Using current sensing technology, a wealth of data on driving sessions is potentially available through a combination of vehicle sensors and drivers' physiology sensors (heart rate, breathing rate, skin temperature, etc.). Our hypothesis is that it should be possible to exploit the combination of time series produced by such multiple sensors during a driving session, in order to (i) learn models of normal driving behaviour, and (ii) use such models to detect important and potentially dangerous deviations from the norm in real-time, and thus enable the generation of appropriate alerts. Crucially, we believe that such models and interventions should and can be personalised and tailor-made for each individual driver. As an initial step towards this goal, in this paper we present techniques for assessing the impact of cognitive distraction on drivers, based on simple time series analysis. We have tested our method on a rich dataset of driving sessions, carried out in a professional simulator, involving a panel of volunteer drivers. Each session included a different type of cognitive distraction, and resulted in multiple time series from a variety of on-board sensors as well as sensors worn by the driver. Crucially, each driver also recorded an initial session with no distractions. In our model, such initial session provides the baseline times series that make it possible to quantitatively assess driver performance under distraction conditions.
More simulator-based results for hand-held and hands-free mobile phone conversations are available from Haigney @cite_16 , using mean and standard deviation of heart rate, speed and variability of accelerator pedal travel before, during and after the call. Both groups of authors comment on compensatory behaviour of drivers while using a mobile phone. Yet another study in this space, by Rakauskas @cite_13 , also investigated the effects of mobile phone conversations on driving performance using a simulator as well as a similar set of measures, namely accelerator variability, speed variability, average speed, steering offset, mean lateral speed, reaction time, collisions and mental workload. Once again, the conclusion is that the effects of mobile phone conversations while driving are higher workloads and a decreased driving performance. Finally, the study by Drews @cite_7 is the closest to ours in terms of the situation and factors considered, i.e., the effects on driving performance of having passenger and mobile phones conversations, again assessed using a simulator. In this case, lane keeping, mean speed and mean distance were used for the assessment.
@cite_16 @cite_13 @cite_7
2066681138 2154998637 2146019564
Abstract This paper reports a simulator-based study of the effects of mobile phone use on driving performance. Changes in heart rate indicated that mobile phone use increases the cognitive demand experienced by drivers with, it is argued, consequent reduction in safety margins. However, experimental results also suggested that participants engaged in a process of risk compensation, with driving speed being slower at times of mobile phone conversation while the number of off-road excursions (OFFS) and collisions remained stable. There also was some evidence that the use of a hand-held mobile phone (when compared to a hands-free system) was associated with poorer driving performance. Implications for real world' driving are considered. Problem: The prevalence of automobile drivers talking on cell phones is growing, but the effect of that behavior on driving performance is unclear. Also unclear is the relationship between the difficulty level of a phone conversation and the resulting distraction. Method: This study used a driving simulator to determine the effect that easy and difficult cell phone conversations have on driving performance. Results: Cell phone use caused participants to have higher variation in accelerator pedal position, drive more slowly with more variation in speed, and report a higher level of workload regardless of conversation difficulty level. Conclusions: Drivers may cope with the additional stress of phone conversations by enduring higher workloads or setting reduced performance goals. Impact on Industry: Because an increasing number of people talk on the phone while driving, crashes caused by distracted drivers using cell phones will cause disruptions in business, as well as injury, disability, and permanent loss of personnel. D 2004 National Safety Council and Elsevier Ltd. All rights reserved. This study examines how conversing with passengers in a vehicle differs from conversing on a cell phone while driving. We compared how well drivers were able to deal with the demands of driving when conversing on a cell phone, conversing with a passenger, and when driving without any distraction. In the conversation conditions, participants were instructed to converse with a friend about past experiences in which their life was threatened. The results show that the number of driving errors was highest in the cell phone condition; in passenger conversations more references were made to traffic, and the production rate of the driver and the complexity of speech of both interlocutors dropped in response to an increase in the demand of the traffic. The results indicate that passenger conversations differ from cell phone conversations because the surrounding traffic not only becomes a topic of the conversation, helping driver and passenger to share situation awareness, but the driving condition also has a direct influence on the complexity of the conversation, thereby mitigating the potential negative effects of a conversation on driving. This papers presents a context-aware NLP approach to automatically detect noteworthy information in spontaneous mobile phone conversations. The proposed method uses a supervised modeling strategy which considers both features from the content of the conversation as well as contextual information from the call. We empirically analyze the predictive performance of features of different nature on a corpus of mobile phone conversations. The results of this study reveal that the context of the conversation plays a crucial role on boosting the predictive performance of the model. In this paper, we develop a