Margins of postural stability in Parkinson’s disease: an application of control theory

Introduction: Postural instability is a restrictive feature in Parkinson’s disease (PD), usually assessed by clinical or laboratory tests. However, the exact quantification of postural stability, using stability theorems that take into account human dynamics, is still lacking. We investigated the feasibility of control theory and the Nyquist stability criterion—gain margin (GM) and phase margin (PM)—in discriminating postural instability in PD, as well as the effects of a balance-training program.Methods: Center-of-pressure (COP) data of 40 PD patients before and after a 4-week balance-training program, and 20 healthy control subjects (HCs) (Study1) as well as COP data of 20 other PD patients at four time points during a 6-week balance-training program (Study2), collected in two earlier studies, were used. COP was recorded in four tasks, two on a rigid surface and two on foam, both with eyes open and eyes closed. A postural control model (an inverted pendulum with a Proportional-integral-derivative (PID) controller and time delay) was fitted to the COP data to subject-specifically identify the model parameters thereby calculating |GM| and PM for each subject in each task.Results: PD patients had a smaller margin of stability (|GM| and PM) compared with HCs. Particularly, patients, unlike HCs, showed a drastic drop in PM on foam. Clinical outcomes and margins of stability improved in patients after balance training. |GM| improved early in week 4, followed by a plateau during the rest of the training. In contrast, PM improved late (week 6) in a relatively continuous-progression form.Conclusion: Using fundamental stability theorems is a promising technique for the standardized quantification of postural stability in various tasks

cGAN-Based High Dimensional IMU Sensor Data Generation for Therapeutic Activities

Human activity recognition is a core technology for applications such as rehabilitation, ambient health monitoring, and human-computer interactions. Wearable devices, particularly IMU sensors, can help us collect rich features of human movements that can be leveraged in activity recognition. Developing a robust classifier for activity recognition has always been of interest to researchers. One major problem is that there is usually a deficit of training data for some activities, making it difficult and sometimes impossible to develop a classifier. In this work, a novel GAN network called TheraGAN was developed to generate realistic IMU signals associated with a particular activity. The generated signal is of a 6-channel IMU. i.e., angular velocities and linear accelerations. Also, by introducing simple activities, which are meaningful subparts of a complex full-length activity, the generation process was facilitated for any activity with arbitrary length. To evaluate the generated signals, besides perceptual similarity metrics, they were applied along with real data to improve the accuracy of classifiers. The results show that the maximum increase in the f1-score belongs to the LSTM classifier by a 13.27% rise when generated data were added. This shows the validity of the generated data as well as TheraGAN as a tool to build more robust classifiers in case of imbalanced data problem

Evaluating a new verbal working memory-balance program: a double-blind, randomized controlled trial study on Iranian children with dyslexia

Abstract: Background: It is important to improve verbal Working Memory (WM) in reading disability, as it is a key factor in learning. There are commercial verbal WM training programs, which have some short-term effects only on the verbal WM capacity, not reading. However, because of some weaknesses in current verbal WM training programs, researchers suggested designing and developing newly structured programs that particularly target educational functions such as reading skills. In the current double-blind randomized clinical trial study, we designed a new Verbal Working Memory-Balance (VWM-B) program which was carried out using a portable robotic device. The short-term effects of the VWM-B program, on verbal WM capacity, reading skills, and postural control were investigated in Iranian children with developmental dyslexia. Results: The effectiveness of the VWM-B program was compared with the VWM-program as a traditional verbal WM training. In comparison with VWM-program, the participants who received training by the VWM-B program showed superior performance on verbal WM capacity, reading skills, and postural control after a short-term intervention. Conclusions: We proposed that the automatized postural control resulting from VWM-B training had a positive impact on improving verbal WM capacity and reading ability. Based on the critical role of the cerebellum in automatizing skills, our findings support the cerebellar deficit theory in dyslexia. Trial registration: This trial was (retrospectively) registered on 8 February 2018 with the Iranian Registry of Clinical Trials (IRCT20171219037953N1)

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

ABSTRACT: 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

Vibration Reduction in Robots Using Gain Optimization

ABSTRACT This paper focuses on vibration reduction in robots by optimizing their control gains. Conventional methods for gain tuning are mostly based on trial and error, which are very time consuming, and require experienced personnel to tune the gains correctly. In this work we use optimization to tune the gains of an industrial motion controller. Although the optimization tuning method is described and examined on a specific robot, the method is general and can be applied to any servo system. The gains of a PID and second order feedforward controller, along with the gains of an anti-resonance notch filter are considered in the optimization procedure

A postural control model to assess the improvement of balance rehabilitation in Parkinson's disease

Studies have shown that balance and mobility in people with Parkinson's disease (PD) can improve through rehabilitation interventions. However, until now no quantitative method investigated how these patients improve their balance control. In this study, a single inverted pendulum model with PID controller was used to describe the improvement of forty PD patients after a 12-session therapy program, and to compare their balance with twenty healthy subjects. The Center of Pressure (COP) data were recorded in seven sensory conditions - on rigid and foam surface, each with eyes open and closed, and with visual disturbance; and stance on rigid surface with attached vibrator to the Achilles tendons. From COP data four Stabilogram Diffusion Function (SDF) measures were extracted. In order to find the appropriate model parameters (three control parameters and a noise gain) from the SDF measures, first model simulations were performed to tune an artificial neural network (ANN) which relates the SDF measures to the PID parameters, and second the trained ANN was used to find the suitable PID model parameters from the experimentally recorded SDF measures. Statistical analysis revealed that patients had lower control parameters and noise gain than healthy subjects; confirming reduced control ability and sensory information in PDs. Balance rehabilitation improved the patients' clinical scores, which is reflected in the increased control parameters (particularly in foam tasks), and noise gain (in tasks on rigid surface). The presented method provides a good and sensitive measure to describe functional balance and mobility in PD.</p