Within-socket Myoelectric Prediction of Continuous Ankle Kinematics for Control of a Powered Transtibial Prosthesis

Objective. Powered robotic prostheses create a need for natural-feeling user interfaces and robust control schemes. Here, we examined the ability of a nonlinear autoregressive model to continuously map the kinematics of a transtibial prosthesis and electromyographic (EMG) activity recorded within socket to the future estimates of the prosthetic ankle angle in three transtibial amputees. Approach. Model performance was examined across subjects during level treadmill ambulation as a function of the size of the EMG sampling window and the temporal \u27prediction\u27 interval between the EMG/kinematic input and the model\u27s estimate of future ankle angle to characterize the trade-off between model error, sampling window and prediction interval. Main results. Across subjects, deviations in the estimated ankle angle from the actual movement were robust to variations in the EMG sampling window and increased systematically with prediction interval. For prediction intervals up to 150 ms, the average error in the model estimate of ankle angle across the gait cycle was less than 6°. EMG contributions to the model prediction varied across subjects but were consistently localized to the transitions to/from single to double limb support and captured variations from the typical ankle kinematics during level walking. Significance. The use of an autoregressive modeling approach to continuously predict joint kinematics using natural residual muscle activity provides opportunities for direct (transparent) control of a prosthetic joint by the user. The model\u27s predictive capability could prove particularly useful for overcoming delays in signal processing and actuation of the prosthesis, providing a more biomimetic ankle response

Human-activity-centered measurement system:challenges from laboratory to the real environment in assistive gait wearable robotics

Assistive gait wearable robots (AGWR) have shown a great advancement in developing intelligent devices to assist human in their activities of daily living (ADLs). The rapid technological advancement in sensory technology, actuators, materials and computational intelligence has sped up this development process towards more practical and smart AGWR. However, most assistive gait wearable robots are still confined to be controlled, assessed indoor and within laboratory environments, limiting any potential to provide a real assistance and rehabilitation required to humans in the real environments. The gait assessment parameters play an important role not only in evaluating the patient progress and assistive device performance but also in controlling smart self-adaptable AGWR in real-time. The self-adaptable wearable robots must interactively conform to the changing environments and between users to provide optimal functionality and comfort. This paper discusses the performance parameters, such as comfortability, safety, adaptability, and energy consumption, which are required for the development of an intelligent AGWR for outdoor environments. The challenges to measuring the parameters using current systems for data collection and analysis using vision capture and wearable sensors are presented and discussed

GAIT PERFORMANCE AND CONTROL OF A PROSTHETIC ANKLE JOINT FOR BELOW-KNEE AMPUTEES

Traumatic events such as accidents or vascular and circulatory disorders often lead to amputation of the lower limb below the knee joint. The surgery is followed by fitting of a prosthetic device and rehabilitation process to help the individual recover mobility. The recovered gait of the individual depends to a large extent on his/her health, the amputation technique, and the functional level of the prosthesis. Prior research in amputee gait has focused mostly on assessing gait symmetry, movement of the healthy joints, activities of the unaffected muscles, and the metabolic energy consumption in individuals who had undergone traditional amputation. Very little research has been carried out on the performance of individuals with non-traditional amputation procedures designed to maximize the ability of the residual limb to support body weight at the extremity and to maintain the ability of the affected muscles. Moreover, majority of the studies were limited to gait tests in laboratory environments which restricted the mobility of the individuals. Current ankle/foot prostheses for people with below-knee amputation are primarily passive devices whose performance cannot be adapted or optimized to meet the requirements of different users. The adverse consequences of wearing poorly functioning prosthetic feet include asymmetric gait, increased metabolic consumption, limited blood flow, instability, and pain. Over the long term, the amputees, especially ones with diabetes, might have to undergo hip replacement procedure and use wheel-chair on a daily basis. There exists a high and increasing demand for an advanced prosthetic foot that is comfortable and able to replicate the function of the biological foot. Some of the factors hindering the development and performance validation of such an actively controlled foot are the lack of complete understanding of the gait, the interaction between the residual limb and the controller, presence of human in the control loop, unknown interaction between the terrain and the foot, and stringent requirements on the mechanical power and rigidity of the foot. This dissertation aims to address these shortcomings in a systematic fashion in order to develop an intelligent ankle/foot prosthesis system. The following are the key steps in the process adopted in this dissertation. • First, a gait monitoring device and algorithms for gait analysis will be developed to study the gait of people with below-knee amputation in real time during work-related activities. Experimental protocols are then designed to collect gait data from individuals with below-knee amputation in order to understand the activity of the residual muscles and the ability of the prosthetic device to support body weight during gait. • The dependence of the interfacial socket forces and electromyography signals from the muscles in the residual limb on the type of the gait and gait-related events will then be studied. The use of this dependence to recognize user gait and the corresponding ankle displacement pattern for the controlled prosthetic foot will be investigated. • Finally, hierarchical learning-based control strategies will be developed to adaptively compensate for the unknown, changing ankle dynamics and drive the prosthetic ankle joint along the desired trajectories. It is anticipated that the learning capabilities of these control strategies will enable the prosthetic ankle joint to not only replicate the movement of the healthy ankle, but also improve the stability of the gait and optimize the performance. The above approaches are demonstrated in this dissertation in two parts. The analysis of the gait of a group of otherwise healthy men with non-traditional amputation technique called transtibial osteomyoplastic amputation (TOA) is considered in the first part of the dissertation. The TOA procedure is prescribed for healthy, young individuals who desire a very active lifestyle. TOA offers stable bony residuum capable of bearing the weight of the individual and residual muscles that are active throughout the gait cycle. The gait study carried out in this dissertation is shown to confirm loading at the distal end-bearing area of the residual limb and active contraction of the residual muscles below the knee during gait of all participants. The interfacial forces in the socket and the activity of the residual muscles in subjects with TOA are shown to be related to and dependent on the type of gait, as well as the type of prosthetic feet used. In addition, the potential of residuum socket interface forces in recognition of the gait is also demonstrated. Learning-based control of the prosthetic ankle joint is addressed in the second part of the dissertation. Two hierarchical learning-based control algorithms that take into account the ankle dynamics, foot-ground interaction, and the movement of upper body are considered. The first strategy uses an artificial neural network-based feedback linearization controller to learn the unknown and changing dynamics of the ankle joint and to track a desired ankle displacement profile. In the second strategy, a neural dynamic programming-based controller that can track an ankle displacement profile while optimizing a cost function based on the tracking error is considered. Actual gait data obtained from the subjects in the first part of this dissertation is used to study the effectiveness of the control strategy. For the first time, an adaptive controller has been demonstrated that can address changes in terrain and in user requirements to provide consistent and stable functioning of the prosthetic ankle. It is anticipated that the strategy developed in this dissertation will help build an intelligent prosthetic foot that can significantly improve the mobility and long-term health of people with amputation of the lower limb. Keyword: Gait Analysis, Prosthetic Foot, Intelligent Contro

