1. Leveraging Wearables for Steering and Driver Tracking
Cagdas Karatas∗ , Luyang Liu∗ , Hongyu Li∗ , Jian Liu† , Yan Wang‡ , Sheng Tan§ , Jie Yang § , Yingying Chen† , Marco Gruteser∗ , Richard Martin∗
∗WINLAB
Rutgers University, NJ
{cagdas, luyang, hongyuli, gruteser,rmartin}
@winlab.rutgers.edu
†Stevens Institute of Technology, NJ, {jliu28,yingying.chen}
@stevens.edu
‡Binghamton University, NY,
§Florida State University, FL, USA

2. Motivation Mobile and Wearable devices are often viewed as distraction
Can they compensate for the distraction by enabling safety applications?
Steering is one of the most important inputs of the driver
Can wrist-mounted wearables and mobile devices track steering wheel usage?
A driver using both hands to text while driving*
Steering usage and angle
from smartwatch
Although mobile and wearable devices is often viewed as one source of distraction while driving. Is there anything they can do to compensate that? Can they also be used to enable safety applications?
Especially, can smartwatches track steering wheel usage, since it is the most important input of the driver
Also smartphones can track the vehicle
Tracking these could enable novel safety applications especially for old or cheap cars which are not accessorized with expensive safety systems

3. Safety Applications Detecting Understeer and Oversteer
Compare car’s motion with steering wheel angle during turns
Curve Speed Warning
Crowd-sourcing by using historical safe turning data sets
Car Maintenance
Compare car’s motion with steering wheel angle continuously.
A constant offset when vehicle needs alignment.
Lane Change Detection
Compare steering wheel angle with map
If we had steering wheel usage and the steering wheel angle which is our ultimate output, we could compare it with car’s movement during the turns and we could detect understeer and oversteer events which lead the vehicle to slip
Since we have these slip events we could Crowd-source this to warn the drivers for safe turn speed as they approach to dangerous curve
Furthermore by comparing steering wheel angle with car’s motion not only during the turns but continuously, we could know when the car needs alignment which usually happens when there is a constant offset between these two.
Finally, A lane change detection system can be implemented by comparing the steering wheel angle with road angle obtained from online map
Lane change detection can be further used for impaired driving detection
The implementation of safety applications are beyond the scope of this paper and we focus on how to detect steering wheel usage

4. Activity Tracking with wearables
Existing solutions
Inertial sensor based
(Dai et al)
Steering Wheel Based
Lane change detection [17]
Drowsy driving[21]
Computer vision based
Lane change assistance[12,13,19]
Safe following distance
Drowsy driving[20 & 19]
Behavior Detection
Activity Tracking with wearables
Smoking detection and other activities
Not specific to driving
(Raif et al)
Before getting in details I would like to introduce existing solutions.
There are many computer vision based driver behavior apps and studies. However as we all know, computer vision techniques are computationally intensive
Some studies placed sensors on the steering wheel to track the driver, however these systems rely on steering wheel angle which is not readily available in mobile devices.
The third body of related work utilize inertial sensors and which is closer to our approach, however, they only track the vehicle not the driver.
Finally Activity tracking community has been utilizing wearables to track human movements. However, they are designed for casual daily events and they are not targeted to driving scenario which is a quite complex environment.
In contrast to these work, Our system doesn’t require resource intensive methods,
It doesn’t require any additional sensors
And it is specifically designed for this complex environment

5. Approach Overview Is the user driver or passenger ?
Is the phone in the driver or passenger area [1]-[3]
Are the hands on the steering wheel?
Need to track hand movements
What is the steering wheel turning angle?
Only proprietary interfaces to the vehicle CAN bus
-> Circular arm movements
-> Linear arm movements
-> Wrist rotation
As I said before we want to track steering wheel usage, specifically steering wheel angle.
To track the steering wheel angle from the smartwatch we first need to know whether the smartwatch user is the driver.
And also we need to know whether the driver’s hands are on the steering wheel
We detect the circular arm movements during the turns to determine whether the user is the driver or passenger
We detect when the driver has taken his hand off the steering wheel or put them back to the steering wheel by utilizing linear arm movements
Finally we use wrist rotation to estimate the steering wheel angle
The first part is our previous work, that’s why I’m not going to get in to the details in this presentation

