Effect of Sensory Manipulations on Human Joint Stiffness Strategy and Its Adaptation for Human Dynamic Stability

Sensory input plays an important role to human posture control system to initiate strategy in order to counterpart any unbalance condition and thus, prevent fall. In previous study, joint stiffness was observed able to describe certain issues regarding to movement performance. But, correlation between balance ability and joint stiffness is still remains unknown. In this study, joint stiffening strategy at ankle and hip were observed under different sensory manipulations and its correlation with conventional clinical test (Functional Reach Test) for balance ability was investigated. In order to create unstable condition, two different surface perturbations (tilt up-tilt (TT) down and forward-backward (FB)) at four different frequencies (0.2, 0.4, 0.6 and 0.8 Hz) were introduced. Furthermore, four different sensory manipulation conditions (include vision and vestibular system) were applied to the subject and they were asked to maintain their position as possible. The results suggested that joint stiffness were high during difficult balance situation. Less balance people generated high average joint stiffness compared to balance people. Besides, adaptation of posture control system under repetitive external perturbation also suggested less during sensory limited condition. Overall, analysis of joint stiffening response possible to predict unbalance situation faced by human

Walking gait event detection based on electromyography signals using artificial neural network

In many gait applications, the focal events are the stance and swing phases. Although detecting gait events using electromyography signals will help the development of assistive devices such as exoskeleton, orthoses, and prostheses, stance and swing phases have yet to be observed using electromyography signals. The core of this study is to propose a classification system for both stance and swing phases based on electromyography signals. This is to be done by extracting the patterns of electromyography signals from time domain features and feeding them into an artificial neural network classifier. In addition, a different number of input features and two prominent training algorithm of artificial neural network have been employed in this study. Eight subjects that participated in this study were divided into two categories namely, learned (first seven subjects) and unlearned data (the remaining one subject). It was observed that Levenberg-Marquardt algorithm with five time domain features performed better than other features with an average percentage of classification accuracy of 87.4%. This system was further tested with electromyography signals of learned and unlearned data to identify the stance and swing phases in order to detect the timing of heel strike and toe off. The mean absolute different values between artificial neural network and footswitch data for learned data were 16 ± 18 ms and 21 ± 18 ms for heel strike and toe off, respectively. For this case, no significant differences (p < 0.05) were observed in mean absolute different for heel strike and toe off detections. Besides, the mean absolute different values of unlearned data were shown to be acceptable, 35 ± 25 ms for heel strike and 49 ± 15 ms for toe off. By the end of this experiment, basing the examination of gait events with electromyography signals using artificial neural network is possible

Estimation of transition frequency during continuous translation surface perturbation

Depending on task requirements, a human is able to select distinct strategies such as the use of an ankle strategy and hip strategy to maintain their balance. Postural control actions often co-occur with other movements, and such movements may bring about a change from one type of postural coordination to another. The selection of a postural control strategy has typically been investigated by the transition of the center of mass (COM), center of pressure (COP), and in between angle joint motion along with their characteristics. In this paper, we proposed a method using the logistic function of the sigmoid model based on cross-correlation coefficient (CCF) data for investigating and observing the transition of postural control strategies of COM-COP and ankle-hip angles towards anterior-posterior (AP) continuous translation perturbation. Subjects were required to stand on the motion base platform where perturbations with an increasing frequency (0.2 Hz to 0.8 Hz) and decreasing frequency (0.8 Hz to 0.2 Hz) in steps of 0.02 Hz, were induced. As the frequency increased, the COM and COP displacements were decreased, with the opposite trend observable with decreasing frequency. This pattern was also observed at the head peak-to-peak amplitude. Meanwhile, ankle and hip angular displacements were increased during increasing frequency and decreased during decreasing frequency. In this paper, the proposed sigmoid model could identify the transition frequency of COM-COP and ankle-hip transition. The mean transition frequency of COM-COP during increasing frequency was 0.44 Hz, and the ankle-hip transition frequency was 0.42 Hz. Meanwhile, for decreasing frequency, the COM-COP transition frequency was 0.55 Hz, and for the ankle-hip transition the frequency was 0.56 Hz. With frequencies, both increasing and decreasing, the COM-COP and ankle-hip transition frequencies occurred almost at the same frequency. Furthermore, the transition occurred at a lower time scale during increasing frequency compared to decreasing frequency. In conclusion, the continuous translation surface perturbation provided information on the behavior of postural control strategies. A sudden change in 'phase angle' was observed, where either an ankle or hip strategy was implemented to maintain balance. Besides, the transition frequency of postural control strategies could be determined to occur between 0.4 Hz and 0.6 Hz, based on the average value, for healthy young subjects in the AP plane. Furthermore, the proposed sigmoid model was believed to be able to be used in the determination of transition frequency in postural control strategies

Ankle joint stiffness and damping pattern under different frequency of translation perturbation

