Environmental exposure assessment using indoor/outdoor detection on smartphones

Anagnostopoulos, Theodoros; García, Juan; Gonçalves, Jorge; Ferreira, Denzil; Hosio, Simo; Kostakos, Vassilis

doi:10.1007/s00779-017-1028-y

Cited by 19 publications

(8 citation statements)

References 44 publications

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“…However, there are practical methods to deal with this problem. One such method is based on collecting data using various sensors that are available on mobile devices [1,[9][10][11][12]. Another is based solely on collecting data using a GPS sensor [13,14].…”

Section: Related Workmentioning

confidence: 99%

“…Although the input data is available with mobile, this method requires complex calculations and a large amount of input data to work well. In another study, the author used even more sensors and data for I/O detection, including an activity recognition Application Programming Interface (API), barometric pressure, a light sensor, cloud coverage data, a timer, Global System for Mobile Communications (GSM) signal strength, an accelerometer, a magnetometer, and ambient noise [10]. These sets of input data increase I/O detection accuracy to more than 98%; however, calculation and data collection are complicated and consume significant power.…”

Section: Related Workmentioning

confidence: 99%

“…Harsh testing conditions also affect I/O detection accuracy. In [11], the authors used the same input dataset with previous study [10], including an accelerometer, cellular radio, light sensor, magnetometer, and proximity sensor data; however, the accuracy is only 80%. Power consumption is also greater compared to a previous study [9].…”

Section: Related Workmentioning

confidence: 99%

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GPS-Based Indoor/Outdoor Detection Scheme Using Machine Learning Techniques

Bui

et al. 2020

Applied Sciences

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Recent advances in mobile communication require that indoor/outdoor environment information be available for both individual applications and wireless signal transmission in order to improve interference control and serve upper-layer applications. In this paper, we present a scheme to identify the indoor/outdoor environment using GPS signals combined with machine learning classification techniques. Compared to traditional schemes, which are based on received signal strength indicator (RSSI), the proposed scheme promises a robust approach with high accuracy, smooth operation when moving between indoor and outdoor environments, as well as easy implementation and training. The proposed scheme combined information from a certain number of GPS satellites, using the GPS sensor on mobile devices. Then, data are collected, preprocessed, and classified as indoor or outdoor environment using a machine learning model that is optimized for the best performance. The GPS input data were collected in the Kookmin University area and included 850 training samples and 170 test samples. The overall accuracy reached 97%.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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GPS-Based Indoor/Outdoor Detection Scheme Using Machine Learning Techniques

Bui

et al. 2020

Applied Sciences

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“…As described in the research referenced below, indoor-outdoor detection using GPS suffers from high power usage and can be biased in outdoor, highly-dense urban areas, where a clear line of sight with the satellites is unavailable. Various methods use multiple sensors such as the accelerometer, proximity and light sensors, time, GSM receiver, and magnetometer [7,8]. Radu et al provide a review of the available techniques and critique them, generally arguing that GPS based methods are both inaccurate and high power consumers [9].…”

Section: Indoor-outdoor Detectionmentioning

confidence: 99%

Clustering Wi-Fi fingerprints for indoor–outdoor detection

2018

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This paper presents a method for continuous indoor-outdoor environment detection on mobile devices based solely on Wi-Fi fingerprints. Detection of indoor-outdoor switching is an important part of identifying a user's context, and it provides important information for upper layer context aware mobile applications such as recommender systems, navigation tools, etc. Moreover, future indoor positioning systems are likely to use Wi-Fi fingerprints, and therefore Wi-Fi receivers will be on most of the time. In contrast to existing research, we believe that these fingerprints should be leveraged, and they serve as the basis of the proposed method. Using various machine learning algorithms, we train a supervised classifier based on features extracted from the raw fingerprints, clusters, and cluster transition graph. The contribution of each of the features to the method is assessed. Our method assumes no prior knowledge of the environment, and a training set consisting of the data collected for just a few hours on a single device is sufficient in order to provide indoor-outdoor classification, even in an unknown location or when using new devices. We evaluate our method in an experiment involving 12 participants during their daily routine, with a total of 828 hours' worth of data collected by the participants. We report a predictive performance of the AUC (area under the curve) of 0.94 using the gradient boosting machine ensemble learning method. We show that our method can be used for other context detection tasks such as learning and recognizing a given building or room.

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“…It can automatically learn characteristics of new environments and devices and thereby provides a detection accuracy exceeding 90% even in unfamiliar circumstances. Anagnostopoulos [ 32 ] leveraged J48 and other machine learning algorithms to detect the IO state. They utilized multiple contextual features such as activity, barometric, ambient light, GSM, magnetometer variance, etc.…”

Section: Introductionmentioning

confidence: 99%

A Fast Indoor/Outdoor Transition Detection Algorithm Based on Machine Learning

Zhu

Luo

Wang

et al. 2019

Sensors

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The widespread popularity of smartphones makes it possible to provide Location-Based Services (LBS) in a variety of complex scenarios. The location and contextual status, especially the Indoor/Outdoor switching, provides a direct indicator for seamless indoor and outdoor positioning and navigation. It is challenging to quickly detect indoor and outdoor transitions with high confidence due to a variety of signal variations in complex scenarios and the similarity of indoor and outdoor signal sources in the IO transition regions. In this paper, we consider the challenge of switching quickly in IO transition regions with high detection accuracy in complex scenarios. Towards this end, we analyze and extract spatial geometry distribution, time sequence and statistical features under different sliding windows from GNSS measurements in Android smartphones and present a novel IO detection method employing an ensemble model based on stacking and filtering the detection result by Hidden Markov Model. We evaluated our algorithm on four datasets. The results showed that our proposed algorithm was capable of identifying IO state with 99.11% accuracy in indoor and outdoor environment where we have collected data and 97.02% accuracy in new indoor and outdoor scenarios. Furthermore, in the scenario of indoor and outdoor transition where we have collected data, the recognition accuracy reaches 94.53% and the probability of switching delay within 3 s exceeds 80%. In the new scenario, the recognition accuracy reaches 92.80% and the probability of switching delay within 4 s exceeds 80%.

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Environmental exposure assessment using indoor/outdoor detection on smartphones

Cited by 19 publications

References 44 publications

GPS-Based Indoor/Outdoor Detection Scheme Using Machine Learning Techniques

GPS-Based Indoor/Outdoor Detection Scheme Using Machine Learning Techniques

Clustering Wi-Fi fingerprints for indoor–outdoor detection

A Fast Indoor/Outdoor Transition Detection Algorithm Based on Machine Learning

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