A miniaturized threshold-triggered acceleration data-logger for recording burst movements of aquatic animals

Nishiumi, Nozomi; Matsuo, Ayane; Kawabe, Ryo; Payne, Nicholas L.; Huveneers, Charlie; Watanabe, Yuki; Kawabata, Youhei

doi:10.1242/jeb.172346

Cited by 7 publications

(3 citation statements)

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“…(Watanuki et al 2007) and (Volpov et al 2015) took this a step farther by incorporating a depth sensor, allowing their cameras to only trigger when an animal surpassed a predefined depth threshold. (Nishiumi et al 2018) deployed devices with two acceleration sensors, using a low-cost (low-frequency) acceleration sensor to activate a second high-cost (high-frequency) acceleration sensor when a preset threshold had been surpassed. Finally, (Brown et al 2012) measured the variance from their low-cost acceleration sensor to dynamically adjust the sampling rate of their high-cost GPS sensor based on predetermined threshold values.…”

Section: Discussionmentioning

confidence: 99%

“…Here we show how AI can be leveraged on board these devices to intelligently control their activation of costly sensors, e.g., video cameras, allowing them to make the most of their limited resources during long deployment periods. Our method goes beyond previous works that have proposed controlling such costly sensors using simple threshold-based triggers, e.g., depth-based (Watanuki et al 2007; Volpov et al 2015) and acceleration-based (Nishiumi et al 2018; Brown et al 2012) triggers. Using AI-assisted biologgers, biologists can focus their data collection on specific complex target behaviors such as foraging activities, allowing them to automatically record video that captures only the moments they want to see.…”

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confidence: 96%

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AI on animals: AI-assisted animal-borne logger never misses the moments that biologists want

Korpela

Suzuki

Matsumoto

et al. 2019

Preprint

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14Animal-borne data loggers, i.e., biologgers, allow researchers to record a variety of sensor data 15 from animals in their natural environments (Hussey et al. 2015; Kays et al. 2015). This data allows 16 biologists to observe many aspects of the animals' lives, including their behavior, physiology, social 17 interactions, and external environment. However, the need to limit the size of these devices to a 18 small fraction of the animal's size imposes strict limits on the devices' hardware and battery 19 capacities (Kays et al. 2015). Here we show how AI can be leveraged on board these devices to 20 intelligently control their activation of costly sensors, e.g., video cameras, allowing them to make 21 the most of their limited resources during long deployment periods. Our method goes beyond 22 Sensor data logger 104We begin with a brief introduction to the sensor data loggers used in this study (for more details see 105 Online Methods). Fig. 1 (c) shows a close-up view of the logger, with the camera module located on 106 the far-left end of the logger. Fig. 1 (d) shows an example of the data collected from a chest-107 mounted logger, with the map displaying the GPS data collected and the two inset images showing 108 frames from foraging activity captured by the device. Fig. 1 (e) shows an example of how these 109 devices were attached in the field. In this example, the logger is attached on the back of the animal, 110 with the camera facing forward and the GPS receiver (white square to the rear of the device) facing 111 the sky. Additionally, in some cases the devices were instead attached to the chest of the birds, in 112 order to improve the camera's field of view during foraging activities. 114Note that because our logger is equipped with a commercially-available MCU and sensors using a 115 simple circuit design (see Online Methods), we believe that reproduction of the logger system using 116 rapid prototyping platforms, such as Arduino, is relatively easy. 118Activity recognition method 119 Overview 120 Our method is based on supervised machine learning, which can be divided into two main phases: 121 training and testing. This approach assumes that sensor data that corresponds to the data collected 122 by our low-energy sensors can be collected in advance during the training phase. During the 123 training phase, the preexisting sensor data is labelled by biologists to indicate the target activities 124 that should be captured by the loggers' cameras. This labeled sensor data is then used to train the 125

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Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 96%

AI on animals: AI-assisted animal-borne logger never misses the moments that biologists want

Korpela

Suzuki

Matsumoto

et al. 2019

Preprint

View full text Add to dashboard Cite

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“…A data-logger is an electronic device coupled with at least one probe, also called a sensor, that automatically measures physical quantities at regular time intervals [23,42]. It is a discrete, easy-to-use, miniaturized device that produces reliable high-frequency data [43,44]. The small size of the probes allows their insertion into biomimetic models that mimic the organism physical properties [45][46][47].…”

Section: Spatio-temporal Studies Using Biomimetic Logger An Original ...mentioning

confidence: 99%

Multi-Site and Multi-Year Remote Records of Operative Temperatures with Biomimetic Loggers Reveal Spatio-Temporal Variability in Mountain Lizard Activity and Persistence Proxy Estimates

2020

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Commonly, when studies deal with the effects of climate change on biodiversity, mean value is used more than other parameters. However, climate change also leads to greater temperature variability, and many papers have demonstrated its importance in the implementation of biodiversity response strategies. We studied the spatio-temporal variability of activity time and persistence index, calculated from operative temperatures measured at three sites over three years, for a mountain endemic species. Temperatures were recorded with biomimetic loggers, an original remote sensing technology, which has the same advantages as these tools but is suitable for recording biological organisms data. Among the 42 tests conducted, 71% were significant for spatial variability and 28% for temporal variability. The differences in daily activity times and in persistence indices demonstrated the effects of the micro-habitat, habitat, slope, altitude, hydrography, and year. These observations have highlighted the great variability existence in the environmental temperatures experienced by lizard populations. Thus, our study underlines the importance to implement multi-year and multi-site studies to quantify the variability and produce more representative results. These studies can be facilitated by the use of biomimetic loggers, for which a user guide is provided in the last part of this paper.

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Machine learning enables improved runtime and precision for bio-loggers on seabirds

et al. 2020

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Unravelling the secrets of wild animals is one of the biggest challenges in ecology, with bio-logging (i.e., the use of animal-borne loggers or bio-loggers) playing a pivotal role in tackling this challenge. Bio-logging allows us to observe many aspects of animals’ lives, including their behaviours, physiology, social interactions, and external environment. However, bio-loggers have short runtimes when collecting data from resource-intensive (high-cost) sensors. This study proposes using AI on board video-loggers in order to use low-cost sensors (e.g., accelerometers) to automatically detect and record complex target behaviours that are of interest, reserving their devices’ limited resources for just those moments. We demonstrate our method on bio-loggers attached to seabirds including gulls and shearwaters, where it captured target videos with 15 times the precision of a baseline periodic-sampling method. Our work will provide motivation for more widespread adoption of AI in bio-loggers, helping us to shed light onto until now hidden aspects of animals’ lives.

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A miniaturized threshold-triggered acceleration data-logger for recording burst movements of aquatic animals

Cited by 7 publications

References 31 publications

AI on animals: AI-assisted animal-borne logger never misses the moments that biologists want

AI on animals: AI-assisted animal-borne logger never misses the moments that biologists want

Multi-Site and Multi-Year Remote Records of Operative Temperatures with Biomimetic Loggers Reveal Spatio-Temporal Variability in Mountain Lizard Activity and Persistence Proxy Estimates

Machine learning enables improved runtime and precision for bio-loggers on seabirds

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