Developing a Body Sensor Network to Detect ...

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Developing a Body Sensor Network to Detect Emotions During Driving ARTICLE in IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS · AUGUST 2014 Impact Factor: 2.47 · DOI: 10.1109/TITS.2014.2335151

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5 AUTHORS, INCLUDING: Genaro Rebolledo-Mendez

Angelica Reyes

University of Veracruz

Polytechnic University of Catalonia

39 PUBLICATIONS 201 CITATIONS


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Sebastian Paszkowicz

Lee Skrypchuk

Jaguar Land Rover Limited

Independent Researcher

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Available from: Angelica Reyes Retrieved on: 04 September 2015

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, VOL. 15, NO. 4, AUGUST 2014

Developing a Body Sensor Network to Detect Emotions During Driving Genaro Rebolledo-Mendez, Angélica Reyes, Sebastian Paszkowicz, Mari Carmen Domingo, and Lee Skrypchuk Abstract—Emerging applications using body sensor networks (BSNs) constitute a new trend in car safety. However, the integration of heterogeneous body sensors with vehicular ad hoc networks (VANETs) poses a challenge, particularly on the detection of human behavioral states that may impair driving. This paper proposes a detector of human emotions, of which tiredness and stress (tension) could be related to traffic accidents. We present an exploratory study demonstrating the feasibility of detecting one emotional state in real time using a BSN. Based on these results, we propose middleware architecture that is able to detect emotions, which can be communicated via the onboard unit of a vehicle with city emergency services, VANETs, and roadside units, aimed at improving the driver’s experience and at guaranteeing better security measures for the car driver.

Index Terms—Body sensor network (BSN), driver’s behavior, vehicular ad hoc network (VANET). I. I NTRODUCTION Body sensor networks (BSNs) are becoming more complex due to the use of different kinds of sophisticated sensors, which provide advanced functionalities. BSNs are continuously being integrated into different environments of our everyday lives, including cars. This paper presents the results of ongoing research in the area of emotional detection using a BSN in cars. This paper uses the empirical evidence obtained during one experiment to propose a new architecture designed to prevent accidents caused by driver’s negative emotional reactions while driving. To achieve this, we considered a pervasive computing environment in which one vehicle with communication capabilities was integrated with drivers who wore a BSN in order to collect physiological data that could be related to driving impairment. Most drivers are aware of the effects that drinking alcohol and using cell phones may have on driving [1]–[3]. However, little consideration has been given to other factors that may impair driving such as the emotional state of the driver. According to official statistics, inattention (including emotional factors) could have serious or fatal consequences for driving [4]. For example, according to the U.S. National Highway Traffic Safety Administration [4], 20% of injury crashes in 2009 involved reports of distracted driving. In addition, 2.7% of drivers and motorcycle riders involved in fatal crashes were drowsy, asleep, faManuscript received October 29, 2013; revised January 19, 2014, April 22, 2014, and June 18, 2014; accepted June 19, 2014. Date of publication August 1, 2014; date of current version August 1, 2014. This work was supported in part by Jaguar Land Rover and in part by the Spanish Ministry of Education and Science under project TRA2013-45119-R RPAS OPERATIONS IN THE SINGLE EUROPEAN SKY and project TIN2010-20136-C03-01. The Associate Editor for this paper was C. Olaverri-Monreal. G. Rebolledo-Mendez is with the Facultad de Estadística e Informática, Universidad Veracruzana, 91020 Jalapa, Mexico, and also with AffectSense, 91500 Veracruz, Mexico (e-mail: [email protected]; [email protected]). A. Reyes is with the Department of Computer Architecture, Universitat Politécnica de Catalunya, 08034 Barcelona, Spain (e-mail: [email protected]). S. Paszkowicz and L. Skrypchuk are with the Jaguar Land Rover Research and Advanced Engineering, International Digital Laboratory, Warwick Manufacturing Group, University of Warwick, Coventry CB4 7AL, U.K. (e-mail: [email protected]; [email protected]). M. C. Domingo is with the Escola d’Enginyeria de Telecomunicaciói Aeroespacial de Castelldefels and the Departament d’Enginyeria Telemática, Universitat Politécnica de Catalunya, 08034 Barcelona, Spain (e-mail: mari. [email protected]). Digital Object Identifier 10.1109/TITS.2014.2335151

