VUB-CYBERLEGs CYBATHLON 2016 Beta-Prosthesis: case study in control of an active two degree of freedom transfemoral prosthesis

Development and Preliminary Evaluation of a Bimodal Foot Prosthesis for Walking and Running

People often alternate between bouts of walking and running, for instance, when adults participate in recreational activities. Transitioning between activities can be challenging for prosthesis users because existing prosthetic feet are not well-suited for both tasks. Meanwhile, switching between prostheses for different tasks is often impractical. Collectively, these challenges can present barriers to physical activity participation for people with limb loss, which can negatively impact social or physical health. This work describes the development and evaluation of a passive bimodal prosthetic foot prototype with different configurations and stiffnesses for walking and running. Users rated the bimodal prosthesis higher for standing and walking compared to a running prosthesis (+2.3 for both tasks on a seven-point Likert scale). Users rated the bimodal prosthesis higher for running compared to a walking prosthesis (+1.7 and +0.5 for 2.0 and 2.5 m/s running, respectively). Changing from walking to running mode increased the device's stiffness by 23-84%, depending on the user's preference. Users could switch between bimodal prosthesis walking and running modes quickly (21.3 ± 12.0 s). Overall, the preliminary results were encouraging in terms of user satisfaction, stiffness change between modes, and mode-switching speed. These findings motivate future exploration of this bimodal prosthesis concept.

Testing and evaluation of lower limb prosthesis prototypes in people with a transfemoral amputation: a scoping review on research protocols

BACKGROUND

When developing new lower limb prostheses, prototypes are tested to obtain insights into the performance. However, large variations between research protocols may complicate establishing the potential added value of newly developed prototypes over other prostheses.

OBJECTIVE

This review aims at identifying participant characteristics, research protocols, reference values, aims, and corresponding outcome measures used during prosthesis prototype testing on people with a transfemoral amputation.

METHODS

A systematic search was done on PubMed and Scopus from 2000 to December 2020. Articles were included if testing was done on adults with transfemoral or knee disarticulation amputation; testing involved walking with a non-commercially available prototype leg prosthesis consisting of at least a knee component; and included evaluations of the participants' functioning with the prosthesis prototype.

RESULTS

From the initial search of 2027 articles, 48 articles were included in this review. 20 studies were single-subject studies and 4 studies included a cohort of 10 or more persons with a transfemoral amputation. Only 5 articles reported all the pre-defined participant characteristics that were deemed relevant. The familiarization time with the prosthesis prototype prior to testing ranged from 5 to 10 min to 3 months; in 25% of the articles did not mention the extent of the familiarization period. Mobility was most often mentioned as the development or testing aim. A total of 270 outcome measures were identified, kinetic/kinematic gait parameters were most often reported. The majority of outcome measures corresponded to the mobility aim. For 48% of the stated development aims and 4% of the testing aims, no corresponding outcome measure could be assigned. Results indicated large inconsistencies in research protocols and outcome measures used to validate pre-determined aims.

CONCLUSIONS

The large variation in prosthesis prototype testing and reporting calls for the development of a core set of reported participant characteristics, testing protocols, and specific and well-founded outcome measures, tailored to the various aims and development phases. The use of such a core set can give greater insights into progress of developments and determine which developments have additional benefits over the state-of-the-art. This review may contribute as initial input towards the development of such a core set.

Comparison of machine learning and deep learning-based methods for locomotion mode recognition using a single inertial measurement unit

Locomotion mode recognition provides the prosthesis control with the information on when to switch between different walking modes, whereas the gait phase detection indicates where we are in the gait cycle. But powered prostheses often implement a different control strategy for each locomotion mode to improve the functionality of the prosthesis. Existing studies employed several classical machine learning methods for locomotion mode recognition. However, these methods were less effective for data with complex decision boundaries and resulted in misclassifications of motion recognition. Deep learning-based methods potentially resolve these limitations as it is a special type of machine learning method with more sophistication. Therefore, this study evaluated three deep learning-based models for locomotion mode recognition, namely recurrent neural network (RNN), long short-term memory (LSTM) neural network, and convolutional neural network (CNN), and compared the recognition performance of deep learning models to the machine learning model with random forest classifier (RFC). The models are trained from data of one inertial measurement unit (IMU) placed on the lower shanks of four able-bodied subjects to perform four walking modes, including level ground walking (LW), standing (ST), and stair ascent/stair descent (SA/SD). The results indicated that CNN and LSTM models outperformed other models, and these models were promising for applying locomotion mode recognition in real-time for robotic prostheses.

Competitive motivation increased home use and improved prosthesis self-perception after Cybathlon 2020 for neuromusculoskeletal prosthesis user

Background

Assistive technologies, such as arm prostheses, are intended to improve the quality of life of individuals with physical disabilities. However, certain training and learning is usually required from the user to make these technologies more effective. Moreover, some people can be encouraged to train more through competitive motivation.