6. System Design Driver/Passenger Classification
Does the user rotate his hand when the car is making a turn? Yes => Driver!
98.9% accuracy. Details in previous paper.
Again we first detect whether the user is the driver or the passenger and after we know he is the driver we detect whether his hands are on the steering wheel or off the steering wheel. Finally if we know his hands are on the steering wheel, we estimate the steering wheel angle.
And for this purpose, we use inertial sensors on the smartphone and the smartwatch. Let’s have a look at the modules individually
Coordinate Alignment
Since the smartwatch and the smartphone have different rotations and different coordinate frames, in order to use their data we first need to represent these data to a common coordinate frame. For this purpose we use coordinate alignment
Then we look for circular arm movements during the turns. So if the driver is making circular arm movements when the car is making a turn : we classify him as the driver.
Again, these two modules are from our previous work so I won’t get into details of them. However, The driver/passenger classification module achieved 98.9% accuracy in our experiments.

7. Hand on/off Steering Wheel Detection
Hand movement detection
Wrist Acceleration -> Eliminate car’s movement -> Find Peaks -> Cluster Peaks
Linear direction estimation
General scenario Additional rule
To detect when driver’s hand is leaving the steering wheel and when it is coming back to the steering wheel, we first detect every hand movement and estimate their direction and finally match them to determine whether an up hand movement is actually leaving the steering wheel or returning back to the steering wheel.
In order to detect hand movements we use wrist acceleration, we eliminate the car’s movement by using coordinate alignment and simply subtracting smartphone’s acceleration.
Linear direction estimation is quite straight-forward. When the hand moves, it first accelerates then it deaccelerates. So the direction of the first peak actually the direction of the hand movement. This is the general scenario however for only hand movements we observed that sometimes there may not be a positive peak. So we defined an additional rule : if there is no up positive peak, then it is an up hand movement. We think this might happen because we are not able to eliminate gravity perfectly.
Finally we need to match the hand movements to determine whether the driver’s hand is on the steering wheel or not. For example in this figure purple down movement can match with the up movement on the right and we might think the drivers hand is on the steering wheel afterwards, however if it matches with the up movement on the left we would think driver’s hands are off the steering wheel after 15th hand movement.
To match them we simply compare the magnitudes of the rotation angles for two hand movements.
!!!!
Rotation based matching
Ideally we should be able to match the movements with their linear directions however if our system detects an movement by mistake which can be because of a bump on the road, this can ruin all
Matching sequence. For this reason, we match hand movements by using rotation. The idea is simple magnitude of rotation of the watch w.r.t smartphone will be a continuous movement when the hand is on the steering wheel
And it will be more stable when the hand is off the steering wheel so on the graph is actually up/down pair , not we use variance filter

8. Steering Wheel Angle Estimation
How to find the wrist rotation?
Coordinate Alignment
Remove vehicle’s angular velocity
Integrate
Why do we need Profile?
Wrist Angle ≠ Steering Wheel angle
How to construct the profile?
Offline data, Linear Regression
To estimate the steering wheel angle we use the use the wrist rotation
Estimating wrist rotation is quite straight forward, we first use the coordinate alignment and remove the vehicle’s angular velocity by simply subtracting smartphone’s gyroscope data.
Then we integrate the remaining angular velocity to find wrist rotation
Although wrist rotation is quite related to steering wheel angle it is not directly equal to it, therefore we need a profile to match the wrist rotation angle to steering wheel angle
To construct we profile we use linear regression with offline data. Then during the experiments we use the gyroscope data estimate the wrist rotation and then plug it to offline constructed profile to find the steering wheel angle
!!!!
When the driver rotates the steering wheel the smart watch also experiences a wrist rotation. The steering wheel angle can be inferred from the wrist rotation of the smartwatch, particularly from the gyroscope readings
We again use the coordinate alignment to work on a common coordinate frame and we remove the car’s motion from the smartwatch readings in data calibration and integration step
Then wrist rotations can be used in angle estimation to infer the steering wheel angle based on the constructed driver rotation profile in Profile construction
Data calibration simply subtract vehicle’s angular velocity from the smartwatch after coordinate alignment. Integration wrist rotation angle
we use a supervised learning method to create a driver rotation profile. We use offline data to create a mapping : liner regression to fit the smart watch rotation to the steering wheel angle. We experience with different models, such as linear, quadratic and cubic fits, and find that the linear fit is simple yet effective.

