T. Sarkodie-Gyan

Gait pattern of severely disabled hemiparetic subjects on a new controlled gait trainer as compared to assisted treadmill walking with partial body weight support

Objectives: To investigate to what extent and with how much therapeutic effort nonambulatory stroke patients could train a gait-like movement on a newly developed, machine-supported gait trainer. Design: Open study comparing the movement on the gait trainer with assisted walking on the treadmill. Setting: Motion analysis laboratory of a rehabilitation centre. Subjects: Fourteen chronic, nonambulatory hemiparetic patients. Intervention: Complex gait analysis while training on the gait trainer and while walking on the treadmill. Main outcome measures: Gait kinematics, kinesiological EMG of several lower limb muscles and the required assistance. Results: Patients could train a gait-like movement on the gait trainer, characterized kinematically by a perfect symmetry, larger hip extension during stance, less knee flexion and less ankle plantar flexion during swing as compared to treadmill walking (p <0.01). The pattern and amount of activation of relevant weight-bearing muscles was comparable with an even larger activation of the M. biceps femoris on the gait trainer (p <0.01). The tibialis anterior muscle of the nonaffected side, however, was less activated during swing (p <0.01). Two therapists assisted walking on the treadmill while only one therapist was necessary to help with weight shifting on the new device. Conclusion: The newly developed gait trainer offered severely disabled hemiparetic subjects the possibility of training a gait-like, highly symmetrical movement with a favourable facilitation of relevant anti-gravity muscles. At the same time, the effort required of the therapists was reduced.

Asymmetry of gait initiation in hemiparetic stroke subjects

OBJECTIVEnTo investigate symmetry of gait initiation in healthy and hemiparetic subjects.nnnDESIGNnSurvey.nnnSETTINGnKinematic laboratory affiliated with a hospital-based department of rehabilitation.nnnPATIENTS OR OTHER PARTICIPANTSnTen healthy and 14 hemiparetic stroke subjects starting five times with their right and left leg, respectively.nnnMAIN OUTCOME MEASURESnDuration of defined periods, step length, center of pressure, and center of mass were recorded and calculated using two triaxial force plates, contact switches, and a video camera system.nnnRESULTSnHealthy subjects displayed a high degree of independence of kinetic and kinematic parameters of the starting limb. Hemiparetic patients showed differences with respect to the starting limb: when starting with the nonaffected leg, the swing period and step length was shorter and the center of pressure displayed a more marked medio-lateral sway with no corresponding initial movement of the center of mass; when starting with the affected leg the movement pattern of the center of pressure and center of mass was comparable to that of normal subjects.nnnCONCLUSIONSnThe trajectories of the center of pressure and center of mass and the symmetry parameters are in accordance with a higher degree of uncertainty when starting with the non-affected limb in hemiparetic subjects.

Entwicklung eines Gangtrainers mit geregeltem Servoantrieb zur Rehabilitation gehunfähiger Patienten - Development of a Servo-controlled Gait Trainer for the Rehabilitation of Non-ambulatory Patients

The aim of the present study was to develop a new gait trainer for the rehabilitation of non-ambulatory patients. For the simulation of the gait phases, we used a commercially available fitness trainer (Fast Track) with two foot plates moving in an alternating fashion and connected to a servo-controlled propulsion system providing the necessary support for the movement depending on the patients impairment level. To compensate deficient equilibrium reflexes, the patient was suspended in a harness capable of supporting some of his/her weight. Video analysis of gait and the kinesiological EMG were used to assess the pattern of movement and the corresponding muscle activity, which were then evaluated in healthy subjects, spinal cord injured and stroke patients and compared with walking on the flat or on a treadmill. Walking on the gait trainer was characterised by a symmetrical, sinusoidal movement of lower amplitude than in normal gait. The EMG showed a low activity of the tibialis anterior muscle, while the antigravity muscles were clearly activated by the gait trainer during the stance phase. In summary, the new gait trainer generates a symmetrical gait-like movement, promoting weight acceptance in the stance phase, which is important for the restoration of walking ability.