Methods

In this study, we investigated if the training for and participation in a competitive event (Cybathlon 2020) could promote behavioral changes in an individual with upper limb amputation (the pilot). We defined behavioral changes as the active time while his prosthesis was actuated, ratio of opposing and simultaneous movements, and the pilot’s ability to finely modulate his movement speeds. The investigation was based on extensive home-use data from the period before, during and after the Cybathlon 2020 competition.

Results

Relevant behavioral changes were found from both quantitative and qualitative analyses. The pilot’s home use of his prosthesis nearly doubled in the period before the Cybathlon, and remained 66% higher than baseline after the competition. Moreover, he improved his speed modulation when controlling his prosthesis, and he learned and routinely operated new movements in the prosthesis (wrist rotation) at home. Additionally, as confirmed by semi-structured interviews, his self-perception of the prosthetic arm and its functionality also improved.

Conclusions

An event like the Cybathlon may indeed promote behavioral changes in how competitive individuals with amputation use their prostheses. Provided that the prosthesis is suitable in terms of form and function for both competition and at-home daily use, daily activities can become opportunities for training, which in turn can improve prosthesis function and create further opportunities for daily use. Moreover, these changes appeared to remain even well after the event, albeit relevant only for individuals who continue using the technology employed in the competition.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12984-022-01024-4.

A survey on the influence of CYBATHLON on the development and acceptance of advanced assistive technologies

Background

Advanced assistive technologies (AAT) aim to exploit the vast potential of technological developments made in the past decades to improve the quality of life of people with disabilities. Combining complex robotic technologies with the unique needs of people with disabilities requires a strong focus on user-centered design to ensure that the AAT appropriately addresses the daily life struggles of target users. The CYBATHLON aims to promote this mindset by empowering the AAT target users (“pilots”) to compete on race tracks that represent approximations of daily life obstacles. The objective of this work was to investigate the AAT technology development, usability, and user involvement (i.e., application of user-centered design) in the context of the CYBATHLON.

Methods

With an online survey targeting the pilots and technical leads of teams preparing for the CYBATHLON 2020 Global Edition, we investigated to what extent the pilots were involved in device development and how this influences the perceived usability of the showcased AAT. Furthermore, the effects of user-centered design variables on the individual race performances were analyzed.

Results

A total of 81 responses from 35 pilots and 46 technical leads were collected in the two weeks prior to the event. Of all teams partaking in the included disciplines of the CYBATHLON 2020 Global Edition, 81.8% (36 of 44) were included in the study. User-centered design appeared to be a prevalent practice among the teams, as 85.7% of all pilots reported a certain level of involvement. However, only 25.5% of the pilots reported daily life usage, despite QUEST usability scores of both respondent groups showing moderate to high satisfaction with the respected AAT across all investigated disciplines. An explorative linear mixed model indicated that daily life usage (p < 0.05) and prolonged user involvement (e.g., more than 2 years, p < 0.001) have a significant positive effect on the race performance at the competition.

Conclusions

We conclude that the CYBATHLON positively fulfills its conceptual goals of promoting active participation and inclusion of people with disabilities in the design and evaluation of AAT, thereby stimulating the development of promising novel technological solutions. Also, our data could underline the value of the competition as a benchmark, highlighting remaining usability limitations or technology adoption hurdles.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12984-022-01015-5.

Performance of the CYBERLEGs motorized lower limb prosthetic device during simulated daily activities

Background

The CYBERLEGs-gamma (CLs-ɣ) prosthesis has been developed to investigate the possibilities of powerful active prosthetics in restoring human gait capabilities after lower limb amputation.

Objective

The objective of this study was to determine the performance of the CLs-ɣ prosthesis during simulated daily activities.

Methods

Eight participants with a transfemoral amputation (age: 55 ± 15 years, K-level 3, registered under: NCT03376919) performed a familiarization session, an experimental session with their current prosthesis, three training sessions with the CLs-ɣ prosthesis and another experimental session with the CLs-ɣ prosthesis. Participants completed a stair-climbing-test, a timed-up-and-go-test, a sit-to stand-test, a 2-min dual-task and a 6-min treadmill walk test.

Results

Comparisons between the two experimental sessions showed that stride length significantly increased during walking with the CLs-ɣ prosthesis (p = .012) due to a greater step length of the amputated leg (p = .035). Although a training period with the prototype was included, preferred walking speed was significantly slower (p = .018), the metabolic cost of transport was significantly higher (p = .028) and reaction times significantly worsened (p = .012) when walking with the CLs-ɣ compared to the current prosthesis.

Conclusions

It can be stated that a higher physical and cognitive effort were required when wearing the CLs-ɣ prosthesis. Positive outcomes were observed regarding stride length and stair ambulation. Future prosthetics development should minimize the weight of the device and integrate customized control systems. A recommendation for future research is to include several shorter training periods or a prolonged adaptation period.

