The new kid on the block: compressed sensing

05 April, 2016

According to the World Health Organization, cardiovascular diseases are the number one cause of death worldwide, responsible for an estimated 17.5 million deaths in 2012 (i.e. 31% of all deaths worldwide) and economic fallout in billions.

In order to combat cardiovascular and other diseases, current healthcare infrastructures are increasingly unsuitable due to escalating levels of supervision, medical management, and associated healthcare costs. For example, in 2010, the USA alone spent 17% of its GDP in healthcare with costs rising every year.

A consensus around the need for a cost-effective next-generation advanced patient monitoring relies on patient-centric eHealth solutions. One option is to develop wearable personal health systems based on wireless body sensors (WBSN).

What are WBSN solutions?

WBSN-enabled eHealth solutions consist of outfitting patients with wearable, miniaturized and wireless sensors that can measure, pre-process and wirelessly report various physiological, metabolic and kinematic biosignals (e.g. ECG, body temperature, SpO2, non-invasive blood pressure) to tele-health providers. This would enable the required personalized, long-term and real-time remote monitoring of chronic patients, its seamless integration with the patient’s medical record and its coordination with nursing/medical support.

The limitations of WBSN

However, WBSN are critically resource constrained by limited power supply, memory, processing performance and communication bandwidth. Energy efficiency is necessary in every level of operations (e.g. sensing, computing, and transmission) for its successful deployment.

Compressed sensing has the potential to revolutionize the sensor design for not only WBSN but also active implantables.


Technical components


What is compressed sensing?

Compressed sensing is based on the principle that, through optimization, the sparsity of a signal can be exploited to recover it from far fewer samples than required by the Shannon-Nyquist sampling theorem. Traditional data acquisition acquires the entire signal at the beginning, then does the compression and throws away most of information away at the end. The new idea combines signal acquisition and compression as one step which improves the overall efficiency significantly.

This new approach opens novel ways for low-cost sensor design and ultra-low power hardware processing platforms. Novel designs are already emerging. One example is the single-pixel compressive camera, which is capable of obtaining an image using a single detection element (the "single pixel") while measuring the scene fewer times than the number of pixels. It is also claimed that the camera can be adapted to image at wavelengths where conventional CCD and CMOS imagers are blind.

Compressed sensing is a relatively new approach and largely driven by academia. It will take several years and substantial financial investment, but compressed sensing will be key to delivering cost effective eHealth solutions in the next decade.

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