smart phone-based driving behavior evaluation system, named Join Driving, which helps drivers notice how aggressive their driving behaviors are and be aware of the riding comfort level of passengers. The proposed evaluation system is made of two parts: driving events detection and evaluation part and riding comfort level evaluation part. In driving events detection and evaluation part, the proposed system, Join Driving, first presents a model to detect drivers' driving events, based on the data collected from the acceleration, orientation and GPS sensors in smart phones. Then, based on the detected drivers' driving events, Join Driving implements a novel scoring mechanism to quantitatively evaluate how aggressive these driving events are. In riding comfort level evaluation part, the proposed system gives the specific scores to rate passengers' riding comfort level based on ISO 2631. Finally, several practical experiments are conducted to evaluate the effectiveness of the proposed scoring system. Research in social science has shown that mobile phone conversations distract users, presenting a significant impact to pedestrian safety; for example, a mobile phone user deep in conversation while crossing a street is generally more at risk than other pedestrians not engaged in such behavior. We propose WalkSafe, an Android smartphone application that aids people that walk and talk, improving the safety of pedestrian mobile phone users. WalkSafe uses the back camera of the mobile phone to detect vehicles approaching the user, alerting the user of a potentially unsafe situation; more specifically WalkSafe i) uses machine learning algorithms implemented on the phone to detect the front views and back views of moving vehicles and ii) exploits phone APIs to save energy by running the vehicle detection algorithm only during active calls. We present our initial design, implementation and evaluation of the WalkSafe App that is capable of real-time detection of the front and back views of cars, indicating cars are approaching or moving away from the user, respectively. WalkSafe is implemented on Android phones and alerts the user of unsafe conditions using sound and vibration from the phone. WalkSafe is available on Android Market. Background: Several thousand mobile phone apps are available to download to mobile phones for health and fitness. Mobile phones may provide a unique means of administering health interventions to populations. Objective: The purpose of this systematic review was to systematically search and describe the literature on mobile apps used in health behavior interventions, describe the behavioral features and focus of health apps, and to evaluate the potential of apps to disseminate health behavior interventions. Methods: We conducted a review of the literature in September 2014 using key search terms in several relevant scientific journal databases. Only English articles pertaining to health interventions using mobile phone apps were included in the final sample. Results: The 24 studies identified for this review were primarily feasibility and pilot studies of mobile apps with small sample sizes. All studies were informed by behavioral theories or strategies, with self-monitoring as the most common construct. Acceptability of mobile phone apps was high among mobile phone users. Conclusions: The lack of large sample studies using mobile phone apps may signal a need for additional studies on the potential use of mobile apps to assist individuals in changing their health behaviors. Of these studies, there is early evidence that apps are well received by users. Based on available research, mobile apps may be considered a feasible and acceptable means of administering health interventions, but a greater number of studies and more rigorous research and evaluations are needed to determine efficacy and establish evidence for best practices. [JMIR Mhealth Uhealth 2015;3(1):e20] Background: Ecological momentary assessment (EMA) is a useful method to tap the dynamics of psychological and behavioral phenomena in real-world contexts. However, the response burden of (self-report) EMA limits its clinical utility. Objective: The aim was to explore mobile phone-based unobtrusive EMA, in which mobile phone usage logs are considered as proxy measures of clinically relevant user states and contexts. Methods: This was an uncontrolled explorative pilot study. Our study consisted of 6 weeks of EMA unobtrusive EMA data collection in a Dutch student population (N=33), followed by a regression modeling analysis. Participants self-monitored their mood on their mobile phone (EMA) with a one-dimensional mood measure (1 to 10) and a two-dimensional circumplex measure (arousal valence, –2 to 2). Meanwhile, with participants’ consent, a mobile phone app unobtrusively collected (meta) data from six smartphone sensor logs (unobtrusive EMA: calls short message service (SMS) text messages, screen time, application usage, accelerometer, and phone camera events). Through forward stepwise regression (FSR), we built personalized regression models from the unobtrusive EMA variables to predict day-to-day variation in EMA mood ratings. The predictive performance of these models (ie, cross-validated mean squared error and percentage of correct predictions) was compared to naive benchmark regression models (the mean model and a lag-2 history model). Results: A total of 27 participants (81 ) provided a mean 35.5 days (SD 3.8) of valid EMA unobtrusive EMA data. The FSR models accurately predicted 55 to 76 of EMA mood scores. However, the predictive performance of these models was significantly inferior to that of naive benchmark models. Conclusions: Mobile phone-based unobtrusive EMA is a technically feasible and potentially powerful EMA variant. The method is young and positive findings may not replicate. At present, we do not recommend the application of FSR-based mood prediction in real-world clinical settings. Further psychometric studies and more advanced data mining techniques are needed to unlock unobtrusive EMA’s true potential. [J Med Internet Res 2016;18(3):e72] The experiment investigates the effect of perceived control on risk taking in a dynamic, everyday task. Using established and validated video simulation