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Neural network based smart accelerometers for use in telecare medicine.

Gaura EI., Rider RJ., Steele N., Naguib RNG.

Systems Science

2000

Vol. 26, nr 3

83-96

Sensors that perform the task of measuring the physical quantity of acceleration are discussed. Applications for such measurements and thus of accelerometers, range from early diagnosis procedures for tremor-related diseases (e.g. Parkinsons) to monitoring daily patterns of patient activity using telemetry systems. The system-level requirements in such applications are considered and two novel neural network transducer designs developed by the authors are presented which aim to satisfy such requirements. Both designs are based on a micromachined sensing element with capacitive signal pick-off. The first is an open-loop design utilising a direct inverse control strategy, whilst the second is a closed-loop design where electrostatic actuation is used as a form of feedback. Both transducers are nonlinearly compensated, capable of self-test and provide digital outputs.

A comparison of approaches for the design of closed-loop micromachined accelerometers.

Gaura E. I., Kraft M., Steele N., Rider R.J.

Systems Science

1999

Vol. 25, nr 4

47-62

Traditionally, closed-loop actuation has been used for many sensors to increase the bandwidth dynamic range and to linearise the response of the transducer. In this paper, three control approaches are described with application to a bulk-micromachined accelerometer with capacitive signal pick-off. This device has inherent nonlinear properties in open-loop operation. All three approaches rely on balancing the inertial force acting on the proof mass by electrostatic actuation, the magnitude of which provides a measure of the acceleration signal The first approach is a simple analogue , linear control strategy based on proportional, integral and derivative control action. The second approach uses a digital control strategy, based on proportional, integral control action. The second approach used a digital control strategy, based on proportional, integral control action. The third approach employs a novel strategy based upon an artificial neural network. Simulation results suggest that a closed-loop accelerometer with neural network control will have a more stable behaviour and a wider dynamic range than its analogue or digital counterparts.