Intelligent real-time postural control monitoring system for self-rehabilitation systems

The essence of all human motion is maintaining postural alignment while standing upright. Reliable sensory information and its proper integration for the Central Nervous System (CNS) is necessary for human body postural control. Loss of sensory information resulting from injuries (previous or current) or aging is seen to be the leading cause of falls. Poor postural control is a strong indicator of degrading or a loss of sensory information to the CNS. Loss and degradation of certain sensory information, such as the proprioception, can be retrained through rehabilitation by providing augmented or compensatory signals to the CNS. However, conventional rehabilitation, referred to as face-to-face therapy, is known to incur high costs. Therefore, this thesis looks to design and develop a real-time approach for monitoring and correcting postural control, with the view of enabling and promoting self-rehabilitation routines. Self-rehabilitation routines allow for certain rehabilitation procedures to be conducted independent of professionals and therapists, without compromising the final outcome of rehabilitation. Rehabilitation in this work is considered with regard to monitoring occurrences of postural instability and the steps leading towards correction and restoration of posture. A Force Sensing Platform (FSP) which is sensitive to changes of the postural control was designed. The platform was capable of measuring effects of postural control in static and dynamic conditions. The FSP functioned as a qualitative tool for initial on-the-spot postural control assessment and quantitative measurement tool for post-acquisition assessment of balancing abilities. The design and implementation of the FSP presents the potential of incorporating commonly available Force Sensing Resistor (FSR) as a sensing element for gauging and quantifying postural control. The FSP functions to test and gauge postural control of individuals by monitoring pressure distribution at the feet, while providing real-time quantitative information to end-user. Real-time qualitative assessment assisted end users in visually identifying areas of the feet with high force concentration. Soft-computing techniques were applied to the measured data, for post-acquisition analysis which provided quantitative measure of postural control. Successful implementation of FSRs in the FSP improved mobility of the designed platform, allowing for outside laboratory data acquisition which is a desirable feature for self-rehabilitation. An intelligent bio-feedback system was also designed, using vibrotactile feedback. Vibration actuators (vibrotactors) have a simplistic manner of providing augmented or complementary feedback to the CNS using pulses of vibration at varying levels. The designed approach presents a real-time biofeedback system, which uses artificial intelligence for decision making. Artificial intelligence was used to decide on the severity of measured postural control, which then determined the level of feedback provided in real-time. The feedback provided acted as a forewarning mechanism to the CNS. The system relied on miniature kinematic sensors for measures relating to postural control, ensuring a compact and lightweight design. The designed systems demonstrated competence in clinical and biomedical applications, with potential of being expanded into self-rehabilitation. Components of the systems presented simple attachments with minimal calibration, and were capable of providing reliable and consistent measurements. The results in this work demonstrated the viability of the prototypes designed for monitoring an improving postural control for self-rehabilitation purposes. This work was supported in part by Moves Fitness International, USA and the Ministry of Science, Technology and Innovation Malaysia (MOSTI)

Assistive vibrotactile biofeedback system for postural control on perturbed surface.

Postural control is an important aspect of human locomotion and stance. When inputs to theCentral Nervous System (CNS), consisting of the vestibular, somatosensory, and visual senses,degrade or become dysfunctional, the postural control is a®ected. Biofeedback has beenestablished as a potential intervention method to assist individuals improve postural control, byaugmenting or complementing signals to the CNS. This paper presents an approach to helpachieve better postural control using vibrotactile biofeedback. Tests to monitor postural con-trol, in eyes open and eyes closed states, on a wobble board were introduced to assess theviability of the designed system in providing accurate real-time biofeedback responses. Posturalcontrol was gauged by measuring the angular displacement of perturbations experienced.Perturbations along the anterior and posterior direction are used to determine the level ofprovided vibrotactile biofeedback. The feedback informs subjects the severity of perturbationand direction of imbalance. Signi¯cant improvement (p-value < 0:05) in postural control whileon perturbed surface was detected when the designed biofeedback system was used. Thewearable system was found to be e®ective in improving postural control of the subjects and canbe expanded for rehabilitation, conditioning, and strengthening applications dealing withhuman postural control

Ground-Image Plane Mapping for Lane marks detection

Autonomous vehicles are equipped with optical sensors and micro-processing units to perform intelligent visual analysis of its surroundings. Due to the high speed of moving vehicle, the captured information has to be processed in a short duration to avoid possible collision. In this paper, aground-image plane mapping technique is proposed to quickly locate detected object if the object’s position is known in the real world. A three dimensional (3D) world coordinate is mathematically derived to an image plane using pinhole camera model. Several 3D perspective parameters such as vehicle’s steering angle and its velocity, sensor’s height and tilting angle are encompassed in the ground plane measurement. The optical sensor’s intrinsic parameters such as focal length, principal point, pixel’s height and width are also inserted for the mathematical model derivation. The importance of this ground to image plane mapping enables a rapid search of an object in a moving scene to achieve fast object identification during sensor acquisition. Experimental results have been carried on the application of lane marks detection with 93.82% correct mapping, using approximately 20% less processing time