9. Experiment Setup Invensense MPU-9150 9-axis motion sensors @50hz
On wrist
On steering wheel : θreal , ground truth
Smartphone
GoPro camera
Honda Civic , Toyota Camry
Steering wheel angle evaluation
( 20 trips)
Highway : smooth curves, small angles
Local Road : sharp turns, large angles
Hand on/off evaluation
Extra route : frequent hand movements intentionally
Of course you wonder about the experiments and the results In our experiments we used two 9axis motion 50hz one on the wrist and the other one on the steering wheel for the steering wheel angle ground truth.
We also had a smartphone in the vehicle and one camera for hand on/off ground truth and we tested in two different cars : honda civic and Toyota camry.
We had two sets of experiments. For the steering wheel angle estimation we had 20 trips both in local and highway. We used different roads because they have different curve types and therefore different turn angles
For the hand on/off evaluation we had an additional route where we could introduce frequent hand movements intentionally and safely.

10. Performance Evaluation
Steering Wheel Angle Estimation Evaluation
Steering wheel angle estimation accuracy for different angles
How much accuracy does this correspond to in understeer/oversteer scenario?
Steering wheel angle
109 turns between 120 degrees right turn to 90 degrees left turn
25 turns < 30 degrees,
43 turns 30 to 60 degrees
41 turns > 60 degrees.
When rotation is less than 30 degrees std is 2.21 degrees 99% accuracy for detecting 10degrees slipping (or 90% accuracy for 5 degrees slipping)
Rotation is between degrees std is 3.82 degrees 99.5% accuracy for 20 degrees ( or 90% accuracy for 10degrees)
Larger than 60 degrees, 6.12 degrees std 95% accuracy for 20 degree slipping

11. Performance Evaluation
Hand On/Off Steering Wheel Detection Evaluation
In terms of duration : the system achieves 99.9% true positive rate and 89.2% true negative rate.
In terms of hands on/off events :
>91.5
>91.5
=89.2
>81.5
for different types of hand movements. In the experiments, we tested three different typical hands-off events of drivers, namely hand on leg, hand on the armrest and adjusting sun visor
for both hand on leg and hand on armrest events, the true negative rate remains above 91.5% and false negative rate is less than 8.5%.
for reaching for the sun visor event, the performance decreases to as low as 81.5%. This is mainly because reaching the sun visor has a smaller moving duration and continuous moving process than that of other two events. Thus, it is relative harder to match the hand movements to the departure movement and returning movement.
<19
Positives : Hands on the wheel
Negatives : Hands off the wheel
<8.5
<8.5
Hand on/off performance for different movement types

12. Discussion & Conclusion
Exploiting using wrist-worn wearable devices to enable activity recognition of unsafe driving
hand on/off steering wheel detection
steering wheel angle estimation
Examined whether wearables could achieve sufficient accuracy for building driving safety applications. Experiments showed that ;
Hand on/off the steering wheel detection : true positive rate around 99%, true negative rate above 80%
Steering angle estimation with errors less than 3.4 degrees
Future work
Search for a more general profile or profile construction mechanism that can cover more people and different driving styles