techniques, the risk-taking preferences for 96 drivers were measured for a range of driving activities (speed choice, following distance, gap acceptance, and overtaking). The perceived control manipulation was as follows: Half of the participants were told to imagine they were driving the vehicle, and the other half were told to imagine they were passengers. Those who were told to imagine they were driving chose significantly faster speeds than did those who were told to imagine they were passengers. Differences for the other risk-taking measures were not significant. For speed choice, it could be argued that an illusion of control was in operation, such that those who were in control (i.e., drivers) were comfortable with a higher level of risk than those who were not in control (i.e., passengers). We evaluate a mobile application that assesses driving behavior based on in-vehicle acceleration measurements and gives corresponding feedback to drivers. In the insurance business, such applications have recently gained traction as a viable alternative to the monitoring of drivers via "black boxes" installed in vehicles, which lacks interaction opportunities and is perceived as privacy intrusive by policyholders. However, pose uncertainty and other noise-inducing factors make smartphones potentially less reliable as sensor platforms. We therefore compare critical driving events generated by a smartphone with reference measurements from a vehicle-fixed IMU in a controlled field study. The study was designed to capture driver variability under real-world conditions, while minimizing the influence of external factors. We find that the mobile measurements tend to overestimate critical driving events, possibly due to deviation from the calibrated initial device pose. While weather and daytime do not appear to influence event counts, road type is a significant factor that is not considered in most current state-of-the-art implementations. TiltText, a new technique for entering text into a mobile phone is described. The standard 12-button text entry keypad of a mobile phone forces ambiguity when the 26- letter Roman alphabet is mapped in the traditional manner onto keys 2-9. The TiltText technique uses the orientation of the phone to resolve this ambiguity, by tilting the phone in one of four directions to choose which character on a particular key to enter. We first discuss implementation strategies, and then present the results of a controlled experiment comparing TiltText to MultiTap, the most common text entry technique. The experiment included 10 participants who each entered a total of 640 phrases of text chosen from a standard corpus, over a period of about five hours. The results show that text entry speed including correction for errors using TiltText was 23 faster than MultiTap by the end of the experiment, despite a higher error rate for TiltText. TiltText is thus amongst the fastest known language-independent techniques for entering text into mobile phones. Novel aspects of human dynamics and social interactions are investigated by means of mobile phone data. Using extensive phone records resolved in both time and space, we study the mean collective behavior at large scales and focus on the occurrence of anomalous events. We discuss how these spatiotemporal anomalies can be described using standard percolation theory tools. We also investigate patterns of calling activity at the individual level and show that the interevent time of consecutive calls is heavy-tailed. This finding, which has implications for dynamics of spreading phenomena in social networks, agrees with results previously reported on other human activities. We study the performance and user experience of two popular mainstream mobile text entry methods: the Smart Touch Keyboard (STK) and the Smart Gesture Keyboard (SGK). Our first study is a lab-based ten-session text entry experiment. In our second study we use a new text entry evaluation methodology based on the experience sampling method (ESM). In the ESM study, participants installed an Android app on their own mobile phones that periodically sampled their text entry performance and user experience amid their everyday activities for four weeks. The studies show that text can be entered at an average speed of 28 to 39 WPM, depending on the method and the user's experience, with 1.0 to 3.6 character error rates remaining. Error rates of touchscreen input, particularly with SGK, are a major challenge; and reducing out-of-vocabulary errors is particularly important. Both SGK and STK have strengths, weaknesses, and different individual awareness and preferences. Two-thumb touch typing in a focused setting is particularly effective on STK, whereas one-handed SGK typing with the thumb is particularly effective in more mobile situations. When exposed to both, users tend to migrate from STK to SGK. We also conclude that studies in the lab and in the wild can both be informative to reveal different aspects of keyboard experience, but used in conjunction is more reliable in comprehensively assessing input technologies of current and future generations. Stress can have long term adverse effects on individuals' physical and mental well-being. Changes in the speech production process is one of many physiological changes that happen during stress. Microphones, embedded in mobile phones and carried ubiquitously by people, provide the opportunity to continuously and non-invasively monitor stress in real-life situations. We propose StressSense for unobtrusively recognizing stress from human voice using smartphones. We investigate methods for adapting a one-size-fits-all stress model to individual speakers and scenarios. We demonstrate that the StressSense classifier can robustly identify stress across multiple individuals in diverse acoustic environments: using model adaptation StressSense achieves 81 and 76 accuracy for indoor and outdoor environments, respectively. We show that StressSense can be implemented on commodity Android phones and run in real-time. To the best of our knowledge, StressSense represents the first system to consider voice based stress detection and model adaptation in diverse real-life conversational situations using smartphones. Much of the stress and strain of student