A prototype indexing approach to 2-D object description and recognition

This paper introduces an indexing approach to 2-D object description and recognition in the presence of rotation, translation, scale, and partial occlusion of objects. The scheme is based on the polygonal approximations of object boundaries. To obtain stable features to represent 2-D objects, three polygonal approximations are computed for the objects using different line fitting tolerances. Local structural features of objects are extracted, which consist of line and circular arc elements. The indexing entries computed based on the initial features are employed for fast access to the object-model database and generating hypotheses. A dynamic feature-matching method is designed to implement final-shape matching by evaluating and verifying the hypotheses. The system is tested with both manufactured workpieces and prototype-test-objects, and the experimental results are presented.

An efficient object recognition scheme for a prototype component inspection

An object recognition scheme for inspection of high tolerances in manufactured engine components has been developed and tested. A mechano-optical arrangement is used to generate and capture images of the components. A simple edge detection algorithm and an effective feature transformation plus indexing technique are applied to extract and analyse the features of the images. A classification scheme based on fuzzy membership functions is proposed. Some promising results of feature extraction, feature analysis and classification are shown.

A threshold strategy for edge tracking

In this paper, we address a threshold strategy based on fuzzy sets theory, which can be used to guide an edge tracking execution. First, we briefly introduce the principle of edge tracking technique. A set of edge models is proposed. The cost functions are defined not only according to the change of local gradient magnitude but also to the change of local gradient directions. A set of thresholds is defined in terms of the histogram of edge enhanced images to determine the start and the end of edge tracking. So that, no matter how much the intensity levels of images vary, the algorithm will always gain the best edge extraction. The fuzzy sets concept is used to combine the human linguistic language into the operation of the determination of edge contrast to the background. A more acceptable interface is established according to the human visual perception. The new algorithm has been tested on several real world images and the experimental results are given.

Gas-sensing System Using an Array of Coated Quartz Crystal Microbalances with a Fuzzy Inference System

Quartz crystal microbalances have high mass sensitivities. Their application in gas sensing has been limited because they are required to have both high selectivity and reversibility. Yet by the inherent nature of their operation these properties are mutually exclusive. One approach to this problem is to use an array of quartz crystal microbalances. We have used an array of six coated quartz crystal microbalances for the classification of methanol, propan-1-ol, butan-1-ol, hexane, heptane and toluene. A novel classification scheme using fuzzy membership functions was found to be highly efficient.

Organic vapour sensing using a coated piezoelectric quartz crystal sensor array

The pattern of responses from a four sensor array have been used for the classification of methanol, propanol, butanol, hexane, heptane and toluene using artificial intelligence (AI) based pattern recognition methods. A feedforward forward network with backpropagation was trained using sensor array data with approximately 300 training vectors and 100 test cases and covering a period of four months. The network consisting of four input nodes, six output nodes, learning rate of 0.1 and momentum of 0 was built using a commercial package (NeuroShell). A classification success rate of 75% was achieved. The bulk of the mis-classifications arose from propanol being classified as butanol and hexane being classified as heptane. These mis-classifications are rational since the respective compounds are very similar in nature. A fuzzy logic algorithm where class membership functions are developed using the mean frequency change and standard deviation of individual sensors was developed for classification of the vapors. In this particular case, classification using the developed fuzzy logic Gaussian algorithm was not as good as the feedforward network with backpropagation, but the Gaussian membership function offers a more rational approach than the previously published trapezoidal membership function.

Development of a novel image sensor and its application to analysis of automobile components

The authors have developed a novel image sensor for continuous conditioned monitoring of high-precision tolerances of a complex automotive product. A novel mechanooptical arrangement has been designed and validated to capture the images/silhouettes of the components as input into a neural network designed on approximate reasoning architecture. The design is extensible to handle a large number of rules, and the speed of inference is almost independent of the number of rules.

A fuzzy clustering method for efficient 2-D object recognition

Advances in image processing architecture have provided speed and inspection capabilities previously not attainable for machine vision applications. Methods for improving the quality of visual data have stored great interest. Image acquisition has become increasingly important for the analysis of complex scenes where grey scale, colour, depth, texture and/or motion information is present. In this paper, we illustrate our design of a prototype system for the diagnosis of high tolerances in machined or cast components that copes with uncertainty and performs approximate reasoning since information used in decision-making or reasoning processes in advanced manufacturing metrology could be uncertain, imprecise, or incomplete. In the design, we employ fuzzy logic based on fuzzy sets theory. Inference procedures that incorporate uncertainty are becoming more important in rule-based expert-like systems. The design is extensible to handle a large number of rules, and the speed of inference is almost independent of the number of rules.