CapSeat - Capacitive Proximity Sensing for Automotive Activity Recognition 1
Andreas Braun1 , Sebastian Frank2 , Martin Majewski1 , Xiaofeng Wang2 Fraunhofer IGD, Darmstadt, Germany, {firstname.lastname}@igd.fraunhofer.de 2 RheinMain University of Applied Sciences, Wiesbaden, Germany,
[email protected],
[email protected]
ABSTRACT
Inattentiveness is one of the major causes of traffic accidents. Advanced car safety systems try to mitigate this by detecting potential signs of distraction or tiredness, and providing alerts to the driver. In this paper we present CapSeat - a car seat equipped with integrated capacitive proximity sensors that are used to measure a wide range of physiological parameters about the driver. This can support safety systems by detecting inattentiveness and increase passive safety by facilitating suitable seat adjustments and posture detection. We present a sensor electrode layout suitable for detecting the necessary parameters and processing methods that acquire multiple physiological parameters from sensor data, using a variety of different algorithms. A prototype of the system is presented that was evaluated for all detectable parameters in a proof-of-concept study. We achieved a classification precision between 95% and 100%. Figure 1. CapSeat with exposed electrodes (left) and covered up (right)
Author Keywords
Car seat; capacitive proximity sensing; driver safety; classification ACM Classification Keywords
H.5.2. Information Interfaces and Presentation (e.g.,HCI): User Interfaces—Ergonomics, Prototyping INTRODUCTION
The average American worker in 2009 spent about four hours on their commute every week, 89.5% of them used a passenger vehicle [22]. Consequently, a modern car is equipped with a multitude of different functions that go beyond driving and range from safety systems to comfort functions. Despite of improvements in the last decades, still more than 30,000 people die each year in vehicle accidents in the US [9]. A common cause of accidents is inattentiveness of the driver.
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[email protected]. AutomotiveUI ’15, September 01 - 03, 2015, Nottingham, United Kingdom Copyright © 2015 ACM. ISBN 978-1-4503-3736-6/15/09...$15.00 DOI: http://dx.doi.org/10.1145/2799250.2799263
Even short distractions can lead to fatal accidents. Consequently, systems that notice inattentiveness and provide suitable countermeasures are an active area of research and Human Machine Interaction. In this work, we set out to identify how capacitive proximity sensor technology can be used to create an Advanced Driver Assistance System that supports the driver in various situations and tasks. This technology detect the presence of conductive objects that are in an electric field. They have a variety of applications in human sensing [25, 23, 3]. Their primary advantage is measurement over a distance and through any non-conductive material. As no direct contact is required we can develop a mechanically robust system that does not limit the freedom of exterior design. As opposed to wearable sensors, integrated systems can be tuned to a specific vehicle and can’t be forgotten by the driver. A major disadvantage is the limited resolution of integrated capacitive proximity sensors. In our case, the focus is on physiological sensing, whereas we consider two potential areas. The first is the detection of biomechanical parameters that are required for optimal configuration of the seat and ensuring a safe driver posture. The second area is the recognition of an inattentive driver. This can be used to inform an Advanced Driver Assistance System. We aim to evaluate if capacitive proximity sensors are suitable for physiological sensing in a car, which number and layout of electrodes and sensors is suitable, and
which processing methods are required for recognizing driver state and activities in a vehicle. The result is CapSeat - a system composed of sixteen capacitive proximity sensors that are invisibly integrated into a car seat. It detects three anthropometric parameters (occupancy, optimal seat settings, and driver posture) and four activity parameters (nodding, yawning, gazing and erratic steering). This can be used to give feedback to the driver, or inform existing safety systems about the driver’s state. We evaluate a suitable sensor layout that is able to measure the presence and activities of the driver with respect to the limited resolution of the senor system. For each parameter, we provide suitable processing methods. CapSeat has been integrated into an existing car seat and attached to a demonstration and testing system that allows us to evaluate different functions of an Advanced Driver Assistance System. The contributions of this paper are: 1. We propose a sensing system for car seats, composed of capacitive proximity sensors that are invisibly integrated into different areas of the seat. 2. We present processing methods for detecting various physiological parameters from capacitive proximity sensor data. 3. We outline a concept for an Advanced Driver Assistance System that aids in seat adjustment, proposes correct posture during the drive, and informs on signs of potential drowsiness. 4. We describe a prototypical implementation, adaptation towards real-life challenges, as well as design and results of an exploratory study. RELATED WORK
