Advanced Smartphone-Based Sensing with Open-Source Task Automation

Abstract

Smartphone-based sensing is becoming a convenient way to collect data in science, especially in environmental research. Recent studies that use smartphone sensing methods focus predominantly on single sensors that provide quantitative measurements. However, interdisciplinary projects call for study designs that connect both, quantitative and qualitative data gathered by smartphone sensors. Therefore, we present a novel open-source task automation solution and its evaluation in a personal exposure study with cyclists. We designed an automation script that advances the sensing process with regard to data collection, management and storage of acoustic noise, geolocation, light level, timestamp, and qualitative user perception. The benefits of this approach are highlighted based on data visualization and user handling evaluation. Even though the automation script is limited by the technical features of the smartphone and the quality of the sensor data, we conclude that task automation is a reliable and smart solution to integrate passive and active smartphone sensing methods that involve data processing and transfer. Such an application is a smart tool gathering data in population studies.

Cyclists’ Multiple Environmental Urban Exposures—Comparing Subjective and Objective Measurements

“…The mobile device selection was based on an earlier sensor comparison where wearables for multifactorial exposure sensing were evaluated in terms of data accuracy and ease of use (see reference [35] for all technical specifications including calibration and sensitivity analysis). Furthermore, we applied a self-designed smartphone application to measure ambient noise, geolocation, light level, and subjective opinions [34]. For ambient heat and humidity, an external sensor connected to the smartphone was used.…”

“…The basic equipment for each cyclist consisted of two parts ( Figure 1): (i) a particle counter (Dylos DC 1700, Dylos Cooperation, Riverside, CA, USA) with an attached ventilated temperature sensor (Hobo MX 1101, Onset Computer Corporation, Bourne, MA, USA) and (ii) a smartphone (Motorola G3, Motorola Mobility LLC, Chicago, IL, USA) with an attached external microphone (ETM-001, Edutige Europe, Munich, Germany). Our designed open-source smartphone sensing application was preinstalled [34]. The recording interval for noise and heat was 2 s and 60 s (fixed interval) for air pollution.…”

“…In connection with low-cost sensing technologies for environmental monitoring, it is possible to investigate micro-level exposures at short time-intervals [33]. The most important advantage of SSMs is the option for coincident collection of objective and subjective data in place and time using geolocation and display prompts [34]. The study at hand applied a unique collection of wearables to investigate the dynamic personal exposure to multifactorial stressors with three main aims: (i) to compare multiple exposure profiles of urban cyclists, (ii) to contrast the objective and subjective individual perceptions of exposures, and (iii) to examine the roles of decision-making about routes and health impact awareness for healthier route choices.…”

Features and Practicability of the Next-Generation Sensors and Monitors for Exposure Assessment to Airborne Pollutants: A Systematic Review

“…It allows easy and stable communication between NGSMs and a smartphone in which the mobile app is supported. In this review, 23 articles [ 19 , 27 , 32 , 35 , 37 , 38 , 39 , 50 , 51 , 56 , 60 , 67 , 69 , 77 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 ] out of 67 reports information about the use of any mobile app supporting NGMSs; most of those (13 apps) were developed on the Android platform [ 6 , 35 , 37 , 38 , 40 , 50 , 53 , 60 , 81 , 84 , 86 , 87 , 88 ], only one was developed on the iOS platform [ 81 ], and the remaining were not specified. As reported by Kanjo et al [ 89 ], using a mobile phone to collect data can bring many advantages, especially related to the fact that (i) a large percentage of the population carries around mobile phones; (ii) many kinds of data can be processed, stored, and transferred easily by mobile phones; (iii) the collection of data should be more power-efficient because the acquired information are sent directly to the mobile phone.…”

Instrumentation to Measure On-Road Cyclist Noise Exposure: Considerations for Study Design with Smartphones and Sound Level Meters

“…Although most cycling studies employ professional SLM instruments, some have used smartphones for noise monitoring, with either the internal microphone or an attached external microphone ( 9 , 30 , 31 ). A recent U.S. study on noise-safety relationships ( 9 ) used smartphones for noise data collection, and comparison with a professional SLM for a small validation data set (just 11 observations) showed that smartphone readings were significantly higher (by 7 dB-A), but well correlated (Kendall rank correlation of 0.88).…”