Smartphones as a Light Measurement Tool: Case of Study

Nocturnal Smartphone Use Affects Sleep Quality and Cognitive and Physical Performance in Tunisian School-Age Children

Nocturnal smartphone use emits blue light, which can adversely affect sleep, leading to a variety of negative effects, particularly in children. Therefore, the present study aimed to determine the effect of acute (AC) (one night) and repeated (RC) (five nights) nocturnal smartphone exposure on sleep, cortisol, and next-day performance in Tunisian children. Thirteen participants (seven girls and six boys, age 9 ± 0.6, height 1.32 ± 0.06, weight 34.47 ± 4.41) attended six experimental nights. The experiment started with a baseline night (BL) with no smartphone exposure, followed by repeated sessions of nocturnal smartphone exposure lasting 90 minutes (08:00 pm-09:30 pm). Actigraphy; salivary cortisol; the Stroop test (selective attention); choice reaction time (CRT); N-back (working memory); counter-movement jump (CMJ), composed of flight time (time spent in the CMJ flight phase) and jump height; and a 30 m sprint were assessed the morning after each condition. Both AC and RC shortened total sleep time (TST) (p < 0.01), with a greater decrease with RC (-46.7 min, ∆% = -9.46) than AC (-28.8 min, ∆% = -5.8) compared to BL. AC and RC significantly increased waking after sleep onset (3.5 min, ∆% = 15.05, to 9.9 min, ∆% = 43.11%) and number of errors made on the Stroop test (1.8 error, ∆% = 74.23, to 3.07 error, ∆% = 97.56%). Children made 0.15 and 0.8 more errors (∆% = 6.2 to 57.61%) and spent 46.9 s and 71.6 s more time on CRT tasks (∆% = 7.22 to 11.11%) with AC and RC, respectively, compared to BL. The high-interference index of the Stroop task, CMJ performance, and 30 m sprint speed were only altered (p < 0.01) following RC (0.36, Δ% = 41.52%; -34 s, Δ% = -9.29%, for flight time and -1.23 m, -8.72%, for jump height; 0.49 s, Δ% = 6.48, respectively) when compared to BL. In conclusion, one- or five-night exposure to smartphones disturbed the children's sleep quality and their performance, with more pronounced effects following RC.

Brighter Time: A Smartphone App Recording Cognitive Task Performance and Illuminance in Everyday Life

Light is an influential regulator of behavioural and physiological state in mammals. Features of cognitive performance such as memory, vigilance and alertness can be altered by bright light exposure under laboratory and field conditions. However, the importance of light as a regulator of performance in everyday life is hard to assess and has so far remained largely unclear. We set out to address this uncertainty by developing a tool to capture measures of cognitive performance and light exposure, at scale, and during everyday life. To this end, we generated an app (Brighter Time) which incorporated a psychomotor vigilance (PVT), an N-back and a visual search task with questionnaire-based assessments of demographic characteristics, general health, chronotype and sleep. The app also measured illuminance during task completion using the smartphone's intrinsic light meter. We undertook a pilot feasibility study of Brighter Time based on 91-week-long acquisition phases within a convenience sample (recruited by local advertisements and word of mouth) running Brighter Time on their own smartphones over two study phases in winter and summer. Study compliance was suitable (median = 20/21 requested task completions per subject). Statistically significant associations were observed between subjective sleepiness and performance in all tasks. Significant daily variations in PVT and visual search performance were also observed. Higher illuminance was associated with reduced reaction time and lower inverse efficiency score in the visual search. Brighter Time thus represents a viable option for large-scale collection of cognitive task data in everyday life, and is able to reveal associations between task performance and sleepiness, time of day and current illuminance. Brighter Time's utility could be extended to exploring associations with longer-term patterns of light exposure and/or other light metrics by integrating with wearable light meters.

Artificial Light at Night Drives Earlier Singing in a Neotropical Bird

Simple Summary

Urban birds have to cope with dominant stressors as anthropogenic noise and artificial light at night by adjusting their song traits. However, evidence of such adjustments has been studied thus far in temperate cities, rather than tropical cities. Here, I tested whether noise and light pollution influence earlier singing behavior in a tropical bird, the Saffron Finch. Birds in highly urbanized sites sang earlier at dawn and this timing difference was driven by light pollution instead of anthropogenic noise. Overall, these results suggest that light pollution could have a detrimental impact on the circadian rhythms of urban tropical birds such as daily singing routines.