tigued, ill, or had had a blackout. These are important figures that need to be addressed for accident prevention. This paper taps into this need and presents empirical evidence toward the detection of emotions. Previous work has focused on the detection of inattentive states in relation to drunkenness and other nonemotional factors in driving. A system to automatically detect both drunk and drowsy driving states was developed by Sakairi and Togami [5]. Chin-Teng et al. [6], [7] proposed a technique to continuously detect drivers’ cognitive states in relation to their abilities in perception, recognition, and vehicle control using electroencephalography (EEG). The authors developed a drowsiness-estimation system based on EEG to estimate a driver’s cognitive state when he/she was driving a car in a virtual-realitybased dynamic simulator. EEG signals have been also used to detect drowsiness. For example, Flores et al. [8] proposed a real-time wireless EEG-based computer interface system to collect, amplify, filter, preprocess, and send EEG signals to a signal-processing module using wireless communication. The signal-processing module was capable of detecting real-time drowsiness. Some work have addressed the recognition of the emotional state of the drivers using BSN in simulation environments [9]–[11], whereas others have analyzed drivers’ emotions in real-life scenarios [12]–[14]. Although the papers reporting experiments in simulated environments provide a good indication of the feasibility of detecting emotional states during driving, there are indications [9] that subjects experienced different emotions in simulation environments to those they may experience in real conditions. Because real-life driving conditions potentially provoke genuine emotions, we chose to carry out our experiments in realistic settings as a means to provide unique insights into drivers’ emotional behaviors. In [12], physiological sensing has been applied to determine the driver’s stress levels using an electrocardiogram (ECG), an electromyogram, and electrodermal activity (EDA) in real scenarios comprising highway and city driving. The authors suggested that the first sensors that should be integrated into a car should be the skin conductance and heart rate sensors [12]. In [13], a real-time methodology for the assessment of drivers’ stress has been introduced, employing not only physiological data but also driving history extracted from Global Positioning System records and the vehicle’s controller area network bus data. This information has been incorporated into a Bayesian network to estimate the levels of stress. Their results in real driving conditions show accuracy of 82% in stress event detection. However, the authors notice that more reliable stress metrics should be based, for example, on EEG [13]. Singh et al. [14] monitored the driver’s affective state using physiological signals (EDA and photoplethysmography) during on-road driving experiments. This paper aims to provide preliminary empirical evidence of how to recognize four emotional states in a real-world driving situation: concentrated, tension, tired, and relaxed. The objective of this paper is twofold. On one hand, we present one field study specifically defined to measure emotions in drivers using a BSN. On the other hand, we propose an architecture describing how the BSN to detect emotions could be integrated into a vehicular onboard unit (OBU). Our proposal consists of detecting driver’s emotions and defining corresponding actions such as the transmission of notification messages to emergency services, other vehicles within the transmission range, roadside units

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Fig. 1.

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Proposed scenario.

(RSUs), and nearby pedestrians operated by the OBU and/or the driver’s wireless personal device. The structure of this paper is as follows. Section II describes a scenario for the inclusion of a BSN in conjunction with the vehicle’s OBU. Section III presents a proposition for an architecture taking advantage of emotional recognition using a BSN. Section IV presents a study where the resulting BSN was deployed for emotional detection during real driving conditions. Finally, a discussion of our work and the research challenges to be addressed is presented in Section V. II. P ROPOSED S CENARIO We propose a scenario (see Fig. 1) where the driver’s behavior is monitored in real time. A driver wears a BSN consisting of at least two sensors capable of reading physiological signals. The driver’s physiology is constantly measured and sent to the OBU, which is embedded in the vehicle. In this context, the OBU determines the driver’s emotional states, considering the models of emotions similar to those described in Section IV. In this proposition, common causes of traffic accidents related to emotional states such as cognitive fatigue or stress can be detected. The OBU provides clues in an effort to make the driver become aware of these states. In this paper, we focus on highway and city contexts, as well as the types of emotional reactions that occur during the driving sessions. Based on the results presented in Section IV, we hypothesize that it is possible to safely monitor the driver and detect emotions that may pose a danger for the driver and other road users. Because of this, our proposed architecture considers mechanisms to inform emergency services in case there is an associated driving danger (see Fig. 1). III. A RCHITECTURE FOR D RIVERS E MOTION D ETECTION U SING BSN We propose a BSN deployed to sense drivers’ physiological change in real time, as well as to examine the feasibility of establishing an onboard system capable of sensing physiological data and of calculating a driver’s emotional state in real time. Our field study consists of an ECG, EEG, EDA, and respiration sensors. This paper presents results in relation to the EEG and EDA sensors. Future work will integrate results from the data obtained with the other sensors. If the BSN detects a driver’s emotional state that could produce impaired driving such as excessive tiredness or tension, then alarm notification messages are sent from the vehicle’s OBU to the RSUs or emergency services (see Fig. 2).

