Instrumentation & Measurement Magazine 23-2 - 34

Fig. 4. Using a bioinspired artificial lateral line to monitor fishway effectiveness. (a) Fishways are structures consisting of a series of pools to aid upstream
migrating fish around dams, after [9]. (b) An artificial lateral line probe consists of a fish-shaped body outfitted with two arrays of pressure sensors. (c) The
probe is placed at different pool locations and records body-oriented pressure fluctuations. (d) The results of the bioinspired sensor are the autonomous spatial
classification of three distinct flow types, which are known to be preferred or avoided by fish during upstream migration. This is in contrast to conventional, pointbased measurements which rely on expert judgement and do not take the sensing abilities of fish into account.

migration around barriers, especially during the spawning
season. Unfortunately, experience has shown that building a
fishway does not guarantee successful passage. After spending
billions of Euro on building fishways, the international knowledge gathered over the last 40 years has led to regulations and
guidelines providing specific technical requirements for fishways. To satisfy these regulations, fishway monitoring has
become an international commercial enterprise. A wide range
of monitoring technologies are deployed, including acoustic
velocity probes, infrared scanners, and SONAR imaging.
Current fishway monitoring technologies are largely limited to measurements of static, time-averaged flow fields. Due
to the lack of time-varying data, maps of the flow velocity
from pool to pool are created using static point measurements
or numerical models. The maps relate the flow velocity
and turbulence to observations of fish migration efficiency
34	

from laboratory and field investigations. The mapping procedure requires expensive equipment and cumbersome
measurements as well as significant post-processing. In order
to improve fishway monitoring, a bioinspired artificial lateral
line probe was developed. The probe is outfitted with an array
of pressure transducers which captures changes in the flow
acting over a fish-shaped body. Machine learning algorithms
perform unsupervised classification using the pressure data.
Compared to the state-of-the-art, this provides a more accurate
and efficient mapping system for real-world flows encountered during fishway monitoring. A graphical depiction of
the bioinspired fishway monitoring process is shown in Fig. 4.

Challenges
In stark contrast to conventional land-based sensors, underwater devices must withstand large pressures, water intrusion,

IEEE Instrumentation & Measurement Magazine	

April 2020



Instrumentation & Measurement Magazine 23-2

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