ayyoob jafari

Deaths due to road traffic accidents (RTAs) are a major public health concern around the world. Developing countries are over-represented in these statistics. Punitive measures are traditionally employed to lower RTA related behavioural risk factors. These are, however, resource intensive and require infrastructure development. This is a randomised controlled study to investigate the effect of non-punitive behavioural intervention through peer-comparison feedback based on driver behaviour data gathered by an in-vehicle telematics device. Design, Setting, and Participants: A randomised controlled trial using repeated measures design conducted in Iran on the drivers of 112 public transport taxis in Tehran province and 1309 inter-city busses operating nationwide. Driving data is captured by an in-vehicle telematics device and sent to a centrally located data centre using a mobile network. The telematics device is installed in all vehicles. Participants are males aged above 20 who have had the device operating in their vehicles for at least 3 months prior to the start of the trial. Intervention: The study had three stages: 1- Driver performance was monitored for a 4-week period after which they were randomised into intervention and control groups. 2- Their performance was monitored for a 9-week period. At the end of each week, drivers in the intervention group received a scorecard and a note informing them of their weekly behaviour and ranking within their peer group. Drivers in the control group received no feedback via short messaging service (SMS). 3- Drivers did not receive further feedback and their behaviour was monitored for another 4 weeks. Primary and Secondary Outcome Measure: Primary outcome was changes in weekly driving score in intervention and control groups during stage 2 of intervention. Taxis and busses were analysed separately using generalised estimating equation analysis. Funding and Ethical Approval: This project was funded by the National Institute for Medical Research Development (Grant No.940576) and approved by its ethics committee (Code: IR.NIMAD.REC.1394.016). This trial was registered at www.irct.ir as IRCT20180708040391N1.

In this study, we focused on the gait of Parkinson’s disease (PD) and presented a gray box model for it. We tried to present a model for basal ganglia structure in order to generate stride time interval signal in model output for healthy and PD states. Because of feedback role of dopamine neurotransmitter in basal ganglia, this part is modelled by “Elman Network”, which is a neural network structure based on a feedback relation between each layer. Remaining parts of the basal ganglia are modelled with feed-forward neural networks. We first trained the model with a healthy person and a PD patient separately. Then, in order to extend the model generality, we tried to generate the behaviour of all subjects of our database in the model. Hence, we extracted some features of stride signal including mean, variance, fractal dimension and five coefficients from spectral domain. With adding 10% tolerance to above mentioned neural network weights and using genetic algorithm, we found proper parameters to model every person in the used database. The following points may be regarded as clues for the acceptability of our model in simulating the stride signal: the high power of the network for simulating normal and patient states, high ability of the model in producing the behaviour of different persons in normal and patient cases, and the similarities between the model and physiological structure of basal ganglia.