Capacitive sensors can be categorized into touch and proximity sensing varieties. Additionally, there are three different measurement modes. Shunt mode uses distinct sender and receiver electrodes, similar to transmit mode, where a conductive object is coupled to the sender. Loading mode uses a single electrode that is continually charged and discharged [25]. The sensors have been used for physiological sensing in the past. Cheng et al. have created active capacitive sensors. They are attached to different body parts, sensing activities such as swallowing, speaking, or sighing [4]. While they achieve a high accuracy, contact between body and electrodes is required. A shunt mode system by Rus et al. uses sensor wires integrated into a sheet to detect lying postures [20]. Capacitive proximity sensors have been previously used in some automotive applications. In cooperation with NEC, Smith developed a shunt mode capacitive proximity sensors that can discriminate different objects on the passenger seat of a car [23]. The proposed use case is to identify presence and orientation of a child seat. This information could be used to deactivate the passenger airbag, thus preventing injuries of the child. Capacitive systems that only detect occupation without additional pose information are fairly common [6, 10]. The Geremin by Endres et al. is a small interaction device placed next to the steering wheel that enables small
gestures for controlling car systems, while keeping the hands on the steering wheel [5]. We have used a heterogeneous sensor array in an arm rest that distinguish several arm positions and two different modes of finger gesture interaction [2]. Sensor-supported seat adjustment is an active area of research. Riener uses pressure mats on the seat to statically identify persons and distinguish activities, based on dynamic pressure values [18]. Lorenz combines a variety of different sensors to measure anthropometric parameters of the driver [15]. This includes pressure mats for posture recognition, but also capacitive sensors in the head rest that are capable of detecting the driver’s height and aid in adjusting the head rest position. Opposed to those systems, CapSeat uses a single category of integrated sensors that require no direct contact to the driver. Detecting inattentiveness or drowsiness of the driver is an important support system to prevent accidents. It is indicated e.g. by heart rate changes, gazing, micro-sleep, yawning, or erratic steering maneuvers. These parameters can be assessed using a wide variety of sensors [21]. Li et al. use a system composed of depth camera for gaze and blink tracking, pulse rate sensor, and steering angle measurement [14]. Riener et al. use the heart rate variability to detect the current level of arousal on the driver [19]. There are some commercial sensors available that have been integrated in car seats, such as the Plessey system for capturing a contact-free ECG [16]. CapSeat detects several parameters that are indicative of inattentiveness, without requiring multiple sensor categories. DRIVER ASSISTANCE USING CAPACITIVE PROXIMITY SENSORS
Capacitive proximity sensors are able to detect grounded objects at a distance of 10 cm to 30 cm with a good spatial resolution [24]. They can be installed below any non-conductive material. In this work we use the previously mentioned loading mode that relies on single electrodes. This enables heterogeneous layouts with varying sizes of electrodes that are not arranged uniformly. Our system supports three main functions. An assisted seat adjustment that informs the driver on properly setting the seat to a safe and comfortable position. During the drive, the posture is continuously monitored, to avoid positions that may be dangerous during an accident. Finally, the system detects various signs that may be an indicator of drowsiness. Those are nodding, yawning, gazing, and erratic steering behavior. This requires a careful selection of the number and position of sensors. This layout is described in the following section. Electrode Layout