Fig. 2. Integrated BSN and a vehicle’s OBU.

A. BSN Module The two sensors used in this BSN consisted of two portable commercially available sensors. The physiological data collected consisted of neural and EDA. The sensor used to collect neural activity was NeuroSky’s MindWave.1 The MindWave software indicates two types of neural activity: attention and meditation. Attention is related to a state of alertness and denotes an increase in Beta waves. Meditation is related to increases in Alpha waves and indicates a state of alert relaxation. The EDA sensor was Affectiva’s Q sensor [15] consisting of a bracelet with a sensor attached to it. The Q sensor measures EDA, which is also called skin conductance. The Q sensor displays variations in electrical activity measured at the surface of the skin in microsiemens (a unit of conductance). In its raw format, EDA expresses electrical conductance (inverse of resistance) across the skin. Changes in EDA are automatically and unconsciously activated by the wearer’s brain and reflect arousal levels on the part of the wearer. Higher levels of EDA indicate higher levels of arousal and could be related to a person being more engaged, stressed, or excited. Lower EDA indicates lower levels of arousal and relates to disengagement, boredom, or calmness. The decision to utilize these sensors was primarily based on driver safety. It was of paramount importance to use a BSN that was not obtrusive or impeded a driver’s ability to correctly perform all the tasks involved in guiding a car. A second consideration was the reliability of the data collection process. The EDA data collection mechanism with the Q sensor had previously been tested [16], [17] as it was specifically designed for field data collection. We assume that its reliability could be ascertained. Unlike the Q sensor, the NeuroSky device does not store data on the device, but depends on external storage mechanisms and a steady Bluetooth-enabled connection. We achieved this by developing a program capable of reading data generated by the MindWave and logging it onto a laptop computer serving as the vehicle’s OBU. The acquired data are transmitted via Bluetooth, but future versions may use a wireless communication module using ultrawideband or IEEE 802.15.6 for wireless transmission between the 1 http://www.neurosky.com/Products/MindWave.aspx

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IV. M ETHOD An experiment was aimed at collecting physiological data using the proposed architecture (except for the emergency services) and a BSN in real driving conditions. The experiment lasted for seven working days. It consisted of asking participants to wear sensors and to drive in two driving conditions in relation to highway and city environments. Gathering data from two conditions allowed the study of body reactions in the same driver. It also enabled the study of multiple data points potentially useful for understanding the drivers’ physiological responses. Fig. 3. Information to be transmitted to the emergency services.

A. Participants sensors and the gateway. Bluetooth or Zigbee could be also used to forward the physiological data from the gateway to the vehicle’s OBU. The information passed between the BSN of the driver and the OBU includes the following: health state and characteristics of the emotional state that impairs driving (see Fig. 3). B. Vehicle’s OBU The acquired data from the BSN are processed by the OBU in real time since the BSN gateway might be restricted by its low capabilities and limited battery capacity. The OBU is divided into three major modules: the feature extraction module, the intelligent driver’s state recognition module, and the alarm notification module. The first module extracts features from the selected biosignals. These features are used by the intelligent driver’s state recognition module to determine if the driver has one of the predefined emotional states. Alarm notifications are sent to the emergency services in case of detection of an emotional state that impairs driving. The communication between OBU and Emergency Services will exploit various communication technologies (DSRC, UMTS/HSDPA, and WAVE) empowering OBUs with vehicular networks and cellular or wireless communications. Vehicle-to-vehicle networks allow faster alarm notifications since sensing and propagation of information are done on the spot in real time via multihop communication. Surrounding vehicles will be immediately notified of the alarm and can be further propagated via radio base stations to the emergency services. C. Emergency Services The emergency services can be also notified if the driver requires medical assistance, for example, due to excessive stress. The information sent from the vehicle’s OBU to the emergency services includes the information collected from the BSN, driver and vehicles characteristics, as well as OBU location (see Fig. 3). Accurate OBU location in open-air scenarios can be provided by the Global Navigation Satellite Systems. However, in dense urban and underground scenarios, these systems suffer from the weakness (or even the blockage) of their signals when the receiver operates in non-line-of-sight conditions. Switching between technologies, such as wideband communication provided trough 3G radio network-based localization methods wireless sensor networks, allows determining the most accurate position of the OBU. Pedestrians and other drivers may be also warned of a driver’s indisposition to drive properly, through the use of notification messages forwarded to their own OBU (e.g., smartphones) using vehicle-to-pedestrian or infrastructure-to-pedestrian communications. The following section reports an evaluation made on an implementation of the architecture (excluding the emergency services) and the BSN in the context of an experiment involving drivers in real driving scenarios.

