Crowdsourcing Methods for Data Collection in Geophysics: State of the Art, Issues, and Future Directions

Data are essential in all areas of geophysics. They are used to better understand and manage systems, either directly or via models. Given the complexity and spatiotemporal variability of geophysical systems (e.g., precipitation), a lack of sufficient data is a perennial problem, which is exacerbated by various drivers, such as climate change and urbanization. In recent years, crowdsourcing has become increasingly prominent as a means of supplementing data obtained from more traditional sources, particularly due to its relatively low implementation cost and ability to increase the spatial and/or temporal resolution of data significantly. Given the proliferation of different crowdsourcing methods in geophysics and the promise they have shown, it is timely to assess the state‐of‐the‐art in this field, to identify potential issues and map out a way forward. In this paper, crowdsourcing‐based data acquisition methods that have been used in seven domains of geophysics, including weather, precipitation, air pollution, geography, ecology, surface water and natural hazard management are discussed based on a review of 162 papers. In addition, a novel framework for categorizing these methods is introduced and applied to the methods used in the seven domains of geophysics considered in this review. This paper also features a review of 93 papers dealing with issues that are common to data acquisition methods in different domains of geophysics, including the management of crowdsourcing projects, data quality, data processing and data privacy. In each of these areas, the current status is discussed and challenges and future directions are outlined

System-Level Power Consumption Analysis of the Wearable Asthmatic Wheeze Quantification

Long-term quantification of asthmatic wheezing envisions an m-Health sensor system consisting of a smartphone and a body-worn wireless acoustic sensor. As both devices are power constrained, the main criterion guiding the system design comes down to minimization of power consumption, while retaining sufficient respiratory sound classification accuracy (i.e., wheeze detection). Crucial for assessment of the system-level power consumption is the understanding of trade-off between power cost of computationally intensive local processing and communication. Therefore, we analyze power requirements of signal acquisition, processing, and communication in three typical operating scenarios: (1) streaming of uncompressed respiratory signal to a smartphone for classification, (2) signal streaming utilizing compressive sensing (CS) for reduction of data rate, and (3) respiratory sound classification onboard the wearable sensor. Study shows that the third scenario featuring the lowest communication cost enables the lowest total sensor system power consumption ranging from 328 to 428 μW. In such scenario, 32-bit ARM Cortex M3/M4 cores typically embedded within Bluetooth 4 SoC modules feature the optimal trade-off between onboard classification performance and consumption. On the other hand, study confirms that CS enables the most power-efficient design of the wearable sensor (216 to 357 μW) in the compressed signal streaming, the second scenario. In such case, a single low-power ARM Cortex-A53 core is sufficient for simultaneous real-time CS reconstruction and classification on the smartphone, while keeping the total system power within budget for uncompressed streaming

A Programmable 3-Channel Acoustic Wake-Up Interface Enabling Always-On Detection of Underwater Events within 20 µA

We present an always-on acoustic wake-up sensor interface, designed for prolonging the autonomy of energy-hungry hardware for underwater acoustic surveillance. Proposed design enables the detection of a passing ship by simultaneous listening up to three arbitrarily defined frequency-bands within the 2.5 kHz range, and generates a wake-up signal upon finding a match with a digitally preset template describing signal’s discriminatory time-frequency features. In this paper, we propose the architecture of such fully programmable, multichannel, mixed-signal wake- up circuit. We show the implementation of a PCB prototype, characterize its sensitivity, analyze its current consumption, and verify its response on real-world hydrophone recordings. It is demonstrated that the design consumes only 6.4 µA per channel (in total <20 µA) with ultra-low- power COTS components, while listening

Low-Power Wearable Respiratory Sound Sensing

Building upon the findings from the field of automated recognition of respiratory sound patterns, we propose a wearable wireless sensor implementing on-board respiratory sound acquisition and classification, to enable continuous monitoring of symptoms, such as asthmatic wheezing. Low-power consumption of such a sensor is required in order to achieve long autonomy. Considering that the power consumption of its radio is kept minimal if transmitting only upon (rare) occurrences of wheezing, we focus on optimizing the power consumption of the digital signal processor (DSP). Based on a comprehensive review of asthmatic wheeze detection algorithms, we analyze the computational complexity of common features drawn from short-time Fourier transform (STFT) and decision tree classification. Four algorithms were implemented on a low-power TMS320C5505 DSP. Their classification accuracies were evaluated on a dataset of prerecorded respiratory sounds in two operating scenarios of different detection fidelities. The execution times of all algorithms were measured. The best classification accuracy of over 92%, while occupying only 2.6% of the DSP’s processing time, is obtained for the algorithm featuring the time-frequency tracking of shapes of crests originating from wheezing, with spectral features modeled using energy

SPW-1: a low-maintenance wearable activity tracker for residential monitoring and healthcare applications

In this paper, we present SPW-1; a low-profile versatile wearable activity tracker that employs two ultra-low-power accelerometers and relies on Bluetooth Low Energy (BLE) for wireless communication. Aiming for a low maintenance system, SPW-1 is able to offer a battery lifetime of multiple months. Measurements on its wireless performance in a real residential environment with thick brick walls, demonstrate that SPW-1 can fully cover a room and - in most cases - the adjacent room, as well. SPW-1 is a research platform that is aimed to be used both as a data collecting tool for health-oriented studies outside the laboratory, but also for research on wearable technologies and body-centric communications. As a result, SPW-1 incorporates versatile features, such as external sensor support, various powering options, and accelerometer configuration options that can support a wide range applications from kinematics to long-term activity recognition