A neuromechanical model characterizing the motor planning and posture control in the voluntary lean in Parkinson's disease.

Parkinson's disease targets patients' cognitive and motor abilities, including postural control. Many studies have been carried out to introduce mathematical models for a better understanding of postural control in such patients and the relation between the model parameters and the clinical assessments. So far, these studies have addressed this connection merely in static tests, such as quiet stance. The aim of this study is to develop a model for voluntary lean, and as such, identify the model parameters for both PD patients and healthy subjects from experimental data. The proposed model comprises planning and control sections. The model parameters for the planning section were extracted from the time response characteristics. Parameters for the control section were identified based on the spatial characteristics of the center-of-pressure (COP) response using an optimization process. 24 PD patients along with 24 matched healthy subjects participated in the study. The results showed a significant difference between the two groups in terms of temporal parameters for the planning section. This difference emphasizes bradykinesia as an essential symptom of PD. Also, differences were found for the postural control section. In all directions, the proportional gain of the feedback controller was significantly larger in PD patients; however, the gain of the feedforward controller was significantly smaller in PD patients. Furthermore, the control gains were strongly correlated with the clinical scales (Functional Reach Test and Unified Parkinson's Disease Rating Scale) in certain directions. In conclusion, the new model helps to better understand and quantify some PD symptoms in voluntary lean tasks.

Overground Walking With a Transparent Exoskeleton Shows Changes in Spatiotemporal Gait Parameters.

Lower-limb gait training (GT) exoskeletons have been successfully used in rehabilitation programs to overcome the burden of locomotor impairment. However, providing suitable net interaction torques to assist patient movements is still a challenge. Previous transparent operation approaches have been tested in treadmill-based GT exoskeletons to improve user-robot interaction. However, it is not yet clear how a transparent lower-limb GT system affects user's gait kinematics during overground walking, which unlike treadmill-based systems, requires active participation of the subjects to maintain stability. In this study, we implemented a transparent operation strategy on the ExoRoboWalker, an overground GT exoskeleton, to investigate its effect on the user's gait. The approach employs a feedback zero-torque controller with feedforward compensation for the exoskeleton's dynamics and actuators' impedance. We analyzed the data of five healthy subjects walking overground with the exoskeleton in transparent mode (ExoTransp) and non-transparent mode (ExoOff) and walking without exoskeleton (NoExo). The transparent controller reduced the user-robot interaction torque and improved the user's gait kinematics relative to ExoOff. No significant difference in stride length is observed between ExoTransp and NoExo (p = 0.129). However, the subjects showed a significant difference in cadence between ExoTransp (50.9± 1.1 steps/min) and NoExo (93.7 ± 8.7 steps/min) (p = 0.015), but not between ExoTransp and ExoOff (p = 0.644). Results suggest that subjects wearing the exoskeleton adjust their gait as in an attention-demanding task changing the spatiotemporal gait characteristics likely to improve gait balance.

Upper-Limb Rehabilitation of Patients with Neuromotor Deficits Using Impedance-Based Control of a 6-DOF Robot.

This paper presents a study on the use of impedance-based control of a 6-degree-of-freedom robot for upper-limb rehabilitation of patients with neuromotor deficits. The control strategy is based on impedance and does not require external force sensors at the end-effector for implementation. The experimental setup involved using the control algorithm to move the robot to a desired position, follow a desired trajectory while being moved out of the trajectory by the user, and reproduce three different rehabilitation exercises (passive, isometric, and ADL). The results suggest that the parameters of the control strategy can be adjusted to set the robot's compliance and support force according to the patient's needs. Ultimately, the study concluded that the proposed control strategy can serve as a foundation for rehabilitation robots, which could potentially improve access to continued upper-limb rehabilitation for stroke patients and reduce the number of healthcare professionals required per patient.Clinical Relevance- This paper proposes a human-robot interaction control scheme to be used in a robotic rehabilitation tool, with the goal of assisting both clinicians and patients during the rehabilitation exercises and ultimately reducing the load on healthcare system. This platform can be potentially used in telemedicine solutions for upper-limb rehabilitation of a wide range of patients with neuromotor deficits such as stroke.

A Systematic Study on Electromyography-Based Hand Gesture Recognition for Assistive Robots Using Deep Learning and Machine Learning Models.

Upper limb amputation severely affects the quality of life and the activities of daily living of a person. In the last decade, many robotic hand prostheses have been developed which are controlled by using various sensing technologies such as artificial vision and tactile and surface electromyography (sEMG). If controlled properly, these prostheses can significantly improve the daily life of hand amputees by providing them with more autonomy in physical activities. However, despite the advancements in sensing technologies, as well as excellent mechanical capabilities of the prosthetic devices, their control is often limited and usually requires a long time for training and adaptation of the users. The myoelectric prostheses use signals from residual stump muscles to restore the function of the lost limbs seamlessly. However, the use of the sEMG signals in robotic as a user control signal is very complicated due to the presence of noise, and the need for heavy computational power. In this article, we developed motion intention classifiers for transradial (TR) amputees based on EMG data by implementing various machine learning and deep learning models. We benchmarked the performance of these classifiers based on overall generalization across various classes and we presented a systematic study on the impact of time domain features and pre-processing parameters on the performance of the classification models. Our results showed that Ensemble learning and deep learning algorithms outperformed other classical machine learning algorithms. Investigating the trend of varying sliding window on feature-based and non-feature-based classification model revealed interesting correlation with the level of amputation. The study also covered the analysis of performance of classifiers on amputation conditions since the history of amputation and conditions are different to each amputee. These results are vital for understanding the development of machine learning-based classifiers for assistive robotic applications.