A basic, safe driving position requires the correct distance from the pedals, correct inclination of the back rest, and a correct height of the head rest. This necessitates sensors that track leg and knee position, when the pedals are touched, sensors that track correct position of the back and shoulders when the steering wheel is held, and sensors in the head rest that are able to acquire the correct head position. These head rest sensors additionally need a configuration that detects gazing, nodding and yawning. Another requirement is measuring the
Figure 3. Geometry of knee angle in relation to seat and person parameters Figure 2. Sensor electrode layout of CapSeat, position on body, and their specific purpose
Adjusting Head Rest, Seating, and Back Rest
level of comfort. Therefore, we add a sufficient number of sensors to the seating and back rest that measure a distribution of pressure. Finally, we also want to track the steering movement. This is accomplished by adding sensors to the lateral back-support of the seat. The final sensor configuration is shown in Figure 2, with electrode positions relative to a body and it’s associated positions on the seat. In conclusion, sixteen electrodes of different shape and size are added to the car seat and attached to sensors. We can now present the methods that are used to analyze the different aspects of the CapSeat measurement system. The data processing initially performs a calibration of the sensors according to the ambient capacitance. A low-pass filter for noise reduction and normalization is applied. The final processing steps depend on the specific function. We use either machine learning classification or some regression based on the sensor values of one or more sensors. An overview of all methods is in Table 1. The details are explained in the specific sections. Seat Adjustment
As previously mentioned, the seat adjustment is comprised of several activities. At first we need to detect occupancy and preferably distinguish different objects that could be on the seat. The actual adjustment is the next step, comprised of setting head rest, back rest and seating area to a secure and comfortable level. For this we need to determine if the person is currently sitting upright, as necessary requirement for all additional measurements. Occupancy and Upright posture
We distinguish three different classes of occupancy: empty seat, foreign object on seat, and person present. For this we use a multiclass SVM classifier that takes the normalized values from all sensors as features. A present driver should result in readings from all available sensors Therefore, sixteen features are associated to three distinct classes. The right position on the seat is likewise based on actual values of all sensors. However, here we only require two different classes and can use binary SVM.
The first step in a safe adjustment is setting the head rest to the appropriate position. This is based on the height of the driver, or, more specifically, the torso length. This is strongly correlated to the actual height of a person [8]. However, this can be simplified, as it is sufficient to center the head in front of the head rest. For this we use another multiclass SVM, based on the sensors of the head rest. The idea is to capture proper head rest position, and distinguish it from positions too low (training data collected 5cm below), or positions too high (collected 5cm above). This has to be accomplished for drivers with varying torso length. During the adjustment process the system can give feedback on the direction in which to adjust. The seating area can have a number of different settings, ranging from changing inclination, to adjusting overall seat height. A safe setting requires the driver to use the pedals quickly and easily. This necessitates free movement of the legs, as indicated by a knee angle of approximately 105° that is ideal for range of motion in any direction. CapSeat can use the sensors in the seating area to get this information. As shown in Figure 3, the required angle is γ. The sensors under the knees can determine the angle α. There is a logarithmic relationship between the parameters. Therefore, we want to find a regression for α = A · ln(v) + B, whereas v is the current value of a knee sensor. For the remaining parameters we use a combination the persons height and average leg length values for the general population [8]. We calculate the distance and direction, in which the seating has to be moved longitudinally. In order to adjust the back rest, we follow a similar approach as with the head rest adjustment. The correct posture is indicated by an elbow angle of approximately 115° [17]. Taking samples of the five sensors in the back rest, as well as the two pelvic sensors, we train a multiclass SVM with persons in three postures: correct, too far to the back, and too far to the front. Measuring Comfort Level
The perceived level of comfort of drivers is strongly correlated with the pressure distribution on the seat. Areas with increased pressure can cause discomfort [13]. Capacitive proximity sensors can be designed to not only react to presence, but also to the geometric deformation of the electrodes that
