Thompson Sarkodie-Gyan

Application of wearable sensors for human gait analysis using fuzzy computational algorithm

Abstract The authors have developed and tested a wearable inertial sensor system for the acquisition of gait features. The sensors were placed on anatomical segments of the lower limb: foot, shank, thigh, and hip, and the motion data were then captured in conjunction with 3D ground reaction forces (GRFs). The method of relational matrix was applied to develop a rule-based system, an intelligent fuzzy computational algorithm. The rule-based system provides a feature matrix model representing the strength of association or interaction amongst the elements of the gait functions (limb-segments accelerations and GRFs) throughout the gait cycle. A comparison between the reference rule-based data and an input test data was evaluated using a fuzzy similarity algorithm. This system was tested and evaluated using two subject groups: 10 healthy subjects were recruited to establish the reference fuzzy rule-base, and 4 relapsing remitting multiple sclerosis subjects were used as an input test data; and the grade of similarity between them was evaluated. This similarity provides a quantitative assessment of mobility state of the impaired subject. This algorithmic tool may be helpful to the clinician in the identification of pathological gait impairments, prescribe treatment, and assess the improvements in response to therapeutic intervention.

Automatic classification of pathological gait patterns using ground reaction forces and machine learning algorithms

An automated gait classification method is developed in this study, which can be applied to analysis and to classify pathological gait patterns using 3D ground reaction force (GRFs) data. The study involved the discrimination of gait patterns of healthy, cerebral palsy (CP) and multiple sclerosis subjects. The acquired 3D GRFs data were categorized into three groups. Two different algorithms were used to extract the gait features; the GRFs parameters and the discrete wavelet transform (DWT), respectively. Nearest neighbor classifier (NNC) and artificial neural networks (ANN) were also investigated for the classification of gait features in this study. Furthermore, different feature sets were formed using a combination of the 3D GRFs components (mediolateral, anterioposterior, and vertical) and their various impacts on the acquired results were evaluated. The best leave-one-out (LOO) classification accuracy 85% was achieved. The results showed some improvement through the application of a features selection algorithm based on M-shaped value of vertical force and the statistical test ANOVA of mediolateral and anterioposterior forces. The optimal feature set of six features enhanced the accuracy to 95%. This work can provide an automated gait classification tool that may be useful to the clinician in the diagnosis and identification of pathological gait impairments.

Determination of Human Gait Phase Using Fuzzy Inference

This paper discusses the design and implementation of a fuzzy inference system for the recognition of human gait phases. In particular, this work focuses on using the angles of the joints of lower limb to determine the current stage of a subjects gait cycle. The fuzzy rule-based system was developed using typical joint angle trajectories over a single gait cycle. The behavior of each joint was examined to determine appropriate rules for differentiating between gait phases. The completed system was then tested using joint angle trajectories measured from healthy human test subjects and shown to be capable of reproducing the gait phase transitions found by a human expert.

Gait Variability while walking with three different speeds

Gait Variability is defined as changes in gait parameters from one stride to the next. Gait variability increases in individuals affected by neurodegenerative conditions such as Parkinsons disease and Huntington disease, and also with falls in the elderly and incident mobility disability. In this work, we study speed-related and age-related gait variabilities in healthy adults. Ten participants, five females (three young and two middle-aged) and five males (three young and two middle-aged) were recruited to walk on an instrumented treadmill for three minutes in this study. The gait variables (stride length, stride width, stride time and stride velocity) were extracted and processed from camera motion system. Results: slow speed walking increased gait variability with all gait variables; only stride width variability was increased significantly in middle aged subjects compared with the young subjects (p≪0.05), there were no changes in other variables. Based on gait variability differences in age, height and body mass, we propose to design a knowledge-base membership function of gait variability for all the related gait variables with different heights and weights (BMI) as cofactors in each age group. An automatic diagnostic tool, Fuzzy Inferential Reasoning system, will help the clinician to identify pathological impairment from normal.