There were 24 drivers (13 males and 11 females) aged between 23 and 48 years old. The average driving time was 8 min and 5 s per condition. Weather, traffic conditions (vehicle volume, and pedestrians), and time of the day were not controlled, and drivers faced variable unpredictable situations. Information related to the participants’ coffee ingestion and hours of sleep during the night prior to the experiment was collected via questionnaires. Participants were asked to spend 2 h of their time in order to complete the experiment. Prior to the experiment, all the participants filled out a consent form. B. Driving Conditions and Driving Tasks The two driving conditions were simulated on Jaguar Land Rover’s vehicle proving ground in Gaydon in the U.K. The Emissions Circuit served as the highway-like situation, and Gaydon’s streets simulated a city-like environment containing roundabouts, pedestrian crossings, and speed limits. The car used for the experiment was a Range Rover (2010 Model Year). The task the participants were asked to perform was to drive the car as normally as they would do on a regular day, but to keep the speed below 100 mi/h (160.93 km/h) in order to comply with Gaydon’s guidelines for experimentation. The participants were told to treat the proving ground as normal public roads and to follow the traffic rules applicable for the U.K.2 Before driving, the participants were asked to adjust the seat, the steering wheel, and the mirrors; and all seat belts were checked to be in place. One team member sitting in the passenger seat provided the driving tasks by reading a predefined set of instructions. These instructions consisted of driving indications that allowed the drivers to navigate the proving ground. Examples of the instructions included “drive to the roundabout at the exit of the observation tower area” or “complete two laps of the emissions circuit.” C. Procedure The procedure consisted of four stages. Stage 1: Drivers were briefed about the aims of the experiment and its processes and were asked to fill out a consent form. Stage 2: Drivers were asked to wear several types of sensors. This report focuses only on two types of physiological data. Stage 3: Drivers were asked to drive in two types of conditions. The first was always highway conditions, followed by city conditions. A video camera was placed on the car’s dashboard to film the driver’s face while driving. Stage 4: The video was immediately used after finishing Stage 3. The drivers were asked to self-report the emotional state they saw at fixed intervals (see Table I for the emotional states); please note that responses were coded in relation to only the four main emotional states. 2 https://www.gov.uk/speed-limits


TABLE I E MOTIONAL S TATES C ONSIDERED FOR THE E XPERIMENT

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TABLE III E MOTIONAL I NFORMATION FOR T WO D RIVING C ONDITIONS

TABLE II D ESCRIPTIVE S TATISTICS FOR D RIVER 10 U NDER D RIVING C ONDITION 2, T IME 7 MIN AND 6 S (N = 427)

The aim of collecting the reports was to look for correlations between one or multiple physiological responses (measured using the Q sensor and the NeuroSky device) and emotional information provided by the drivers themselves. Both the physiological data and the selfreports were used to build preliminary models of emotional reactions while driving. The first model employed logistic regression, where one physiological signal was used to predict emotions. The second model was based on a K-means algorithm to classify the physiological data to predict an emotional state. D. Results The first step consisted of organizing drivers by considering their physiological data and the completeness of their self-reports. Fiftyfour percent (N = 13) of drivers had complete data and were thus considered as part of the analyses. Since the neural activity was captured in raw format, it underwent two transformations: fast Fourier transformation (15%), followed by natural logarithm transformation. Descriptive analyses (see Table II) show that neural activity had high coefficients of variation and did not have any linear relation with other variables. Unlike neural activity, EDA shows lower coefficients of variations. Given the lack of linear relations among the variables, they were treated as independent. To build the regression models, the levels of significance for the variables were tested for a response variable “affect,” a design variable with values from 1 to 4 referring to the categorical values of the main emotional states on Table I. Since it was found that EDA has a significant correlation among all the drivers in the subsample (N = 13, Pearson’s = 0.929, p < 0.05), we chose to utilize this variable for the development of models of emotions. Two principal component analyses (PCA) were used to identify the driver who had the most representative EDA pattern of the subsample. For these analyses, the drivers were treated as variables, and the drivers’ EDA were treated as cases. The results indicated that seven drivers account for 98.5% of cumulative percentage of variance of the subsample’s EDA behavior. A second PCA, on which the seven drivers identified on the first PCA were treated as variables, suggested that driver 10 explains 99.1% of the variability of the newer subsample (N = 7). Driver 10’s EDA data were thus employed as a training set. The data from the rest of the drivers (N = 12) were used as a test set. Table III includes descriptive statistics for the emotional data, as self-reported by the drivers for the two driving conditions. Given that some emotions are not present during driving, five logistic regression