Table 1. Overview of processing methods of CapSeat
Property
Method
Features
Result
Occupancy
Multiclass SVM
All sensors
Three classes: Empty, foreign object, person
Driver posture
Binary SVM
All sensors
Two classes: Upright, not upright
Head rest adjustment
Multiclass SVM
Three head rest sensors
Three classes: Correct, too low, too high
Seating adjustment
Value ratio
Sensors below knee
Knee angle in degrees
Back rest adjustment
Multiclass SVM
Two pelvis sensors, all back rest sensors
Three classes: Correct, too far back, too far front
Comfort adjustment
Value ratio
Seating sensors, back rest sensors
Balance of proximity distribution
Head posture
Multiclass SVM
Three head rest sensors
Three classes: Correct, whiplash area, airbag zone
Steering velocity
Binary SVM
Back rest sensors - value and derivative
Two classes: Too high, normal
Yawn detection
Binary SVM
Single upper back sensor - frequency spectrum
Two classes: Yawn, breathe
Nod detection
Binary SVM
Three head rest sensors - derivative values over time
Two classes: Nod, no nod
Gaze detection
Multiclass SVM
Three head rest sensors
Six classes: No gaze, down, up, left, right, straight
Posture Tracking During Drive
Figure 4. Angular steering velocity measured with accelerometer and associated sensor values from left arm sensor
During the drive there are two potentially dangerous postures of a person in the vehicle. If the head is too far from the head rest, there is a danger of whiplash during an accident. If the head is too close to the airbag, there is an additional risk of injury. The latter position would be highly unorthodox for a driver, but may occur for passengers. In this case, the airbags could be deactivated temporarily to prevent additional injuries. For this measurement, we are also using a multiclass SVM with the classes correct, in whiplash area, and in air bag area. The current values of the three head rest sensors are used as features. Supporting drowsiness detection
is indicative of pressure, with the proximity component becoming less relevant with increasing pressure [1]. The sensor layout in CapSeat is similar enough, so pressure values can be determined. The basic idea is to adjust the seat in a way that the recognized pressure distribution is ideal, or that the seat adjusts to a position that was previously considered good by the driver. The literature in this domain is inconclusive, as to the time required to find an actually comfortable position for the driver [13]. The sum of sensor values of a driving sitting still and upright varied from 2.35 at 64 kg body weight to 3.25 at 100 kg body weight (normalized data from 11 of 16 sensors - no arm sensors or head rest sensors). Thus, our system strongly correlates with weight of the driver. In this work we refrain from evaluating this aspect, as it would require a prolonged testing period during driving conditions.
The CapSeat system for drowsiness detection is intended to provide contextual information for existing systems that determine driver fatigue. Overall, we are trying to capture four different parameters that may be indicative of drowsiness: nodding, yawning, gazing, and erratic steering. Angular Steering Velocity
Fatigued drivers have a tendency towards a more erratic steering behavior, e.g. when they notice that they left the lane and quickly turn in the other direction. A good measure for the angular velocity in such cases is a change of 150°/s [12]. The correlation between data from an accelerometer attached to the steering wheel and the CapSeat sensor next to the left arm is shown in Figure 4. The values are scaled and normalized from -1 to 1. To perform this detection the sensors close to the arms are most suitable. We put their derivative data into a binary SVM, deciding if angular velocity is lower or higher than the boundary condition.
Figure 6. Prototype seat on evaluation system Figure 5. Values of upper back sensor during regular breathing and three yawn events
Nod Detection
Nodding is indicated by a rapid movement of the head in a downwards direction. In CapSeat, this is easiest to measure using the sensors in the head rest. We use a time-window of values, as well as the derivatives as features for another binary SVM. The classes are nodding and no nodding.
The evaluation software is based on C# and the .NET framework. A median filter is applied to the sensor data, which is normalized. A baseline is initially calibrated by using the average of 50 samples. The maximum values are determined by experiment (single finger touch on a blank electrode). Some processing methods require the derivatives of sensor values or a FFT, which are calculated next. The machine learning classifiers use the Accord.NET library.