life remains hidden. The StudentLife continuous sensing app assesses the day-to-day and week-by-week impact of workload on stress, sleep, activity, mood, sociability, mental well-being and academic performance of a single class of 48 students across a 10 week term at Dartmouth College using Android phones. Results from the StudentLife study show a number of significant correlations between the automatic objective sensor data from smartphones and mental health and educational outcomes of the student body. We also identify a Dartmouth term lifecycle in the data that shows students start the term with high positive affect and conversation levels, low stress, and healthy sleep and daily activity patterns. As the term progresses and the workload increases, stress appreciably rises while positive affect, sleep, conversation and activity drops off. The StudentLife dataset is publicly available on the web. Modeling the properties of individual human mobility is a challenging task that has received increasing attention in the last decade. Since mobility is a complex system, when modeling individual human mobility one should take into account that human movements at a collective level influence, and are influenced by, human movement at an individual level. In this paper the authors propose the d-EPR model, which exploits collective information and the gravity model to drive the movements of an individual and the exploration of new places on the mobility space. They implement their model to simulate the mobility of thousands synthetic individuals, and compare the synthetic movements with real trajectories of mobile phone users and synthetic trajectories produced by a prominent individual mobility model. The authors show that the distributions of global mobility measures computed on the trajectories produced by the d-EPR model are much closer to empirical data, highlighting the importance of considering collective information when simulating individual human mobility. Computer vision applications for mobile phones are gaining increasing attention due to several practical needs resulting from the popularity of digital cameras in today's mobile phones. In this work, we consider the task of face detection and authentication in mobile phones and experimentally analyze a face authentication scheme using Haar-like features with Ad-aBoost for face and eye detection, and local binary pattern (LBP) approach for face authentication. For comparison, another approach to face detection using skin color for fast processing is also considered and implemented. Despite the limited CPU and memory capabilities of today's mobile phones, our experimental results show good face detection performance and average authentication rates of 82 for small-sized faces (40times40 pixels) and 96 for faces of 80times80 pixels. The system is running at 2 frames per second for images of 320times240 pixels. The obtained results are very promising and assess the feasibility of face authentication in mobile phones. Directions for further enhancing the performance of the system are also discussed. Billions of people around the world live without access to banks or other formal financial institutions. In the past several years, many mobile operators have launched "Mobile Money" platforms that deliver basic financial services over the mobile phone network. While many believe that these services can improve the lives of the poor, in many countries adoption of Mobile Money still remains anemic. In this paper, we develop a predictive model of Mobile Money adoption that uses billions of mobile phone communications records to understand the behavioral determinants of adoption. We describe a novel approach to feature engineering that uses a Deterministic Finite Automaton to construct thousands of behavioral metrics of phone use from a concise set of recursive rules. These features provide the foundation for a predictive model that is tested on mobile phone operators logs from Ghana, Pakistan, and Zambia, three very different developing-country contexts. The results highlight the key correlates of Mobile Money use in each country, as well as the potential for such methods to predict and drive adoption. More generally, our analysis provides insight into the extent to which homogenized supervised learning methods can generalize across geographic contexts. We find that without careful tuning, a model that performs very well in one country frequently does not generalize to another. We propose a novel approach to reduce the computational cost of evaluation of convolutional neural networks, a factor that has hindered their deployment in low-power devices such as mobile phones. Inspired by the loop perforation technique from source code optimization, we speed up the bottleneck convolutional layers by skipping their evaluation in some of the spatial positions. We propose and analyze several strategies of choosing these positions. We demonstrate that perforation can accelerate modern convolutional networks such as AlexNet and VGG-16 by a factor of 2x - 4x. Additionally, we show that perforation is complementary to the recently proposed acceleration method of We investigate the use of WiFi Received Signal Strength Information (RSSI) at a mobile phone for the recognition of situations, activities and gestures. In particular, we propose a device-free and passive activity recognition system that does not require any device carried by the user and uses ambient signals. We discuss challenges and lessons learned for the design of such a system on a mobile phone and propose appropriate features to extract activity characteristics from RSSI. We demonstrate the feasibility of recognising activities, gestures and environmental situations from RSSI obtained by a mobile phone. The case studies were conducted over a period of about two months in which about 12 hours of continuous RSSI data was sampled, in two countries and with 11 participants in total. Results demonstrate the potential to utilise RSSI for the extension of the environmental perception of a mobile device as well as for the interaction with touch-free gestures. The system achieves an accuracy of 0.51 while distinguishing as many as 11 gestures and can reach 0.72 on average for four more disparate ones.