Robotic Vision System Design for Black Pepper Harvesting

Robotic vision system design is developed in this paper to locate the coordinate of pepper fruits from trees and leaves, and identify pepper ripeness for harvest in Sarawak region, Malaysia. The vision system comprises of three stages, i.e. salient point localization, contour extraction and pepper verification. First, ripe peppers are spotted using visual saliency detection based on color, intensity and orientation. Three most salient regions are then determined by red component detection, whereas red element indicates a ripe pepper region. The detected red salient region is therefore shrunk to pepper edges using active contour method. To further verify the correct detection of peppers, the extracted edges are required to match with pre-defined shape, and to check neighborhoods similarity surrounding the region of interest. Preliminary simulation results showed that the vision system spotted the salient regions with pepper in 91.3% of success rate; contour extractions covering a pepper boundary with 84.35% of success rate and the results for pepper verification stage are promising

Intelligent vibrotactile biofeedback system for real-time postural correction on perturbed surfaces

Biofeedbacks delivery during rehabilitation have been known to improve postural control and shorten rehabilitation periods. A biofeedback system communicates with the human central nervous system (CNS) through a variety of feedback modalities. Among the many available modalities vibrotactile feedback devices are gaining much attention. This is due to their desirable characteristics and simplistic manner of presenting information to the CNS. An intelligent biofeedback system integrated with wireless sensors for monitoring postural control during rehabilitation was hypothesized to shorten rehabilitation periods. This work presents the design of a postural control measuring device integrated with real-time intelligent biofeedback for postural correction. The system integrates three modules: (a) inertial measurement units (IMUs), (b) fuzzy knowledge base, and (c) feedback driver circuit. Human posture is measured using Euler angular measurements from the IMUs. A fuzzy inference system (FIS) was used to determine quality of postural control, based on measurements from the IMUs. Forewarning of poor postural control is given by vibrotactile actuators (biofeedback). Experiments were conducted to test viability of the system in achieving accurate real-time measurements and interventions. The results observed improvements in postural control when biofeedback intervention was present

Parkinson’s disease diagnosis and severity assessment using ground reaction forces and neural networks

Gait analysis plays a key role in the diagnosis of Parkinson’s Disease (PD), as patients generally exhibit abnormal gait patterns compared to healthy controls. Current diagnosis and severity assessment procedures entail manual visual examinations of motor tasks, speech, and handwriting, among numerous other tests, which can vary between clinicians based on their expertise and visual observation of gait tasks. Automating gait differentiation procedure can serve as a useful tool in early diagnosis and severity assessment of PD and limits the data collection to solely walking gait. In this research, a holistic, non-intrusive method is proposed to diagnose and assess PD severity in its early and moderate stages by using only Vertical Ground Reaction Force (VGRF). From the VGRF data, gait features are extracted and selected to use as training features for the Artificial Neural Network (ANN) model to diagnose PD using cross validation. If the diagnosis is positive, another ANN model will predict their Hoehn and Yahr (H&Y) score to assess their PD severity using the same VGRF data. PD Diagnosis is achieved with a high accuracy of 97.4% using simple network architecture. Additionally, the results indicate a better performance compared to other complex machine learning models that have been researched previously. Severity Assessment is also performed on the H&Y scale with 87.1% accuracy. The results of this study show that it is plausible to use only VGRF data in diagnosing and assessing early stage Parkinson’s Disease, helping patients manage the symptoms earlier and giving them a better quality of life

Measuring Human Balance on an Instrumented Dynamic Platform: A Postural Sway Analysis

A system to monitor the trajectory and distribution of Center of Pressure (CoP) oscillations in real-time was designed. The system used a custom built force plate that measured sway area and sway velocity based on the measured CoP. A stable posture is reflected by a controlled CoP oscillation, where the oscillation lies within the limits of stability. Large magnitudes of CoP oscillations (large sway area) indicate weak proprioception strength and a heightened risk of falls. Experiments carried out involved self-induced perturbations that destabilized postural control among volunteers with active and inactive lifestyles. The observed results from the experiment indicate that individuals with active lifestyle have better postural control than individuals with inactive lifestyle. Subjectswith active lifestyles demonstrated greater sway velocities, while maintaining a small sway area

Muscles affecting minimum toe clearance

The aim of this study was to investigate how the anterior and posterior muscles in the shank (Tibialis Anterior, Gastrocnemius Lateralis and Medialis), influence the level of minimum toe clearance (MTC). With aging, MTC deteriorates thus, greatly increasing the probability of falling or tripping. This could result in injury or even death. For this study, muscle activity retention taping (MART) was used on young adults, which is an accepted method of simulating a poor MTC—found in elderly gait. The subject's muscle activation was measured using surface electromyography (SEMG), and the kinematic parameters (MTC, knee and ankle joint angles) were measured using an optical motion capture system. Our results indicate that MART produces significant reductions in MTC (P < α), knee flexion (P < α) and ankle dorsiflexion (P < α), as expected. However, the muscle activity increased significantly, contrary to the expected result (elderly individuals should have lower muscle activity). This was due to the subject's muscle conditions (healthy and strong), hence the muscles worked harder to counteract the external restriction. Yet, the significant change in muscle activity (due to MART) proves that the shank muscles do play an important role in determining the level of MTC. The Tibialis Anterior had the highest overall muscle activation, making it the primary muscle active during the swing phase. With aging, the shank muscles (specifically the Tibialis Anterior) would weaken and stiffen, coupled with a reduced joint range of motion. Thus, ankle-drop would increase—leading to a reduction in MTC