Abstract

Anthropogenic noise and artificial light at night (ALAN) can disrupt the morning singing routines of urban birds, however, its influence on tropical species remains poorly explored. Here, I assessed the association between light and noise pollution with the dawn chorus onset of the Saffron Finch (Sicalis flaveola) in a city in Colombia. I studied 32 sites comprised of different conditions of urban development based on built cover. I recorded the time of the first song of the Saffron Finch, the conspecific density and measured anthropogenic noise and ALAN using smartphone apps. The findings of this study show that Saffron Finches living in highly developed sites sang earlier at dawn than those occupying less urbanized sites. Unexpectedly, this timing difference was related to ALAN instead of anthropogenic noise, suggesting that light pollution could drive earlier dawn chorus in a tropical urban bird. Saffron Finches could take advantage of earlier singing for signaling territorial ownership among neighbors. Future studies need to assess the influence of ALAN on the dawn chorus timing of Neotropical urban birds.

Measurement of the Speed of Induction Motors Based on Vibration with a Smartphone

Induction motors are key pieces of equipment in today’s society, powering a variety of industrial drives and home appliances. The induction motor speed is often used to monitor the performance of all kinds of industrial drives. For example, in the industrial field, the motor speed is very often used to determine the efficiency and mechanical load of motors. In this work, a new simple, low-cost, and nonintrusive procedure is proposed for infield measurement of induction motors speed, which is based on the spectral analysis of the vibration signal of the motors. The motor vibration signal is first acquired using the accelerometers integrated into a basic phone. The acquired signal is then treated by a MATLAB-based algorithm, which can determine the motor speed by identifying the mechanical frequency of the rotor shaft from the harmonic content of the vibration signal. In this way, it is shown that the mechanical frequency corresponding to the speed of rotation of the motors can be acquired by means of the embedded accelerometers of a common smartphone, avoiding the acquisition and installation of external accelerometers. To the authors’ knowledge, this could be the first time that a smartphone has been proposed as a practical means of measuring the speed of a motor by analysing its vibration. Experimental results from an extensive set of tests, including the supply of the motor from a frequency converter, show that the speed can always be measured with a relative error of less than 0.15%.

Illuminance measurement by mobile devices with a diffuser dome: a comparative analysis of different platforms and applications

Studies in the literature suggest that smartphone-based applications are not suitable for measuring illuminance levels. The measurement of this photometric quantity is useful in several sectors, including monitoring the adequacy of lighting conditions in workplaces. This work investigates the contribution of attaching a diffuser dome to the embedded light sensors of smartphones and tablets associated with different applications on their performance for measuring illuminance. Two experimental arrangements were developed using an LED lamp (294.84 lx, 475.58 lx, 880.39 lx) and a xenon arc lamp (607.94 lx, 1013.72 lx, 4012.26 lx, 23933.33 lx). In the comparative analysis, the impact of using diffusion dome attachment was evidenced, indicating its potential to enable some of the device/application combinations to achieve adequate performance for being applied in the daily assessment of illuminance.

Using mobile phones as light at night and noise measurement instruments: a validation test in real world conditions

Exposure to noise from road traffic and industries is known to be linked to various health dysfunctions, including hypertension, cardiovascular diseases and hearing loss. Exposure to artificial light at night (ALAN) is also increasingly recognized as being associated with ecosystem damage and various illnesses, including cancers, excessive weight gain and sleep disorders. However, measuring and monitoring these environmental risk factors by professional equipment are laborious and expensive, which impede large-scale research and various citizen science initiatives. In this study, we test a possibility that reliable noise and ALAN exposure estimates can be gathered using smartphones (SPs) sensors. To verify this assumption, we develop a standardized testing protocol, and use Andro-Sensor app, installed on three different Samsung Galaxy SPs - S7, S20FE5G, and SM520F, - to perform measurements of ALAN and noise in real-world conditions while comparing these measurements with measurements performed by professional (type 2) equipment - SL814 for noise and LX-1330B for illumination. The analysis of 3450 measurements, performed in two different locations in Israel, reveals that the SPs measurements and measurements performed by control instruments correlate strongly for noise (r = 0.76-0.94) and are nearly identical for ALAN (r = 0.998-0.999). The association between the two types of measurements is also found to be close to linear, with the slope of the trend line being close to 45° for ALAN and varying between 30° and 45° for noise, depending on the SPs used. Our conclusion is that the level of accuracy of ALAN measurements by SPs is greater for ALAN than for noise, which can make SPs a useful tool for large-scale ALAN studies that do not require the accuracy of professional instruments.

