An Adaptive Sampling System for Sensor Nodes in Body Area Networks

Abstract

The importance of body sensor networks to monitor patients over a prolonged period of time has increased with an advance in home healthcare applications. Sensor nodes need to operate with very low-power consumption and under the constraint of limited memory capacity. Therefore, it is wasteful to digitize the sensor signal at a constant sample rate, given that the frequency contents of the signals vary with time. Adaptive sampling is established as a practical method to reduce the sample data volume. In this paper a low-power analog system is proposed, which adjusts the converter clock rate to perform a peak-picking algorithm on the second derivative of the input signal. The presented implementation does not require an analog-to-digital converter or a digital processor in the sample selection process. The criteria for selecting a suitable detection threshold are discussed, so that the maximum sampling error can be limited. A circuit level implementation is presented. Measured results exhibit a significant reduction in the average sample frequency and data rate of over 50% and 38%, respectively.Index Terms-Adaptive sampling, analog electronics, analog signal processing, bio-signal recording, sensor networks.

Event-based sampling for reducing communication load in realtime human motion analysis by wireless inertial sensor networks

“…In adaptive sampling, the sampling rate is adjusted based on the signal history. See [10] for a realization of adaptive sampling as an analog circuit and [11] for an example of adaptive sampling applied to general sensor networks. For model-based active sampling, a model is used to predict sensor readings.…”

Towards Affordable Home Health Care Devices Using Reconfigurable System-on-Chip Technology

“…Adaptive sampling is established as a practical method to reduce the sample data volume. Robert Rieger et al Rieger & Taylor (2009) proposed a low-power analog system, which adjusts the converter clock rate to perform a peak-picking algorithm on the second derivative of the input signal. Their proposed ADC clocking scheme operates the converter at minimum sampling frequency and increases the clock rate only during phases of high curvature (i.e., second derivative) of the signal, essentially performing a peak-picking algorithm on this derivative.…”

Integrated control circuit for adaptive sampling

“…The principle of 2nd-order peak picking is discussed in detail in [13,15]. The algorithm selects sample points predominantly at instances of high curvature of the input signal as represented by its 2nd derivative.…”

“…in a data logging application) can be reduced. As typical naturally occurring signals contain a high amount of redundancy, data reduction can be based on the rejection of samples which are predictable from other samples, yielding asynchronous compression [4][5][6][7][8][9][10][11][12][13]. Conventionally, data compression is performed in the digital domain, after sampling the signal at a constant rate [11] or with a rate controlled by a digital processor [12].…”

“…non-uniform data sampling. We have recently proposed an analog system which performs sample picking for straight-line reconstruction based on the 2nd derivative of the input signal [13]. The preliminary circuit was implemented using discrete devices; this paper presents a fully integrated controller.…”