14. References
[1] Y. Wang, J. Yang, H. Liu, Y. Chen, M. Gruteser, and R. P. Martin, “Sensing vehicle dynamics for determining driver phone use,” in ACM MobiSys, 2013, pp. 41–54. [2] Y. Wang, Y. Chen, J. Yang, M. Gruteser, R. Martin, H. Liu, L. Liu, and C. Karatas, “Determining driver phone use by exploiting smartphone integrated sensors,” IEEE TMC, [3] H. L. Chu, V. Raman, J. Shen, A. Kansal, V. Bahl, and R. R. Choudhury, “I am a smartphone and i know my user is driving.” in COMSNETS, 2014, pp. 1–8. [16] C. Bo, X. Jian, X.-Y. Li, X. Mao, Y. Wang, and F. Li, “You’re driving and texting: detecting drivers using personal smart phones by leveraging inertial sensors,” in MobiCom 2013.
*CC BY-SA 3.0

15. Leveraging Wearables for Steering and Driver Tracking
Cagdas Karatas∗ , Luyang Liu∗ , Hongyu Li∗ , Jian Liu† , Yan Wang‡ , Sheng Tan§ , Jie Yang § , Yingying Chen† , Marco Gruteser∗ , Richard Martin∗
DO NOT FORGET TO CHANGE POINTER TYPE
∗WINLAB
Rutgers University, NJ
{cagdas, luyang, hongyuli, gruteser,rmartin}
@winlab.rutgers.edu
†Stevens Institute of Technology, NJ, {jliu28,yingying.chen}
@stevens.edu
‡Binghamton University, NY,
§Florida State University, FL, USA

16. Motivation Mobile and Wearable devices are often viewed as distraction
Steering and driver tracking can also enable safety applications
Mobile devices can track vehicle motions
Wrist-mounted inertial sensors can track steering wheel usage and angle.
A sign in Texas
A driver using both hands to text while driving*
Steering usage and angle
from smartwatch
While the emerging ecosystem of mobile and wearable devices is often viewed as a distraction that can lead to accidents however they can be used for the driving safety applications
By tracking the driver and vehicle.
Particularly, we study how wristmounted inertial sensors, such as those in smart watches and fitness trackers, can track steering wheel usage and angle
Such capability would enable novel classes of mobile safety applications without relying on information or sensors in the vehicle.
Although the implementation of safety applications are beyond the scope of this paper, we are also going to mention about the design of a couple of sample applications

17. Challenges Limited availability of sensors
Require utilizing complex physical models
Ambiguity of the source
Is the user driver or passenger?
Positioning of the mobile device
Orientation of vectors depends on pose of device
Dynamic environment
Activity recognition methods cannot be directly applied to a moving vehicle scenario.
Noise
Noise accumulates when integrated
There are many challenges to enable safety applications by using the wearables and the mobile devices

18. Goal : Enabling Safety Applications
Is the user driver or passenger ?
Is the phone in the driver or passenger area [1]-[3]
Are the hands on the steering wheel?
Need to track hand movements
What is the steering wheel turning angle?
Only proprietary interfaces to the vehicle CAN bus

19. Activity Tracking with wearables
Existing solutions
Behavior Detection
Steering Wheel Based
Phone positioning
Acoustic Sensing
(Yang et al)
Computer vision based
Lane change assistance[12,13,19]
Safe following distance
Drowsy driving[20 & 19]
Acceleration
Centripetal
(Wang et al)
Lane change detection [17]
Drowsy driving[21]
Activity Tracking with wearables
Smoking detection and other activities
Not specific to driving
Inertial sensor based
(Dai et al)
Specific Movements
[3&16]
(Raif et al)

20. APPROACH OVERVIEW
Wrist-worn wearable devices are used for fine-grained tracking driving behaviors, smartphone for vehicle tracking. No built-in devices
Circular arm movements during turns to detect driver/passenger classification
Linear arm movements of the driver to detect hand on/off steering wheel
Steering wheel angle estimation by leveraging wrist rotation