JNER at 15 years: analysis of the state of neuroengineering and rehabilitation

On JNER's 15th anniversary, this editorial analyzes the state of the field of neuroengineering and rehabilitation. I first discuss some ways that the nature of neurorehabilitation research has evolved in the past 15 years based on my perspective as editor-in-chief of JNER and a researcher in the field. I highlight increasing reliance on advanced technologies, improved rigor and openness of research, and three, related, new paradigms - wearable devices, the Cybathlon competition, and human augmentation studies - indicators that neurorehabilitation is squarely in the age of wearability. Then, I briefly speculate on how the field might make progress going forward, highlighting the need for new models of training and learning driven by big data, better personalization and targeting, and an increase in the quantity and quality of usability and uptake studies to improve translation.

A Comparison of Control Strategies in Commercial and Research Knee Prostheses

GOAL

To provide an overview of control strategies in commercial and research microprocessor-controlled prosthetic knees (MPKs).

METHODS

Five commercially available MPKs described in patents, and five research MPKs reported in scientific literature were compared. Their working principles, intent recognition, and walking controller were analyzed. Speed and slope adaptability of the walking controller was considered as well.

RESULTS

Whereas commercial MPKs are mostly passive, i.e., do not inject energy in the system, and employ heuristic rule-based intent classifiers, research MPKs are all powered and often utilize machine learning algorithms for intention detection. Both commercial and research MPKs rely on finite state machine impedance controllers for walking. Yet while commercial MPKs require a prosthetist to adjust impedance settings, scientific research is focused on reducing the tunable parameter space and developing unified controllers, independent of subject anthropometrics, walking speed, and ground slope.

CONCLUSION

The main challenges in the field of powered, active MPKs (A-MPKs) to boost commercial viability are first to demonstrate the benefit of A-MPKs compared to passive MPKs or mechanical non-microprocessor knees using biomechanical, performance-based and patient-reported metrics. Second, to evaluate control strategies and intent recognition in an uncontrolled environment, preferably outside the laboratory setting. And third, even though research MPKs favor sophisticated algorithms, to maintain the possibility of practical and comprehensible tuning of control parameters, considering optimal control cannot be known a priori.

SIGNIFICANCE

This review identifies main challenges in the development of A-MPKs, which have thus far hindered their broad availability on the market.

The Challenges and Achievements of Experimental Implementation of an Active Transfemoral Prosthesis Based on Biological Quasi-Stiffness: The CYBERLEGs Beta-Prosthesis

The CYBERLEGs Beta-Prosthesis is an active transfemoral prosthesis that can provide the full torque required for reproducing average level ground walking at both the knee and ankle in the sagittal plane. The prosthesis attempts to produce a natural level ground walking gait that approximates the joint torques and kinematics of a non-amputee while maintaining passively compliant joints, the stiffnesses of which were derived from biological quasi-stiffness measurements. The ankle of the prosthesis consists of a series elastic actuator with a parallel spring and the knee is composed of three different systems that must compliment each other to generate the correct joint behavior: a series elastic actuator, a lockable parallel spring and an energy transfer mechanism. Bench testing of this new prosthesis was completed and demonstrated that the device was able to create the expected torque-angle characteristics for a normal walker under ideal conditions. The experimental trials with four amputees walking on a treadmill to validate the behavior of the prosthesis proved that although the prosthesis could be controlled in a way that allowed all subjects to walk, the accurate timing and kinematic requirements of the output of the device limited the efficacy of using springs with quasi-static stiffnesses. Modification of the control and stiffness of the series springs could provide better performance in future work.

ED-FNN: A New Deep Learning Algorithm to Detect Percentage of the Gait Cycle for Powered Prostheses

Throughout the last decade, a whole new generation of powered transtibial prostheses and exoskeletons has been developed. However, these technologies are limited by a gait phase detection which controls the wearable device as a function of the activities of the wearer. Consequently, gait phase detection is considered to be of great importance, as achieving high detection accuracy will produce a more precise, stable, and safe rehabilitation device. In this paper, we propose a novel gait percent detection algorithm that can predict a full gait cycle discretised within a 1% interval. We called this algorithm an exponentially delayed fully connected neural network (ED-FNN). A dataset was obtained from seven healthy subjects that performed daily walking activities on the flat ground and a 15-degree slope. The signals were taken from only one inertial measurement unit (IMU) attached to the lower shank. The dataset was divided into training and validation datasets for every subject, and the mean square error (MSE) error between the model prediction and the real percentage of the gait was computed. An average MSE of 0.00522 was obtained for every subject in both training and validation sets, and an average MSE of 0.006 for the training set and 0.0116 for the validation set was obtained when combining all subjects' signals together. Although our experiments were conducted in an offline setting, due to the forecasting capabilities of the ED-FNN, our system provides an opportunity to eliminate detection delays for real-time applications.