Yawn Detection
Yawning is a disturbance in the regular breathing behavior that is indicative of tiredness. In Figure 5 we can see the data of the upper back sensor, which is closest to the chest and therefore registers chest movement the best. Regular breathing is interrupted by three yawn events that cause a spike in sensor values and a disruption of periodical breathing. The changes in periodic movements are best observed in the frequency domain. Therefore, a FFT over five seconds is generated and refreshed every 50 ms. The frequency space is separated into 64 bins that are the input features for a binary SVM, classifying either yawning or breathing. Gaze Detection
Gazing at a certain spot (particularly not to the front) is another indicator for fatigue. In this work, we use the position of the head and a lack of movement, as indicator for gazing. Therefore, both normalized values of the head rest sensors and their derivatives are used as features for a multiclass SVM that has six different outputs. No gazing and gazes into five different directions (down, up, left, right, straight). The training data was collected using markers on the wall, indicating head angles of approximately 45°. A drowsiness detection system that classifies all those directions could consider gazing in a straight direction as non-critical. PROTOTYPE SYSTEM
We equipped a car seat with sixteen capacitive proximity sensors as shown in Figure 1. The assembly was installed in an overall prototype system with movable steering wheel and pedal area, as shown in Figure 6. This setup allows longitudinal seat adjustment, as well as setting of back rest and head rest position. The sensors are based on OpenCapSense, an open source prototyping toolkit [7]. Each kit is able to interface eight sensors. Thus, two are used and connected to each other by CAN bus. The boards are connected to a nearby PC using USB as virtual COM port. The senors are configured to acquire a new sample every 50 ms.
Synchronization and Compensating Temperature Drift
Two additional aspects had to be considered. If multiple prototyping boards are used, there may be interference between the sensors. Loading mode sensors have distinct charge and recharge cycles. If adjacent sensors are charging and measuring at the same time, the result may be skewed. The basic firmware is managing this for a single board, however the second board may interfere. Therefore, a simple protocol was implemented that sends synchronization signals over CAN, so measurements are performed sequentially. The second adaptation is required if the driver is close to the electrodes in CapSeat. Typically, there is a difference between body temperature and the initial temperature of the electrode. Accordingly, the electrode will warm up, which changes the initial capacitance. This effect can be seen in Figure 5. At first breathing and yawning result in low values. The absolute values increase over time, caused by the electrode warming up from room temperature. This temperature drift requires a compensation, until body and electrode have reached equal temperatures. Experiments show that this behavior follows expectations from physical heat transfer and creates a logarithmic function. Due to the heterogeneity of the electrodes and sensors, the parameters of the logarithmic function y = A · ln(x) + B have to be calculated individually. If the seat is occupied and the person is sitting upright, we use 20 samples from each sensor and build a regression that estimates the change in baseline for each sensor. This is directly used to adjust the normalized sensor values, unless certain criteria are met (e.g. too much movement or a person leaving the seat). EVALUATION
In order to evaluate CapSeat, we have conducted a study, that covers all aspects presented in this paper. As this testing required a considerable amount of time for each subject, the number of participants was limited to six persons (2 female,
Table 2. Classification results
Property
# samples
Rate
Occupancy
2522
100.00%
Driver posture
2682
95.26%
Back rest adjustment
885
99.80%
Head rest adjustment
410
94.15%
Head rest (modified features)
410
100.00%
Steering velocity
2596
97.50%
Yawn detection
103
100.00%
Nod detection
372
95.20%
Gaze detection
1604
99.69%
Longitudinal adjustment
1732
100.00%
4 male, median age 27). However, they were chosen, to cove the 5th , 50th , and 90th percentile of average person’s anthropometry [8]. Study Design
The study was comprised of four parts. In a questionnaire we inquired about general information about the participants. Three of six owned a car, with five having a driver’s license. Only one person had very high experience with Advanced Driver Assistance Systems, while most users knew about capacitive proximity sensors (as they were mostly recruited from our research group). The next step was the measurement of the body parameters. The clothed weight ranges from 64 to 100 kg, body size from 168 to 194 cm, torso length from 89 to 95 cm, thigh length from 59 to 67 cm, and shin length from 52 to 64 cm. The third part was testing the different functions of CapSeat. A small software solution was created that aids in data collection and shows icons, indicating the current step in the study and what the participants were supposed to do next. The final step was another questionnaire after testing CapSeat. The participants agreed that it supports the idea of an Advanced Driver Assistance System (Likert disagree 1, agree 11, µ = 10.00, σ = 0.89) and would consider using it in the future (Likert disagree 1, agree 11, µ = 10.00, σ = 1, 55). They consider privacy in cars to be important, with respect to using camera-based systems (Likert privacy important 1, not important 11, µ = 5.00, σ = 2.68).