21. APPLICATION SCENARIOS
Detecting Understeer and oversteer
Real steering wheel angle (θreal)
Estimated steering wheel angle from wearable (θest)
Expected steering wheel angle (θexp) can be calculated from car’s motion via smartphone
Ackerman equation & circular motion laws.
a, ω, d, and L represent centripetal acceleration, angular velocity, width, and length of the car, respectively. k is a constant.
Expected steering wheel angle (θexp) which is the steering wheel angle that can be calculated based on the car’s movement.
When there is no understeer or oversteer, the expected and real steering wheel angles should be ideally equal since the car moves as directed by the steering wheel.
Understeer/oversteer can be detected when | θest - θexp| > θth during turn

22. APPLICATION SCENARIOS
Curve Speed Warning
Crowd-sourcing by using historical safe turning data sets
Car maintenance
Compare θest and θexp continuously.
A constant offset when vehicle needs alignment.
Lane Change detection :
Compare θest with online maps for road angle
Gesture-based input system :
Rotate hand on the steering wheel without actually moving it.
It can enable drivers to change the radio volume without moving their hand off the steering wheel.
Since we have understeer and oversteer events we Crowd-sourcing by using historical safe turning data sets
(without understeer/oversteer) with road conditions and vehicle characteristics
Lane change detection can be used for impaired driving and dangerous driving habit detection systems

23. Accuracy Requirement of understeer/oversteer detection
we can achieve steering wheel angle estimation accuracy how much minimums on classification performance for slip detection we can provide
We carred out calculations for oversteer scenario but the method we use here can be also used for lane change and understeer
If we have this much standard deviation in our wheel angle estimation what is the minimum slip detection angle (MSDA) we can detect and what will be the error rate
Equal Error Rate which is the error rate of the system when the probability of false positives, false oversteer detections, and that of false negatives, missed oversteers, are equal. So we need the to define false positives and false negatuves
For the false positives, we need to consider the case where the car does not slip. The real steering wheel angle and the expected steering will be equal and have the value of θreal.
The estimated steering wheel angle can be modeled as a normal distribution with mean θreal and σsteer standard deviation
The σsteer is the estimated steering wheel angle requirement that the estimation algorithm needs to achieve the aforementioned guarantees.
Any θest angle that is greater than θreal + θth will result in false positives.
For the false negatives, we need to consider the case when the car’s slipping causes θslipping degrees of difference between the real steering wheel angle and the expected steering wheel angle. The estimated the steering wheel angle can still be modeled with normal distribution The false negatives will occur for the values of the estimated steering wheel angle greater than θreal + θslipping − θth
θreal : Real steering wheel angle
θest : Our estimation from wearable
θexp : Expected steering wheel angle
from car’s motion

24. SYSTEM DESIGN Driver/Passenger Classification
Basic idea :Wristmounted inertial sensors capture hand movements and wrist rotation of the driver for tracking steering wheel usage
Coordinate Alignment
The wearables and mobile devices are in different rotations so the inertial data collected from them needs to be taken to a common coordinate frame
Android API provides a rotation vector to transform sensor measurements from sensor coordinate frame to world coordinate frame.
We use this to transform vectors between sensor coordinate frames
D/P Classification
This is a technique we introduced in our paper basically ti checks whether the users arm is rotating while the vehicle is making a turn. We detect the turns from smartphone’s inertial sensors. 98.9% accuracy
Hand on/off Steering wheel
By identifying when the driver’s hand is moving away from and moving back to steering wheel based on the captured hand movements
We further estimate turning angle of the steering wheel based on wrist rotation caused by movement of rotating steering wheel
The output of the system can be used as input to build a broad range of driving safety applications

