratory ambulation modes with an overall success rate of 96% during the online testing session. The results indicate that the DNN model is a promising candidate for subject-independent and tuning-free prosthetic leg control for transfemoral amputees.

A gait phase prediction model trained on benchmark datasets for evaluating a controller for prosthetic legs.

Powered lower-limb assistive devices, such as prostheses and exoskeletons, are a promising option for helping mobility-impaired individuals regain functional gait. Gait phase prediction plays an important role in controlling these devices and evaluating whether the device generates a gait similar to that of individuals with intact limbs. This study proposes a gait phase prediction method based on a deep neural network (DNN). The long short-term memory (LSTM)-based model predicts a continuous gait phase from the 250 ms history of the vertical load, thigh angle, knee angle, and ankle angle, commonly available on powered lower-limb assistive devices. One unified model was trained using publicly available benchmark datasets containing intact limb gaits for level-ground walking (LGW) and ascending stairs (SA). A phase prediction error of 1.28% for all benchmark datasets was obtained. The model was subsequently applied to a state machine-controlled powered prosthetic leg dataset collected from four individuals with unilateral transfemoral amputation. The gait phase prediction results (a phase prediction error of 5.70%) indicate that the model trained on benchmark data can be used for a system not included in the training dataset with no post-processing, such as model adaptation. Furthermore, it provided information regarding evaluation of the controller: whether the prosthetic leg generated normal gait. In conclusion, the proposed gait phase prediction model will facilitate efficient gait prediction and evaluation of controllers for powered lower-limb assistive devices.