Smartphone Sensor Based Indoor Positioning: Current Status, Opportunities, and Future Challenges

The last two decades have witnessed a rich variety of indoor positioning and localization research. Starting with Microsoft Research pioneering the fingerprint approach based RADAR, MIT’s Cricket, and then moving towards beacon-based localization are few among many others. In parallel, researchers looked into other appealing and promising technologies like radio frequency identification, ultra-wideband, infrared, and visible light-based systems. However, the proliferation of smartphones over the past few years revolutionized and reshaped indoor localization towards new horizons. The deployment of MEMS sensors in modern smartphones have initiated new opportunities and challenges for the industry and academia alike. Additionally, the demands and potential of location-based services compelled the researchers to look into more robust, accurate, smartphone deployable, and context-aware location sensing. This study presents a comprehensive review of the approaches that make use of data from one or more sensors to estimate the user’s indoor location. By analyzing the approaches leveraged on smartphone sensors, it discusses the associated challenges of such approaches and points out the areas that need considerable research to overcome their limitations.

Artificial Light at Night (ALAN) Is the Main Driver of Nocturnal Feral Pigeon (Columba livia f. domestica) Foraging in Urban Areas

Simple Summary

Artificial light at night is one of the most extreme alterations in urban areas, which drives nocturnal activity in diurnal species. However, the role of artificial light in the nocturnal activity of the Feral Pigeon (Columba livia f. domestica) is unknown. The objective of this study is to assess the environmental factors associated with the nocturnal activity of the Feral Pigeon in Argentinian cities. Nocturnal foraging by the Feral Pigeon was recorded in three of four surveyed cities. Artificial light at night was positively related to nocturnal foraging activity in Salta and Buenos Aires. The results obtained suggest that urbanization would promote nocturnal activity in Feral Pigeons. Moreover, nocturnal activity was mainly driven by artificial light, which probably alters the circadian rhythm of pigeons.

Abstract

Artificial light at night (ALAN) is one of the most extreme environmental alterations in urban areas, which drives nocturnal activity in diurnal species. Feral Pigeon (Columba livia f. domestica), a common species in urban centers worldwide, has been observed foraging at night in urban areas. However, the role of ALAN in the nocturnal activity of this species is unknown. Moreover, studies addressing the relationship between ALAN and nocturnal activity of diurnal birds are scarce in the Southern Hemisphere. The objective of this study is to assess the environmental factors associated with nocturnal activity of the Feral Pigeon in Argentinian cities. Environmental conditions were compared between sites where pigeons were seen foraging and randomly selected sites where pigeons were not recorded foraging. Nocturnal foraging by the Feral Pigeon was recorded in three of four surveyed cities. ALAN was positively related to nocturnal foraging activity in Salta and Buenos Aires. The results obtained suggest that urbanization would promote nocturnal activity in Feral Pigeons. Moreover, nocturnal activity was mainly driven by ALAN, which probably alters the circadian rhythm of pigeons.

Defining and assessing walkability: a concept for an integrated approach using surveys, biosensors and geospatial analysis

Walking as a transport mode is still often underrepresented in the overall transport system. Consequently, pedestrian mobility is usually not recorded statistically in the same manner as it is performed for motorised traffic which leads to an underestimation of its importance and positive effects on people and cities. However, the integration of walkability assessments is potentially a valuable complement in urban planning processes through considering important quantitative and qualitative aspects of walking in cities. Recent literature shows a variety of approaches involving discrepancies in the definition of walkability, the factors which contribute to it, and methods of assessing them. This paper provides a new understanding of the concept of walkability in the European context. Our approach relies on the extension of methodological competence in transportation, spatial planning and geography by linking new measurement methods for evaluating walkability. We propose an integrated approach to assessing walkability in a comprehensive methodology that combines existing qualitative and GIS-based methods with biosensor technologies and thus captures the perceptions and emotions of pedestrians. This results in an increased plausibility and relevance of the results of walkability analysis by considering the spatial environment and its effect on people.