25. Hand on/off Steering Wheel Detection
Hand movement detection
Eliminate vehicle’s motion after coordinate alignment
Find Peaks
Cluster Peaks
Linear direction estimation
General scenario
Additional rule
When the driver’s hand leaves the steering wheel there needs to be two movements : Departure movement and Returning Movement
for each movement the hand moves in opposite linear and rotational directions
We first capture all driver’s hand movements , then estimate the Linear and rotational directions of hand movements
Then we match them as departure and returning movement
Movement detection
However there are some challenges we need to deal with, first we need to eliminate the effect of vehicle’s motion from the drivers wrist sensor reading
We do this by subtracting car’s acceleration and gyroscope readings from the wearables readings after coordinate alignment, we also apply LPF to eliminate the noise
Our experiments showed that gyroscope is more reliable for hand movement detection than that of acceleration. So get the peaks of gyro magnitude and we cluster the peaks (since a series of peaks can be related to a single continuous movement
Linear direction estimation
Real quick scenario : when hand moves in a direction, there is a speed up and speed down process. So by examing the sign and magnitude if the acceleration, we can find identify the moving direction
In first graph we have 4 pairs of hand up/down movements,
we can see that rule applies for the most of the movements, it sometimes not that clear for this case movement 1,5,7
Additional rule if max of acceleration is less than 0.01, we take it as up movement (This might be because we are not able to remove gravity fully)
Rotation based matching
Ideally we should be able to match the movements with their linear directions however if our system detects an movement by mistake which can be because of a bump on the road, this can ruin all
Matching sequence. For this reason, we match hand movements by using rotation. The idea is simple magnitude of rotation of the watch w.r.t smartphone will be a continuous movement when the hand is on the steering wheel
And it will be more stable when the hand is off the steering wheel so on the graph is actually up/down pair , not we use variance filter

26. Data Calibration and Integration
Steering Wheel Angle Estimation
Data Calibration and Integration
Remove car’s motion
Integrate the angular velocity = θwrist
Profile Construction
θwrist < θreal
Due to hand and watch bend
Use supervised learning method to create a profile
Angle Estimation
Plug the θwrist to the profile
When the driver rotates the steering wheel the smart watch also experiences a wrist rotation. The steering wheel angle can be inferred from the wrist rotation of the smartwatch, particularly from the gyroscope readings
We again use the coordinate alignment to work on a common coordinate frame and we remove the car’s motion from the smartwatch readings in data calibration and integration step
Then wrist rotations can be used in angle estimation to infer the steering wheel angle based on the constructed driver rotation profile in Profile construction
Data calibration when the car is making a turn smartwatch gyroscope will measure a combination of the angular velocities of the vehicle and the steering wheel. We can measure the vehicle’s angular velocity from the smartphone and simply subtract it from the smartwatch after coordinate alignment. The remaining is solely due to steering wheel rotation and by integrating this we can find the steering wheel rotation angle theta wrist
We ideally expect the theta wrist to be equal to theta real however theta wrist will be usually less than theta real because the driver bends his wrist in different ways when holding the steering wheel as in Figure 7

27. PERFORMANCE EVALUATION
Experiment Setup
Invensense MPU axis motion
on wrist : for θwrist
on steering wheel : θreal , ground truth
Smartphone for the sensor and collect data
GoPro camera to record hand on/off movements
Two cars : Honda Civic and Toyota Camry
Steering wheel angle ( 20 trips)
Highway : smooth curves, small angles
Local Road : Sharp turns, large angles
Hand on/off
Route c : intentional frequent
hand on /off movements
We used sensor on the steering wheel because it is very hard to use angle finders in dynamic environments when the steering wheel is moving
However in static environments we have showed that it can be used with an error less than 1 degree

