Pathological and age-related changes may affect an individual's gait, in turn raising the risk of falls. In elderly, falls are common and may eventuate in severe injuries, long-term disabilities, and even death. Thus, there is interest in estimating the risk of falls from gait analysis. Estimation of the risk of falls requires consideration of the longitudinal evolution of different variables derived from human gait. Bayesian networks are probabilistic models which graphically express dependencies among variables. Dynamic Bayesian networks (DBNs) are a type of BN adequate for modeling the dynamics of the statistical dependencies in a set of variables. In this work, a DBN model incorporates gait derived variables to predict the risk of falls in elderly within 6 months subsequent to gait assessment. Two DBNs were developed; the first (DBN1; expert-guided) was built using gait variables identified by domain experts, whereas the second (DBN2; strictly computational) was constructed utilizing gait variables picked out by a feature selection algorithm. The effectiveness of the second model to predict falls in the 6 months following assessment is 72.22%. These results are encouraging and supply evidence regarding the usefulness of dynamic probabilistic models in the prediction of falls from pathological gait.

Spasticity has been successfully managed with different treatment modalities or combinations. No information is available on the effectiveness or individual contribution of botulinum toxin type A (BTA) combined with physical and occupational therapy and neuromuscular electrical stimulation to treat spastic upper limb. The purpose of this study was to assess the effects of such treatment and to inform sample-size calculations for a randomized controlled trial. BTA was injected into spastic upper limb muscles of 10 children. They received 10 sessions of physical and occupational therapy followed by 10 sessions of neuromuscular electrical stimulation on the wrist extensors (antagonist muscles). Degree of spasticity using the Modified Ashworth scale, active range of motion, and manual function with the Jebsen hand test, were assessed. Meaningful improvement was observed in hand function posttreatment (P = 0.03). Median spasticity showed a reduction trend and median amplitude of wrist range of motion registered an increase; however, neither of these were significant (P > 0.05). There is evidence of a beneficial effect of the combined treatment. Adequate information has been obtained on main outcome-measurement variability for calculating sample size for a subsequent study to quantify the treatment effect precisely.

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The purpose of this study is to develop a system capable of performing calculation of temporal gait parameters using two low-cost wireless accelerometers and artificial intelligence-based techniques as part of a larger research project for conducting human gait analysis. Ten healthy subjects of different ages participated in this study and performed controlled walking tests. Two wireless accelerometers were placed on their ankles. Raw acceleration signals were processed in order to obtain gait patterns from characteristic peaks related to steps. A Bayesian model was implemented to classify the characteristic peaks into steps or nonsteps. The acceleration signals were segmented based on gait events, such as heel strike and toe-off, of actual steps. Temporal gait parameters, such as cadence, ambulation time, step time, gait cycle time, stance and swing phase time, simple and double support time, were estimated from segmented acceleration signals. Gait data-sets were divided into two groups of ages to test Bayesian models in order to classify the characteristic peaks. The mean error obtained from calculating the temporal gait parameters was 4.6%. Bayesian models are useful techniques that can be applied to classification of gait data of subjects at different ages with promising results.