An approach to sensor fusion in medical robots

This paper proposes a multi-sensor data fusion technique to determine the complex interactions between the sensory, muscular and mechanical components of the human locomotor systems (neuromechanics). We investigated the use of an array of accelerometers, rate gyroscopes, force plates and electromyogram for the assessment of x-y-z components of the hip, thigh, shank and foot acceleration and velocity in the sagittal plane. The objective here is to determine the impact factor each body segments acceleration has in terms of producing a functional gait. Six able-bodied subjects were employed in this experiment; and subject walked at three different speeds and his/her dynamic/kinematic behaviors of the lower limb recorded with the sensors afore mentioned. Eight distinct gait variability for each subject recorded was detected by our sensing system. Fuzzy fusion technique was employed to evaluate the empirical results and the output matrix shows the relation between the body segments in question in terms of their x-y-z acceleration components. The implementation of this fusion matrix will enhance modeling and building medical robots intended for paraplegic rehabilitation as well as intelligent mobile robots for effective industrial manufacturing.

Dynamically Removing Partial Body Mass Using Acceleration Feedback for Neural Training

This paper describes a control strategy for an active body suspension system for treadmill based neural rehabilitation or training devices. Using an acceleration feedback, the system behaves like dynamically removing part or all of the body mass of the trainee so that he/she will truly feel like having a reduced mass while being trained for walking, jogging, or other leg activities on a treadmill. It will be shown that the proposed controller can compensate any amount of inertia force which would not be present as if the trainee had a real reduced mass. As a result, the dynamic load on the trainees body as well as the supporting legs during an exercise will also be reduced correspondingly. Simulation results are presented to demonstrate the benefits of the actively controlled body suspension system.

Identification of Human Gait using Fuzzy Inferential Reasoning

The restoration of healthy locomotion (gait) after stroke, traumatic brain injury, and spinal cord injury, is a major task in neurological rehabilitation. The rehabilitation process is labor intensive. Patient evaluation is often subjective, foiling determination of precise rehabilitation goals and assessment of treatment effects. To date it is the experienced clinician who continues to perform functional gait assessment and training in the absence of virtually any technological assistance. This paper introduces an algorithm capable of identifying human gait patterns. The fuzzy inferential reasoning uses typical joint angle trajectories to identify varying gait patterns. The algorithm will, thus, offer doctors, therapists, and patients a significant tool to assess the efficacy and outcomes of medical rehabilitation therapies and practices.

Application of fuzzy sets for assisting the physician's model of functional impairments in human locomotion

The authors have developed a system to assist clinicians reliably assess, at an early post-insult stage, the degree of disability the patient will ultimately experience. Physician decision processes offered to date, especially those relative to diagnosis and patient treatment, suffer from the inability to incorporate all useful data on the patient. We present a computational intelligence algorithm based on fuzzy clustering the theory of fuzzy sets and systems techniques to aid the physician to evaluate the complete representation of information emanating from the measured kinetic, kinematics and electromyographic data from the patient. The fuzzy clustering technique helps develop membership functions as an optimization task. The calculated membership grades are organized in the form of optimized partition matrix. As the optimization method operates on available data, it attempts to reflect their characteristics in the resulting constructs, e.g. a distribution of the prototypical values of the clusters.

Assessment of functional impairment in human locomotion: A fuzzy-motivated approach

Many neurological disorders result in disordered motion. The effects of a disorder can be decrease by an appropriate rehabilitation. To make rehabilitation efficient, we need to monitor the patient and check how well he or she improves. In our previous papers, we proposed a fuzzy-based semi-heuristic method of gauging how well a patient improved. Surprisingly, this semi-heuristic method turned out to be more efficient that we expected. In this paper, we provide a justification for this efficiency. In the future, it is desirable to combine this fuzzy-assessment approach with results by Alavarez-Alvarez, Trivino, and Cordón who use fuzzy techniques for modeling human gait.

Recognition and decision-making algorithm in human locomotion based on the principles of fuzzy reasoning

The authors introduce a fuzzy rule-based algorithm to evaluate the activation patterns of muscles of the lower limb with respect to the gait phases during normal human walking. A relational matrix was established as a Cartesian product between the activation behaviors of muscles of the lower limb within the seven gait phases during normal walking. This relational matrix is an expression of the strength of association between the muscles and the gait phases. The resulting knowledge-base, therefore, depicts the relationship between the muscles in the respective gait phases during normal walking tasks. The cross-correlation between an input relational matrix and the knowledge base will provide a diagnostic assessment of the neurological state of the subject.