28. PERFORMANCE EVALUATION
Hand On/Off Steering Wheel Detection Evaluation
Road types
Hand movement type
Steering Wheel Angle Estimation Evaluation
DO NOT FORGET FIGURE DESCRIPTIONS
Overall, our hand on/off steering wheel detection achieves a 99.9% true positive rate and a 89.2% true negative rate for the normal commute data set, and a 99.3% true positive rate and an 88.4% true negative rate for the frequent hand movement commute data set. By combining the results from these three routes, our method achieves a 99.4% accuracy on detecting hand on steering wheel periods and an 88.8% accuracy on detecting hand off steering wheel periods.
forr different types of hand movements. In the experiments, we tested three different typical hands-off events of drivers, namely hand on leg, hand on the armrest and adjusting sun visor
for both hand on leg and hand on armrest events, the true negative rate remains above 91.5% and false negative rate is less than 8.5%.
for reaching for the sun visor event, the performance decreases to as low as 81.5%. This is mainly because reaching the sun visor has a smaller moving duration and continuous moving process than that of other two events. Thus, it is relative harder to match the hand movements to the departure movement and returning movement.
Steering wheel angle
we collect 109 turns ranging from 120 degrees right turn to 90 degrees left turn in both the highway commute and the local road commute
there are 25 turns with steering wheel rotation less than 30 degrees, 43 turns with steering wheel rotation in between 30 to 60 degrees, and 41 turns with steering wheel rotation larger than 60 degrees.
We use two types of errors, true error and absolute error, to evaluate the performance our steering wheel angle estimation. The true error is defined as the real steering wheel rotation minus the estimated steering wheel angle (i.e., θreal − θest), whereas the absolute error is the magnitude of the difference between the true rotation and the estimated angle (i.e., ||θreal − θest||).
When rotation is less than 30 degrees std is 2.21 degrees 99% accuracy for detecting 10degrees slipping (or 90% accuracy for 5 degrees slipping)
rotation is between degrees std is 3.82 degrees 99.5% accuracy for 20 degrees ( or 90% accuracy for 10degrees)
Larger than 60 degrees, 6.12 degrees std 95% accuracy for 20 degree slipping

29. Absolute Error of Steering wheel angle
Mean error is less than 3.4 degrees for all turns
Larger steering wheel angles cause larger errors and 4.38

30. Discussion & Conclusion
Using wrist-worn wearable devices to enable activity recognition of unsafe driving
hand on/off steering wheel detection
steering wheel angle estimation
Leveraging wearables could achieve sufficient accuracy for building driving safety applications
Hand on/off the steering wheel detection : true positive rate around 99%, true negative rate above 80%
Steering angle estimation with errors less than 3.4 degrees
Open challenges
Driver steers only with the watch-less hand
The profile of wrist rotation with respect to the steering wheel angle for one driver.
Different drivers may have different styles of wearing the device
Paper and ink are not very durable
Air moisture or residue from finger may connect exposed terminals
Potential health implications of long-term exposure to nanoparticle-based inks may need to be further studied

32. Contribution
simplifying the use of conductive ink printing for multi-button custom touch input interfaces that augment objects in our environment
introducing a touch circuit design and a resistive polarity switching touch detection technique that supports up to 20 touch points touch points without changes to the external device’s hardware or its 3 pin interface to the paper circuit.
developing a prototype readout circuit and layout guidance to create custom paper user interfaces.
evaluating the accuracy of touch detection and its dependence on several factors such as skin resistance and size of the touch point.

33. References
[1] Y. Wang, J. Yang, H. Liu, Y. Chen, M. Gruteser, and R. P. Martin, “Sensing vehicle dynamics for determining driver phone use,” in ACM MobiSys, 2013, pp. 41–54. [2] Y. Wang, Y. Chen, J. Yang, M. Gruteser, R. Martin, H. Liu, L. Liu, and C. Karatas, “Determining driver phone use by exploiting smartphone integrated sensors,” IEEE TMC, [3] H. L. Chu, V. Raman, J. Shen, A. Kansal, V. Bahl, and R. R. Choudhury, “I am a smartphone and i know my user is driving.” in COMSNETS, 2014, pp. 1–8. [16] C. Bo, X. Jian, X.-Y. Li, X. Mao, Y. Wang, and F. Li, “You’re driving and texting: detecting drivers using personal smart phones by leveraging inertial sensors,” in MobiCom 2013.
*CC BY-SA 3.0