Figure 7. Sensor values associated to knee angles of all participants, including logarithmic regression for person A.
The lower precision of the driver posture detection occurred on a single participant. His posture was recognized correctly in only 76% of the samples. All others achieved 100% accuracy. The recognition rate of the head rest adjustment was fairly low. Our initial assumption, to just use the three head rest sensors, proved insufficient. In order to control for differences in posture, we decided to add the upper back rest sensor values into the feature vector. This modification resulted in a 100.00% recognition rate. The least reliable aspect of the drowsiness detection is the recognition of nods with 95.2%. The time window chosen is 0.36 s for detecting a nod. Some of the movements turned out to be slower. This could be improved by increasing the window size. Other parts of the drowsiness detection worked reasonably well, with detection rates between 97.5% and 100%. Finally, we want to discuss the results of the knee angle detection for longitudinal seat adjustment. Figure 7 shows the measurements for different knee angles of all study participants. The determined logarithmic regression function and associated coefficient of determination (R2 ) for person A are also shown. The method works reasonably well, resulting in mostly stable regressions. The determined variables for all users are noted in Table 3. In addition, we also trained a multiclass SVM to distinguish three classes: correct knee angle, knee angle too low, and knee angle too high. This resulted in a recognition rate of 100%, as shown in italic in Table 2. Both varieties of the method are suitable to assist in proper longitudinal seating adjustment. DISCUSSION
Quantitative Results
The collected data was used to train the different SVMs. It was separated into 80% training samples and 20% testing samples. Table 2 shows the results of the different classification processes. The recognition rate can be considered satisfactory, given the number of samples and the variety of our test group. In order to simulate foreign objects for the occupancy detection, the participants were asked to place a hand anywhere on the seat. The back rest adjustment led to a correct classification for 99.80% of all recordings.
Handling the data of capacitive proximity sensors in actual, dynamic applications is challenging. In addition to initial calibration, we also have to apply compensation methods for environmental factors, such as temperature changes. The methods of CapSeat can be distinguished into two different groups - discrete classifiers and non-linear regression. Our evaluation shows that both methods work for capacitive proximity sensors, whereas each has specific advantages and limitations. The classification, using binary or multiclass SVMs, has high recognition rates, even for challenging multiclass issues, such
Table 3. Logarithmic regression variables of knee angle detection
Person
A
B
R2
A
-10.30
64.80
0.99
B
-12.54
65.64
1.00
C
-21.36
72.35
0.94
D
-14.42
79.21
0.96
E
-13.21
77.59
0.94
F
-15.43
80.36
0.97
as gaze tracking. It is important to select suitable features. A combination of normalized values and their derivative often leads to good results. For periodic events, we select features from the frequency domain. However, the number of distinct classes has to be kept reasonably low. The change in environmental properties and heterogeneity in electrode sizes makes it impossible to statically associate sensor values to physical parameters. The chosen solution is non-linear regression to find the variables during use. The deviation of estimated and actual property is fairly low, as indicated by a coefficient of determination (R2 ) close to 1. However, for some test persons the regression did only work in a certain range of sensor values. We observed this for either light or heavy persons. In our setup, the pressure on the electrodes has a non-linear influence on the sensor readings. However, weight can be easily distinguished using CapSeat. Therefore, it is possible to switch between methods, if a certain body type is recognized. Even for the challenging users, a multiclass classifier was able to achieve high recognition rates - at the trade-off of lower precision. Overall, we are satisfied with the recognition rate that was achieved. In some cases, the initial set of selected features had to be adjusted. Thus, the recognition rate for our measurements is between 95% for driver posture, and 100% for four of nine properties. To further optimize this result, it can be considered to use a machine learning method, to find the best combination of features. There are several limitations of CapSeat. The prototypical version adds about e500 to the cost of a seat. Multiple OpenCapSense boards and the cabling are expensive. This can be mitigated using scaling effects and specifically tailored sensors. The influence of environmental effects on the sensor readings is another challenge. The method used for compensating temperature drift was sufficient in our study. It is uncertain if it works as well in real-life conditions. Additionally, foreign conductive objects can change the outcome of a prelearned system. Dynamic data processing methods are crucial for real-life application. Finally, even though the participants of the study were selected carefully, its scope was still limited. The body types in the study covered various sizes and weights, but did not consider persons with high BMI. The influence on level and variety of sensor values are likely to disturb the classification. A different layout, or specifically trained classifiers might be