Fig. 4. Fitted function and observed values to detect the state concentrated.

models were developed: three for condition 1 and two for condition 2. One model (see Fig. 4) and its formula for the detection of the state “concentrated” for “city-like” condition are presented as example, i.e., y=

exp (−4.05 + (1.68857)∗ x) . (1 + exp(−4.05 + (1.68857)∗ x))

In the formula, “y” values refer to the response variable, whereas “x” values represent the current EDA measurement. To test the model, we fed this with drivers’ physiological data and calculated the levels of agreement (using Cohen’s Kappa) between the models’ responses and the self-reports provided by driver 10. The results showed that the model’s Kappa index is 0.5455, indicating a moderate agreement between the model and the self-reports. The level of agreement between the model and the training set was 0.7186, indicating a substantial agreement. In comparison, a K-means classifier built with the same data set (training and test) has a Kappa of 0.2745, with a fair level of agreement. A characterization of agreement levels proposes levels < 0 to indicate no agreement, 0–0.20 as slight, 0.21–0.40 as fair, 0.41–0.60 as moderate, 0.61–0.80 as substantial, and 0.81–1 as almost perfect agreement [18]. The accuracy of the other four models indicated slight and no agreement. The cause may be the self-reports as they were provided by individual drivers and not by one single observer. Future studies will focus on building models that consider affective assessment only by one person. In addition to building new models, we plan to classify using the one-versus-all approach to pick up the most promising class.

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The results of this preliminary model are encouraging and provide an indication of the feasibility of detecting emotions for real driving situations. Given the levels of agreement between the preliminary model and the self-reports, this methodology has the potential to provide accurate classification of emotions that can be integrated with the vehicle’s OBU. Future studies will analyze drivers’ personal characteristics such as age, driving experience, and use of medications. Other nonphysiological data, such as the pressure exerted on the accelerator and/or the brake, will be used to build more reliable models of emotions. V. C ONCLUSION AND F UTURE W ORK This paper has presented, on one hand, the results of a field study specifically set up to measure emotions in drivers using a BSN. To that end, we defined a BSN as consisting of two sensors. On the other hand, this paper has proposed an architecture describing how the BSN could be integrated into vehicular ad hoc networks (VANETs) in order to analyze driver’s emotions and to orchestrate actions operated by an OBU with the aim of preventing potentially fatal accidents related to negative emotions such as tiredness and stress. This architecture defines actions, including the transmission of notification messages to the emergency services, other vehicles in the transmission range, RSUs, and nearby pedestrians through the VANET. The dissemination of warning and safety messages through VANETs would alert other drivers about possible hazards, increase the available maneuvering time [19], and prevent accidents that would have been caused by driver’s negative emotional behaviors. The results of this study show preliminary evidence of measuring emotions using a BSN and logistic regression. Based on these results, we hypothesize that it is possible to quantify driver’s emotions and the role the proposed architecture plays in preventing car accidents (involving the driver and other people and vehicles) by constantly monitoring the driver’s emotions. Future experiments will analyze the following: 1) the role of emotional awareness (emotional intelligence) and self-regulation of negative emotions while driving; 2) the dynamics of emotional change in relation to external factors such as driving conditions and duration, age, experience, and gender; and 3) the role of the architecture in reducing car accidents. Work for the future also consists of carrying out in-depth data analyses and correlating emotional responses with driving behavior such as pressure on the accelerator and brake and adding a communication component to existing VANETs. We will also analyze which protocols and tools better fit the use of VANETs for user applications. Finally, we would like to study some technical aspects from VANETs, test the overall architecture, and see how much we can reduce preventable car accidents.

