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.

People with neurological disorders like Cerebral Palsy (CP) and Multiple Sclerosis (MS) suffer associated functional gait problems. The symptoms and sign of these gait deficits are different between subjects and even within a subject at different stage of the disease. Identifying these gait related abnormalities helps in the treatment planning and rehabilitation process. The current gait assessment process does not provide very specific information within the seven gait phases. The objective of this study is to investigate the possible application of granular computing to quantify gait parameters within the seven gait phases. In this process we applied fuzzy-granular computing on the vertical ground reaction force (VGRF) and surface electromyography (sEMG) data to obtain respective characteristic values for each gait phase. A fuzzy similarity (FS) measure is used to compare patient values with age and sex matched control able-bodied group. We specifically applied and tested this approach on 10 patients (4 Cerebral Palsy and 6 Multiple Sclerosis) to identify possible gait abnormalities. Different FS values for VGRF for right and left leg is observed. The VGRF analysis shows smaller FS values during the swing phase in CP and MS subjects that are evidence of associated stability problem. Similarly, FS values for muscle activates of the four-selected muscle display a broad range of values due to difference between subjects. Degraded FS values for different muscles at different stage of the gait cycle are reported. Smaller FS values are sign of abnormal activity of the respective muscles. This approach provides individual centered and very specific information within the gait phases that can be employed for diagnosis, treatment and rehabilitation process

The authors have applied a systems analysis approach to describe the musculoskeletal system as consisting of a stack of superimposed kinematic hierarchical segments in which each lower segment tends to transfer its motion to the other superimposed segments. This segmental chain enables the derivation of both conscious perception and sensory control of action in space. This applied systems analysis approach involves the measurements of the complex motor behavior in order to elucidate the fusion of multiple sensor data for the reliable and efficient acquisition of the kinetic, kinematics and electromyographic data of the human spatial behavior. The acquired kinematic and related kinetic signals represent attributive features of the internal reconstruction of the physical links between the superimposed body segments. Indeed, this reconstruction of the physical links was established as a result of the fusion of the multiple sensor data. Furthermore, this acquired kinematics, kinetics and electromyographic data provided detailed means to record, annotate, process, transmit, and display pertinent information derived from the musculoskeletal system to quantify and differentiate between subjects with mobility-related disabilities and able-bodied subjects, and enabled an inference into the active neural processes underlying balance reactions. To gain insight into the basis for this long-term dependence, the authors have applied the fusion of multiple sensor data to investigate the effects of Cerebral Palsy, Multiple Sclerosis and Diabetic Neuropathy conditions, on biomechanical/neurophysiological changes that may alter the ability of the human locomotor system to generate ambulaHow to cite this paper:

Patients with mild traumatic brain injury complain about having balance and stability problems despite normal clinical examination. The objective of this study is to investigate the stride-to-stride gait variability of mTBI subjects while walking on treadmill under dual-task gait protocols. Fuzzy-granular computing algorithm is used to objectively quantify the stride-to-stride variability of temporal gait parameters. The degrees of similarity (DS) of temporal gait parameters in the dual tasks were determined from the corresponding granulated time-series. The mTBI group showed relatively smaller degree of similarity for all window sizes under the cognitive (dual) task walking, showing pronounced stride-to-stride variability. Different levels of DS among the mTBI subjects were observed. Individually, both healthy and mTBI group showed different DS under the two dual-tasks, reflecting the challenging level of the cognitive tasks while walking. The mean values of the temporal parameters for the mTBI group were different from the averaged normal reference. On the other hand, the individual variance analysis shows no significant differences between the normal and dual task values for some mTBI subjects. The granular approach however is able to reveal very fine differences and exhibited similar trends for all mTBI subjects. Different DS values among mTBI group could be indicative for the different severity level or the undergone rehabilitation process.