required. Particularly the dynamic events will be more difficult to detect in a non-lab setting. The dynamics of driving might prevent distinguishing yawning and nodding from normal behavior. CONCLUSION & FUTURE WORK
In this work we have presented CapSeat, a sensing system for car seats, based on sixteen capacitive proximity sensors using heterogeneous electrodes, that can be invisibly integrated in different areas of the seat. We have presented a variety of different processing methods, based on machine learning classification or non-linear regression, to detect different properties of the driver, ranging from static postures to dynamic events, including yawning. The measured properties can be used to inform an Advanced Driver Assistance System that aids in safe and comfortable seat adjustment, by checking position of the seating, back rest and head rest. Additionally, a driver fatigue estimation system can be supported, by checking steering velocity, yawn detection, nod detection, and testing the occurence and direction of gaze. It should be noted that neither of those parameters itself is sufficient for detecting fatigue, but only should be used as support. The system has been integrated into a typical car seat on a test system that allows easy measurement of seat and body properties. A method to adjust for changes in electrode temperature has been developed, to improve results during testing for prolonged periods. A comprehensive study with six users of different body types has been performed. We managed to achieve classification precision between 95% and 100% for our test group. Non-linear regression methods worked reasonably well, but are potentially less suited for persons with specific body types. We see two major areas to improve CapSeat in the future. The first is a fusion with other sensor devices that already contribute to an Assistance System. Parameters, including steering angle, or pedal position can improve the classification, or recognize situations that can’t be detected by CapSeat alone. CapSeat could also be extended by other capacitive Sensors distributed inside the ca. Sensors underneath the foot mat or mounted directly into the pedals could provide more accurate lower limbs positioning. Since our prototype uses only manual adjustments an electrically adjustable car seat could be combined to perform the driver measurement and fitting automatically. The second area is extending the processing of the system to detect different properties. An interest, particularly in highly automated cars, is driver interruptibility that identifies periods in time, when drivers can be safely disturbed. Wearable sensors can detect those times with good precision [11]. It would be interesting to test if CapSeat can achieve comparable detection rates. A context aware classification system could help to minimize classification errors. The exploratory study of this work has to be extended to cover more body types and a higher number of users. We want to identify a suitable set of training data that allows us to reach comparable precision on a pre-trained system. We would like to install a CapSeat system in a car or a realistic simulator, to
test our methods during actual driving situations. Finally, a test set of several seat types and better integrated electrodes should create an even more robust and cost efficient system. REFERENCES
1. Andreas Braun and Henning Heggen. 2012. Context recognition using capacitive sensor arrays in beds. In Proceedings AAL-Kongress.
Factors in Driver Assessment, Training and Vehicle Design. 118–124. 13. Gyouhyung Kyung and Maury A Nussbaum. 2008. Driver sitting comfort and discomfort (part II): Relationships with and prediction from interface pressure. International Journal of Industrial Ergonomics 38, 5 (2008), 526–538.
2. Andreas Braun, Stephan Neumann, S¨onke Schmidt, Reiner Wichert, and Arjan Kuijper. 2014. Towards interactive car interiors - the Active Armrest. In Proceedings NordiCHI. 911–914.
14. Li Li, Klaudius Werber, Carlos F Calvillo, Khac Dong Dinh, Ander Guarde, and Andreas K¨onig. 2014. Multi-sensor soft-computing system for driver drowsiness detection. In Soft Computing in Industrial Applications. Springer, 129–140.
3. Andreas Braun, Reiner Wichert, Arjan Kuijper, and Dieter W Fellner. 2015. Capacitive proximity sensing in smart environments. Journal of Ambient Intelligence and Smart Environments 7, 4 (2015), 1–28.