R EFERENCES [1] S. Kojima et al., “Noninvasive biological sensor system for detection of drunk driving,” in Proc. 9th Int. Conf. ITAB, 2009, pp. 1–4. [2] Y.-C. Wu, Y.-Q. Xia, P. Xie, and X.-W. Ji, “The design of an automotive anti-drunk driving system to guarantee the uniqueness of driver,” in Proc. ICIECS, 2009, pp. 1–4. [3] W. J. Horrey and C. D. Wickens, “Examining the impact of cell phone conversations on driving using meta-analytic techniques,” Hum. Factors, vol. 48, no. 1, pp. 196–205, 2006. [4] “Traffic safety facts—Distracted driving 2009,” U.S. Dept. Transp., Washington, DC, USA, 2010. [5] M. Sakairi and M. Togami, “Use of water cluster detector for preventing drunk and drowsy driving,” in Proc. IEEE Sensors, 2010, pp. 141–144. [6] C.-T. Lin et al., “A real-time wireless brain–computer interface system for drowsiness detection,” IEEE Trans. Biomed. Circuits Syst., vol. 4, no. 4, pp. 214–222, Aug. 2010. [7] L. Chin-Teng et al., “EEG-based drowsiness estimation for safety driving using independent component analysis,” IEEE Trans. Circuits Syst. I, Reg. Papers, vol. 52, no. 12, pp. 2726–2738, Dec. 2005. [8] M. Flores, J. M. Armingol, and A. de la Escalera, “Driver drowsiness warning system using visual information for both diurnal and nocturnal illumination conditions,” EURASIP J. Adv. Signal Process., vol. 2010, no. 1, p. 438 205, Jul. 2010. [9] C. D. Katsis, N. Katertsidis, G. Ganiatsas, and D. I. Fotiadis, “Toward emotion recognition in car-racing drivers: A biosignal processing approach,” IEEE Trans. Syst., Man, Cybern. A, Syst., Humans, vol. 38, no. 3, pp. 502–512, May 2008. [10] C. D. Katsis, Y. Goletsis, G. Rigas, and D. I. Fotiadis, “A wearable system for the affective monitoring of car racing drivers during simulated conditions,” Transp. Res. C, Emerging Technol., vol. 19, no. 3, pp. 541– 551, Jun. 2011. [11] H. Cai and Y. Lin, “Modeling of operators’ emotion and task performance in a virtual driving environment,” Int. J. Hum.-Comput. Stud., vol. 69, no. 9, pp. 571–586, Aug. 2011. [12] J. A. Healey and R. W. Picard, “Detecting stress during real-world driving tasks using physiological sensors,” IEEE Trans. Intell. Transp. Syst., vol. 6, no. 2, pp. 156–166, Jun. 2005. [13] G. Rigas, Y. Goletsis, and D. I. Fotiadis, “Real-time driver’s stress event detection,” IEEE Trans. Intell. Transp. Syst., vol. 13, no. 1, pp. 221–234, Mar. 2012. [14] R. R. Singh, S. Conjeti, and R. Banerjee, “A comparative evaluation of neural network classifiers for stress level analysis of automotive drivers using physiological signals,” Biomed. Signal Process. Control, vol. 8, no. 6, pp. 740–754, Nov. 2013. [15] Liberate Yourself from the Lab: Q Sensor Measures EDA in the Wild, Affectiva Inc., Waltham, MA, USA, Aug. 13, 2013. [16] Z. Liu et al., “Measuring the engagement level of TV viewers,” in Proc. 10th IEEE Int. Conf. Workshops Autom. FG Recog., 2013, pp. 1–7. [17] Y. Ayzenberg, J. Hernandez, and R. W. Picard, “FEEL: Frequent EDA and Event Logging, a mobile social interaction stress monitoring system,” in Proc. CHI Extended Abstr. Hum. Factors Comput. Syst., Austin, TX, USA, 2012, pp. 2357–2362. [18] J. R. Landis and G. G. Koch, “The measurement of observer agreement for categorical data,” Biometrics, vol. 33, no. 1, pp. 159–174, Mar. 1977. [19] B. K. Chaurasia and S. Verma, “Haste induced behavior and VANET communication,” in Proc. IEEE ICVES, Nov. 2009, pp. 19–24.

Developing a Body Sensor Network to Detect ...

Developing a Body Sensor Network to Detect ...

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