Identification of human balance control responses to visual inputs using virtual reality.

Human upright balance is maintained through feedback mechanisms that use a variety of sensory modalities. Vision senses information about the position and velocity of the visual surround motion to improve balance by reducing the sway evoked by external disturbances. This study characterized the effects of visual information on human anterior-posterior body sway in upright stance by presenting perturbations through a virtual reality system. This made it possible to use a new visual perturbation signal, based on trapezoidal velocity pulses, whose amplitude and velocity could be controlled separately. To date, the influences of visual field position and velocity have only been studied independently due to the experimental limitations. The hip displacement, ankle torques, shank angles, and surface EMGs of four major ankle muscles were measured bilaterally as outputs. We found that the root mean square (RMS) hip displacement (body angle) increased systematically with visual input amplitude. However, for each amplitude, the RMS body angle increased when input velocity was changed from 2 to 5 degrees per second (dps) and then decreased from 5 to 10 dps. Spectral analysis was used to compute frequency response over a frequency range from 0.04 to 0.6 Hz. The gain of body sway relative to the perturbation increased with frequency, whereas the coherence declined. Moreover, as the stimulus amplitude increased, the gain generally decreased, whereas the mean coherence values always increased. The mean gains and mean coherence values were greatest for the velocity of 5 dps. This study presents a novel experimental approach to study human postural control and augments our knowledge of how visual information is processed in the central nervous system to maintain balance.NEW & NOTEWORTHY In this paper, we developed a new methodological approach to study the effects of visual information on dynamic body sway. We used VR to apply visual perturbations to induce AP body sway. We designed a new visual stimulus waveform based on trapezoidal velocity pulses whose peak-to-peak amplitude and velocity could be modulated independently. Subsequently, we investigated how the amplitude and velocity of visual field motion influence the postural responses evoked in healthy adults.

Contributions of Vision in Human Postural Control: A Virtual Reality-based Study.

During human standing, it has been previously observed that information about the position and frequency of visual surround motion improves balance by reducing sway responses to external disturbances. However, experimental limitations only allowed for independent investigation of such parameters while being incapable of providing a fully immersive experience of a real environment. The aim of this study is to investigate the effect of visual information on dynamic body sway in the human upright stance by presenting perturbations through a virtual reality (VR) system. Moreover, we designed a new perturbation signal based on trapezoidal velocity (TrapV) pulses enabling us to simultaneously examine the effects of amplitude and velocity on balance control. The experiments included four different peak-to-peak amplitudes (1-10 degrees), and three velocities (2-10 degree/sec). The body angle, ankle torques and shank angles were measured and analyzed in response to each perturbation. The results reveal that stimuli with higher amplitudes evoked larger responses, while they were initially increased and reached a peak, then decreased by increasing the motion velocity of visual surround.

Experimental Methods to Study Human Postural Control.

Many components of the nervous and musculoskeletal systems act in concert to achieve the stable, upright human posture. Controlled experiments accompanied by appropriate mathematical methods are needed to understand the role of the different sub-systems involved in human postural control. This article describes a protocol for performing perturbed standing experiments, acquiring experimental data, and carrying out the subsequent mathematical analysis, with the aim of understanding the role of musculoskeletal system and central control in human upright posture. The results generated by these methods are important, because they provide insight into the healthy balance control, form the basis for understanding the etiology of impaired balance in patients and the elderly, and aid in the design of interventions to improve postural control and stability. These methods can be used to study the role of somatosensory system, intrinsic stiffness of ankle joint, and visual system in postural control, and may also be extended to investigate the role of vestibular system. The methods are to be used in the case of an ankle strategy, where the body moves primarily about the ankle joint and is considered a single-link inverted pendulum.

Molecular prevalence of JC virus in Tehran, Iran.

AIM: Since data about prevalence of JC virus in Iranian population is scarce, this study was designed to evaluate the prevalence of JC virus in healthy individuals who had attended Fajr hospital and Farjam clinical laboratory in Tehran, Iran. BACKGROUND: JC virus is the causative agent of progressive multifocal encephalopathy (PML) in individuals with suppressed immune system. There are some evidences that this virus is responsible for some forms of cancers for example colorectal and gastric cancers in humans. PATIENTS AND METHODS: Urine samples from 133 healthy individuals older than 18 years old were collected and after extraction of viral DNA, PCR was performed to determine the presence of virus. Results of the test and demographic data of subjects were entered into SPSS program and were analyzed by it. RESULTS: 71 subjects were male and 62 individuals were female. Mean age of the population was 42.23 ± 13.47. From the total number of 133 subjects, 51 (38.3%) individuals were positive for the presence of JC virus. Gender had statistically significant relationship with JC virus presence (p= 0.042). Age was not significantly related to JC virus presence status (p= 0.3). CONCLUSION: Obtained rate of JC prevalence in this study is similar to the results of studies in India and Philippine. Because of this virus's role in AIDS and the role of this virus in gastrointestinal cancers have been revealed in recent years, the more extended studies on the prevalence of this virus in different populations in Iran is necessary.