15. Stephan Lorenz. 2011. Assistenzsystem zur Optimierung des Sitzkomforts im Fahrzeug. Ph.D. dissertation. Technische Universit¨at M¨unchen.
4. Jingyuan Cheng, Oliver Amft, and Paul Lukowicz. 2010. Active capacitive sensing: Exploring a new wearable sensing modality for activity recognition. In IEEE Pervasive Computing. Springer, 319–336.
16. Plessey Semiconductors. 2012. Application Note # 291566 Non-contact ECG measurement using EPIC. Technical Report. 17. J Mark Porter and Diane E Gyi. 1998. Exploring the optimum posture for driver comfort. International Journal of Vehicle Design 19, 3 (1998), 255–266.
5. Christoph Endres, Tim Schwartz, and Christian A. M¨uller. 2011. Geremin”: 2D microgestures for drivers based on electric field sensing. In Proceedings of the 15th international conference on Intelligent user interfaces - IUI ’11. 327.
18. Andreas Riener. 2009. Sensor-Actuator Supported Implicit Interaction in Driver Assistance Systems. Ph.D. dissertation. Johannes Kepler University.
6. B. George, H. Zangl, T. Bretterklieber, and G. Brasseur. 2008. A Novel Seat Occupancy Detection System based on Capacitive Sensing. In Instrumentation and Measurement Technology Conference Proceedings, 2008. IMTC 2008. IEEE. 1515–1519.
19. Andreas Riener, Alois Ferscha, and Mohamed Aly. 2009. Heart on the Road: HRV Analysis for Monitoring a Driver’s Affective State. In Proceedings of the 1st International Conference on Automotive User Interfaces and Interactive Vehicular Applications. 99–106.
7. Tobias Grosse-Puppendahl, Yannick Berghoefer, Andreas Braun, Raphael Wimmer, and Arjan Kuijper. 2013. OpenCapSense: A rapid prototyping toolkit for pervasive interaction using capacitive sensing. In Proceedings PerCom 2013. 152–159.
20. Silvia Rus, Tobias Grosse-Puppendahl, and Arjan Kuijper. 2014. Recognition of Bed Postures Using Mutual Capacitance Sensing. In Ambient Intelligence. Springer, 51–66.
8. Ronald Huston. 2008. Principles of biomechanics. CRC press, Boca Raton, Florida. 9. Insurance Institute for Highway Safety. 2013. Fatality Facts 2013. (2013). http://www.iihs.org/iihs/ topics/t/general-statistics/fatalityfacts/ state-by-state-overview
10. K. Jinno, S. Sekido, and P.H. Rittmueller. 1999. Vehicle passenger sensing system and method. (Sept. 7 1999). US Patent 5,948,031.
21. Arun Sahayadhas, Kenneth Sundaraj, and Murugappan Murugappan. 2012. Detecting driver drowsiness based on sensors: a review. Sensors 12, 12 (2012), 16937–16953. 22. Adella Santos, Nancy McGuckin, Hikari Yukiko Nakamoto, Danielle Gray, and Susan Liss. 2011. Summary of travel trends: 2009 national household travel survey. Technical Report. 23. Joshua R. Smith. 1999. Electric field imaging. Ph.D. dissertation. Massachusetts Institute of Technology.
11. SeungJun Kim, Jaemie Chun, and Anind K Dey. 2015. Sensors Know When to Interrupt You in the Car: Detecting Driver Interruptibility Through Monitoring of Peripheral Interactions. In CHI 2015.
24. Raphael Wimmer, Matthias Kranz, Sebastian Boring, and Albrecht Schmidt. 2007. A capacitive sensing toolkit for pervasive activity detection and recognition. In Proceedings PerCom 2007. 171–180.
12. Jarek Krajewski, David Sommer, Udo Trutschel, Dave Edwards, and Martin Golz. 2009. Steering wheel behavior based estimation of fatigue. In Proceedings of the Fifth International Driving Symposium on Human
25. Thomas G. Zimmerman, Joshua R. Smith, Joseph A. Paradiso, David Allport, and Neil Gershenfeld. 1995. Applying electric field sensing to human-computer interfaces. In Proceedings CHI 95. 280–287.