The influence of push-off timing in a robotic ankle-foot prosthesis on the energetics and mechanics of walking

Background: Robotic ankle-foot prostheses that provide net positive push-off work can reduce the metabolic rate of walking for individuals with amputation, but benefits might be sensitive to push-off timing. Simple walking models suggest that preemptive push-off reduces center-of-mass work, possibly reducing metabolic rate. Studies with bilateral exoskeletons have found that push-off beginning before leading leg contact minimizes metabolic rate, but timing was not varied independently from push-off work, and the effects of push-off timing on biomechanics were not measured. Most lower-limb amputations are unilateral, which could also affect optimal timing. The goal of this study was to vary the timing of positive prosthesis push-off work in isolation and measure the effects on energetics, mechanics and muscle activity. Methods: We tested 10 able-bodied participants walking on a treadmill at 1.25 m.s(-1). Participants wore a tethered ankle-foot prosthesis emulator on one leg using a rigid boot adapter. We programmed the prosthesis to apply torque bursts that began between 46% and 56% of stride in different conditions. We iteratively adjusted torque magnitude to maintain constant net positive push-off work. Results: When push-off began at or after leading leg contact, metabolic rate was about 10% lower than in a condition with Spring-like prosthesis behavior. When push-off began before leading leg contact, metabolic rate was not different from the Spring-like condition. Early push-off led to increased prosthesis-side vastus medialis and biceps femoris activity during push-off and increased variability in step length and prosthesis loading during push-off. Prosthesis push-off timing had no influence on intact-side leg center-of-mass collision work. Conclusions: Prosthesis push-off timing, isolated from push-off work, strongly affected metabolic rate, with optimal timing at or after intact-side heel contact. Increased thigh muscle activation and increased human variability appear to have caused the lack of reduction in metabolic rate when push-off was provided too early. Optimal timing with respect to opposite heel contact was not different from normal walking, but the trends in metabolic rate and center-of-mass mechanics were not consistent with simple model predictions. Optimal push-off timing should also be characterized for individuals with amputation, since meaningful benefits might be realized with improved timing

Virtual prototyping of a semi-active transfemoral prosthetic leg

This article presents a virtual prototyping study of a semi-active lower limb prosthesis to improve the functionality of an amputee during prosthesis–environment interaction for level ground walking. Articulated ankle–foot prosthesis and a single-axis semi-active prosthetic knee with active and passive operating modes were considered. Data for level ground walking were collected using a photogrammetric method in order to develop a base-line simulation model and with the hip kinematics input to verify the proposed design. The simulated results show that the semi-active lower limb prosthesis is able to move efficiently in passive mode, and the activation time of the knee actuator can be reduced by approximately 50%. Therefore, this semi-active system has the potential to reduce the energy consumption of the actuators required during level ground walking and requires less compensation from the amputee due to lower deviation of the vertical excursion of body centre of mass

Towards a Smart Semi-Active Prosthetic Leg: Preliminary Assessment and Testing

This paper presents a development of a semi-active prosthetic knee, which can work in both active and passive modes based on the energy required during the gait cycle of various activities of daily livings (ADLs). The prosthetic limb is equipped with various sensors to measure the kinematic and kinetic parameters of both prosthetic limbs. This prosthetic knee is designed to be back-drivable in passive mode to provide a potential use in energy regeneration when there negative energy across the knee joint. Preliminary test has been performed on transfemoral amputee in passive mode to provide some insight to the amputee/prosthesis interaction and performance with the designed prosthetic knee