Sampling rate impact on energy consumption of biomedical signal processing systems

Tobola, Andreas; Streit, Franz-Josef; Espig, Chris; Korpok, Oliver; Sauter, Christian; Lang, Nadine; Schmitz, Bjorn; Hofmann, Christian; Struck, Matthias; Weigand, Christian; Leutheuser, Heike; Eskofier, Bjoern M.; Fischer, Georg

doi:10.1109/bsn.2015.7299392

Cited by 26 publications

(12 citation statements)

References 21 publications

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“…None of the previous studies evaluated different positions of sensors in terms of accuracy of classification for eating behaviour in sheep and used sampling rate of 20–25 Hz [ 2 ]. Position and sampling rates could impact the accuracy of the algorithms, with higher sampling rate resulting in higher power consumption [ 22 ]. In our previous work we demonstrated that when using an accelerometer and gyroscope sensor the optimum sampling rate needed to classify lying, standing and walking behaviour in sheep with respect to accuracy and energy efficiency is 16 Hz with a 7-s sample window [ 16 ].…”

Section: Introductionmentioning

confidence: 99%

Feature Selection and Comparison of Machine Learning Algorithms in Classification of Grazing and Rumination Behaviour in Sheep

Mansbridge

Mitsch

Bollard

et al. 2018

Sensors

112

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Grazing and ruminating are the most important behaviours for ruminants, as they spend most of their daily time budget performing these. Continuous surveillance of eating behaviour is an important means for monitoring ruminant health, productivity and welfare. However, surveillance performed by human operators is prone to human variance, time-consuming and costly, especially on animals kept at pasture or free-ranging. The use of sensors to automatically acquire data, and software to classify and identify behaviours, offers significant potential in addressing such issues. In this work, data collected from sheep by means of an accelerometer/gyroscope sensor attached to the ear and collar, sampled at 16 Hz, were used to develop classifiers for grazing and ruminating behaviour using various machine learning algorithms: random forest (RF), support vector machine (SVM), k nearest neighbour (kNN) and adaptive boosting (Adaboost). Multiple features extracted from the signals were ranked on their importance for classification. Several performance indicators were considered when comparing classifiers as a function of algorithm used, sensor localisation and number of used features. Random forest yielded the highest overall accuracies: 92% for collar and 91% for ear. Gyroscope-based features were shown to have the greatest relative importance for eating behaviours. The optimum number of feature characteristics to be incorporated into the model was 39, from both ear and collar data. The findings suggest that one can successfully classify eating behaviours in sheep with very high accuracy; this could be used to develop a device for automatic monitoring of feed intake in the sheep sector to monitor health and welfare.

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Section: Introductionmentioning

confidence: 99%

Feature Selection and Comparison of Machine Learning Algorithms in Classification of Grazing and Rumination Behaviour in Sheep

Mansbridge

Mitsch

Bollard

et al. 2018

Sensors

112

View full text Add to dashboard Cite

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“…Several detailed studies are presented by Tobola et al in which they compare different microcontrollers for WSNs [4], quantify the impact of different sampling rates for various algorithms [5], and optimize the hard-and software implementation of an ECG sensor [6]. Likewise, Berlin et al [7] increased the runtime of a wearable data logger by varying the sampling rates, the low-power modes, the type of used SD-card and by applying a run length encoding.…”

Section: Related Workmentioning

confidence: 99%

Towards a comprehensive power consumption model for wireless sensor nodes

Hesse

Adams

Hörmann

et al. 2016

2016 IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN)

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Abstract-Energy efficiency is the most outstanding design criterion for wireless sensor nodes and especially wireless body sensors. Because a detailed measurement of the system's power consumption is not possible during the design process and often too complex for already manufactured devices, the power consumption has to be estimated. This leads to the need for a comprehensive and modular model for the power consumption of WSNs, which is proposed in this work. Due to the modular structure of the model the user is able to get a first estimate in an early stage of the design process (e.g. choose components) and to get a more accurate estimation later in the design process by lowering the abstraction level. This tackles the demanding trade-off between accuracy and usability in modeling.

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“…If we consider this new scenario, the portable devices strongly constraint the maximum power consumption of the system. In order to enlarge the battery life-time, one possible approach is the reduction of the sampling rate of the sensing node [2], leading to a reduction of data density and signal bandwidth. In fact, the key components of the power consumption in the device are related to the power spent by signal elaboration and data transmission, which are both directly influenced by the sampling rate.…”

Section: Introductionmentioning

confidence: 99%

Optimized Sampling Rate for Voltammetry-Based Electrochemical Sensing in Wearable and IoT Applications

2019

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The recent advancements in electrochemical measurements are guiding the development of new platforms for in-situ point-of-care monitoring of human-metabolite, markers and drugs. Despite this, the application of Voltammetry-Based Sensing (VBS) techniques is still limited in wearable, portable, or IoT systems. In order to use VBS approaches to measure analytes in small and low-power electronic platforms for diagnostics, several improvements are required. For example, the definition of a method to achieve the right trade-off between sample rate and sensing performance is still missing. To develop a method to define the best sampling rate, we present here an extensive analysis of experimental data to prove that is feasible to detect drugs such as paracetamol by Staircase Cyclic Voltammetry (SCV) or Differential Pulse Voltammetry (DVP) direct detection methods, with low sampling frequency. Our results prove that the proposed method helps the development of systems capable of discriminating the minimum pharmacology concentration of the metabolite under analysis with a massive reduction of the sampling frequency.

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Sampling rate impact on energy consumption of biomedical signal processing systems

Cited by 26 publications

References 21 publications

Feature Selection and Comparison of Machine Learning Algorithms in Classification of Grazing and Rumination Behaviour in Sheep

Feature Selection and Comparison of Machine Learning Algorithms in Classification of Grazing and Rumination Behaviour in Sheep

Towards a comprehensive power consumption model for wireless sensor nodes

Optimized Sampling Rate for Voltammetry-Based Electrochemical Sensing in Wearable and IoT Applications

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