The Effects of a Short Forest Recreation Program on Physiological and Psychological Relaxation in Young Polish Adults

Forest recreation is an activity that could be successfully used to alleviate negative symptoms of stress in individuals. Multiple positive psychological and physiological effects have been described in the literature, especially regarding works describing research from Asian countries such as Japan, Korea, and Taiwan. In East-Central Europe, however, the effectuality of forest recreation has not been addressed in scientific research. Thus, a special recreation program was developed, and its usability was examined with the involvement of 21 young Polish adults. A pre- and post-test design was used, wherein four psychological questionnaires were applied (Profile of Mood States, Positive and Negative Affect Schedule, Restorative Outcomes Scale, Subjective Vitality Scale), and physiological measures were assessed (pulse rate, blood pressure) before and after the program. A field study was also conducted at the nature reserve Redykajny, near the suburban forest of the city of Olsztyn. The recreational program had a significant impact on psychological and physiological parameters. After recreation, the negative mood markers of the negative affect decreased and the positive affect, including restoration and vitality, increased. Furthermore, pulse rates, systolic blood pressures, and mean arterial pressures of the participants were significantly lower after the program. These results reveal that the short forest recreation program may be effective in reducing negative symptoms of stress.

Comparing RGB-D Sensors for Close Range Outdoor Agricultural Phenotyping

Phenotyping is the task of measuring plant attributes for analyzing the current state of the plant. In agriculture, phenotyping can be used to make decisions concerning the management of crops, such as the watering policy, or whether to spray for a certain pest. Currently, large scale phenotyping in fields is typically done using manual labor, which is a costly, low throughput process. Researchers often advocate the use of automated systems for phenotyping, relying on the use of sensors for making measurements. The recent rise of low cost, yet reasonably accurate, RGB-D sensors has opened the way for using these sensors in field phenotyping applications. In this paper, we investigate the applicability of four different RGB-D sensors for this task. We conduct an outdoor experiment, measuring plant attribute in various distances and light conditions. Our results show that modern RGB-D sensors, in particular, the Intel D435 sensor, provides a viable tool for close range phenotyping tasks in fields.

MagIO: Magnetic Field Strength Based Indoor- Outdoor Detection with a Commercial Smartphone

A wide range of localization techniques has been proposed recently that leverage smartphone sensors. Context awareness serves as the backbone of these localization techniques, which helps them to shift the localization technologies to improve efficiency and energy utilization. Indoor-outdoor (IO) context sensing plays a vital role for such systems, which serve both indoor and outdoor localization. IO systems work with collaborative technologies including the Global Positioning System (GPS), cellular tower signals, Wi-Fi, Bluetooth and a variety of smartphone sensors. GPS- and Wi-Fi-based systems are power hungry, and their accuracy is severed by limiting factors like multipath, shadowing, etc. On the other hand, various built-in smartphone sensors can be deployed for environmental sensing. Although these sensors can play a crucial role, yet they are very less studied. This research aims at investigating the use of ambient magnetic field data alone from a smartphone for IO detection. The research first investigates the feasibility of utilizing magnetic field data alone for IO detection and then extracts different features suitable for IO detection to be used in machine learning-based classifiers to discriminate between indoor and outdoor environments. The experiments are performed at three different places including a subway station, a shopping mall and Yeungnam University (YU), Korea. The training data are collected from one spot of the campus, and testing is performed with data from various locations of the above-mentioned places. The experiment involves Samsung Galaxy S8, LG G6 and Samsung Galaxy Round smartphones. The results show that the magnetic data from smartphone magnetic sensor embody enough information and can discriminate the indoor environment from the outdoor environment. Naive Bayes (NB) outperforms with a classification accuracy of 83.26%, as against Support vector machines (SVM), random induction (RI), gradient boosting machines (GBM), random forest (RF), k-nearest neighbor (kNN) and decision trees (DT), whose accuracies are 67.21%, 73.38%, 73.40%, 78.59%, 69.53% and 68.60%, respectively. kNN, SVM and DT do not perform well when noisy data are used for classification. Additionally, other dynamic scenarios affect the attitude of magnetic data and degrade the performance of SVM, RI and GBM. NB and RF prove to be more noise tolerant and environment adaptable and perform very well in dynamic scenarios. Keeping in view the performance of these classifiers, an ensemble-based stacking scheme is presented, which utilizes DT and RI as the base learners and naive Bayes as the ensemble classifier. This approach is able to achieve an accuracy of 85.30% using the magnetic data of the smartphone magnetic sensor. Moreover, with an increase in training data, the accuracy of the stacking scheme can be elevated by 0.83%. The performance of the proposed approach is compared with GPS-, Wi-Fi- and light sensor-based IO detection.