RESUMO
Human-machine information transfer through tactile excitation has addressed new applications in virtual reality, robotics, telesurgery, sensory substitution and rehabilitation for the handicapped in the past few years. Power consumption is an important factor in the design of vibrotactile displays, because it affects energy needs and the size, weight, heat dissipation and cost of the associated electronics. An experimental study is presented on the power required to reach tactile thresholds in electromechanical and piezo-electric transducers. Three different waveforms are considered, with an excitatory period formed by a burst of rectangular 50% duty cycle pulses (R50), rectangular low duty cycle pulses (RLO) and sinusoidal pulses (SIN). Ten different pulse repetition periods (RPs) were considered in the range 1/550-1/25 s. The voltage and current waveforms applied to the transducers at sensation thresholds in a group of 12 healthy subjects were sampled and stored in a digital oscilloscope. The average power was determined for each subject, and differences of two orders of magnitude were measured between the electromechanical and the piezo-electric transducer power consumption. Results show that, for the electromechanical transducer, a smaller power consumption of 25 microW was determined for RP = 1/25 s and the RLO waveform. In the case of the piezo-electric transducer, power of 0.21 microW was determined for SIN excitation and RP = 1/250 s. These results show the advantages of reducing power requirements for vibrotactile displays, which can be optimised by the choice of appropriate types of transducer, excitatory waveforms and pulse repetition periods.
Assuntos
Estimulação Física/instrumentação , Auxiliares Sensoriais , Transdutores , Adulto , Fontes de Energia Elétrica , Humanos , Tato , VibraçãoRESUMO
This work is part of a project to develop an expert system for automated classification of the sleep/waking states in human infants; i.e. active or rapid-eye-movement sleep (REM), quiet or non-REM sleep (NREM), including its four stages, indeterminate sleep (IS) and wakefulness (WA). A model to identify these states, introducing an objective formalisation in terms of the state variables characterising the recorded patterns, is presented. The following digitally recorded physiological events are taken into account to classify the sleep/waking states: predominant background activity and the existence of sleep spindles in the electro-encephalogram; existence of rapid eye movements in the electro-oculogram; and chin muscle tone in the electromyogram. Methods to detect several of these parameters are described. An expert system based on artificial ganglionar lattices is used to classify the sleep/waking states, on an off-line minute-by-minute basis. Algorithms to detect patterns automatically and an expert system to recognise sleep/waking states are introduced, and several adjustments and tests using various real patients are carried out. Results show an overall performance of 96.4% agreement with the expert on validation data without artefacts, and 84.9% agreement on validation data with artefacts. Moreover, results show a significant improvement in the classification agreement due to the application of the expert system, and a discussion is carried out to justify the difficulties of matching the expert's criteria for the interpretation of characterising patterns.