The change of e ffective stiffness and damping characteristic of ankle joint are able to indicate degeneration of balance ability due to ageing effect. This paper will discuss the ankle joint stiffness and damping pattern along repeat ed translation perturbation. Six young healthy subjects were exposed to five tr ials of five different frequencies of perturbation (quiet standing, 0.2 Hz, 0.4 Hz, 0.6 Hz and 0.8 Hz). The result showed that the me an of effective stif fness was reduced with the increase of frequency applied ; meanwhile the mean of damping value increased wit h increasing frequency . Additionally , a cubic polynomial curve (u - shape) was estimated to represent stiffness pattern when using curve fitting method with correlation R 2 >0.5 . These estimation s also suggested that ankle joint does not oscillate like sprin g - damper system which is based on inverted pendulum model ; however , it applied a different strategy to maintain balance, in particular during initiation, middle and termination of perturbation. These also indicate the influence of sensory processing and ad aptation to maintain balance under a long period of disturbance . On the other hand, damping pattern seems to be similar over different frequenc ies and under repeat ed perturbation . Besides, the change of stiffness patter n at higher frequency o f perturbati on (0.8 Hz) recommends the change in posture strategy from ankle to hip strategy . The se findings indicated that stiffness and damping are able to describe adaptation of human posture strategy to keep balance and motor learnin g under repeat ed perturbation

Assist-as-Needed Control of a Robotic Orthosis Actuated by Pneumatic Artificial Muscle for Gait Rehabilitation

Rehabilitation robots are designed to help patients improve their recovery from injury by supporting them to perform repetitive and systematic training sessions. These robots are not only able to guide the subjects’ lower-limb to a designate trajectory, but also estimate their disability and adapt the compliance accordingly. In this research, a new control strategy for a high compliant lower-limb rehabilitation orthosis system named AIRGAIT is developed. The AIRGAIT orthosis is powered by pneumatic artificial muscle actuators. The trajectory tracking controller based on a modified computed torque control which employs a fractional derivative is proposed for the tracking purpose. In addition, a new method is proposed for compliance control of the robotic orthosis which results in the successful implementation of the assist-as-needed training strategy. Finally, various subject-based experiments are carried out to verify the effectiveness of the developed control system

Design and Evaluation of the AIRGAIT Exoskeleton: Leg Orthosis Control for Assistive Gait Rehabilitation

This paper introduces the body weight support gait training system known as the AIRGAIT exoskeleton and delves into the design and evaluation of its leg orthosis control algorithm. The implementation of the mono- and biarticular pneumatic muscle actuators (PMAs) as the actuation system was initiated to generate more power and precisely control the leg orthosis. This research proposes a simple paradigm for controlling the mono- and bi-articular actuator movements cocontractively by introducing a cocontraction model. Three tests were performed. The first test involved control of the orthosis with monoarticular actuators alone without a subject (WO/S); the second involved control of the orthosis with mono- and bi-articular actuators tested WO/S; and the third test involved control of the orthosis with mono- and bi-articular actuators tested with a subject (W/S). Full body weight support (BWS) was implemented in this study during the test W/S as the load supported by the orthosis was at its maximum capacity. This assessment will optimize the control system strategy so that the system operates to its full capacity. The results revealed that the proposed control strategy was able to co-contractively actuate the mono- and bi-articular actuators simultaneously and increase stiffness at both hip and knee joints

Inverse modeling of nonlinear artificial muscle using polynomial parameterization and particle swarm optimization

The properties of pneumatic artificial muscle (PAM) with excellent power-to-weight ratio and natural compliance made it useful for healthcare engineering applications. However, it has undesirable hysteresis effect in controlling a robotic manipulator. This behavior is quasistatic and quasirate dependent which changed with excitation frequency and external force. Apart from this, it also inherits frictional presliding behavior with nonlocal memory effect. These nonlinearities need to be compensated to achieve optimal performance of the control system. Even though an inverse modeling of PAM has limited application, it is important on certain control system implementation that requires the solution to the inverse problem. In this paper, the inverse modeling of PAM in the form of activation pressure was proposed. This activation pressure model was derived according to static pressure and extracted hysteresis components from pressure/length hysteresis. The derivation of the static pressure model follows the phenomenological-based model of third-order polynomial. It is capable of characterizing the nonlinear region of PAM at low and high pressure. The derivation of extracted hysteresis model follows the mechanism of dynamic friction. In this principle, the activation pressure model was dependent on regression coefficient of the static pressure model and dynamic friction coefficients of the extracted hysteresis model. The regression constants of these coefficients were characterized from the hysteresis dataset by using model parameter identification and the particle swarm optimization (PSO) method. The result from model simulation shows the root mean square error (RMSE) value of less than 10% error was evaluated at various excitation frequencies and external forces. This inverse modeling of PAM implemented a simple approach, but it should be useful in control design applications such as rehabilitation robotics, biomedical system, and humanoid robots

Distinct bilateral prefrontal activity patterns associated with the qualitative aspect of working memory characterized by individual sensory modality dominance.

In addition to quantitative individual differences in working memory (WM) capacity, qualitative aspects, such as enhanced sensory modality (modality dominance), can characterize individual WM ability. This study aimed to examine the neurological basis underlying the individual modality dominance component of WM using functional near-infrared spectroscopy (fNIRS). To quantify the degree of individual WM modality dominance, 24 participants were required to find seven hidden targets and hold their spatial location and appearance order with vibrotactile or visual stimuli aids. In this searching task, eight participants demonstrated higher performance with the tactile condition (tactile-dominant) whereas sixteen demonstrated visual dominance. We then measured prefrontal activity by fNIRS during memorization of visual stimulus numbers while finger tapping as a cognitive-motor dual-task. Individual modality dominance significantly correlated with bilateral frontopolar and dorsolateral prefrontal activity changes over repeated fNIRS sessions. In particular, individuals with stronger visual dominance showed marked decreases in prefrontal area activity. These results suggest that distinct processing patterns in the prefrontal cortex reflect an individual's qualitative WM characteristics. Considering the individual modality dominance underlying the prefrontal areas could enhance cognitive or motor performance, possibly by optimizing cognitive resources