While communication between vehicle and driver will continue, vehicle-to-vehicle communication will replace the definition of basic communication With carmakers driving innovation deeply, players like Apple, Amazon, and Google will jointly collaborate with OEMs to provide services to the consumer Trends indicate the primary focus of safety will be monitoring and predicting the health of the driver.
Autonomous car will largely allow drivers and passengers to sit in the car safely with less communication, manual control, and operation Entertainment capabilities in the car such as augmented reality and virtual reality will give a premium feel to the driver Between now and , a complete paradigm shift is expected, that will result in: The transposition of the growing level of autonomy versus the adoption of haptics in the complete cockpit of the car.
Automotive controls will move to user devices, as will the UI. Greater demand for immersive experiences, adding entertainment to the value of haptics. Once set, the role that haptics plays in safety and improved user experience will match the required level of driver intervention during a car ride. A virtual cockpit where you are able to control everything from windows, seats to entertainment packages neatly with tactile feedback.
With autonomous cars making travel safer, more viable, connected, and entertaining without the need for manual intervention or data overload, the future holds no barriers. We use cookies on our website to give you the most relevant experience by remembering your preferences and repeat visits. Close Privacy Overview This website uses cookies to improve your experience while you navigate through the website.
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These include Nokia-branded smartwatches, connected scales, blood pressure monitors and other consumer health devices, being shipped to stores around the world. VR equipment is beginning to infiltrate hospitals and health clinics, helping patients to manage pain and anxiety and address phobias and depression.
Early adopters are using VR software at home for therapy, guided meditation, and workouts that feel more like gaming than exercise. As cheap headsets proliferate enterprising researchers are discovering a range of wellness potentials: VR programs to battle PTSD, treat drug and alcohol addiction, and help you bounce back faster from injury… Read more. A controversial lab in Montreal is developing virtual reality images that can help build a profile of a pedophile, and determine their risk to society… Read more.
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Medical education takes leap with virtual reality. Haptic feedback was also used to enhance the realism of a walking experience in multimodal environments [ 27 ].
Haptic feedback has also been used as an important tool in gait retraining for treatment of knee osteoarthritis [ 28 ]. The system and results served as basis for this work by informing the use of haptic feedback to capture and improve gait parameters including knee alignment.
They also highlighted issues whereby users were confused when receiving more than one feedback simultaneously i. Such issues are again validation for why QoE assessments of such feedback mechanisms are required. Some authors have applied Augmented Reality in gait analysis. In [ 29 ], a low-cost gait analysis system was developed using AR markers and a single video camera. The AR markers were used to track body segments and capture gait variables.
Even though the authors achieved calibration and accurate tracking for gait angles, they highlighted the use of markers as a limitation e. The use of different AR devices was also reported for guided walking in [ 30 , 31 ]. These works indicated that novel AR technologies could be used in walking guidance with performance, body stability with positive impact in gaze and locomotor control [ 32 , 33 ].
Considering these works, the use of AR for gait feedback has not been deeply explored. There are some exploratory works that suggest employing AR for gait retraining.
The results reported in [ 34 ] results in significant improvement in gait over a 2D monitor. Other research has reported the use of AR in gait posture training [ 35 ] reported statistically significant improvement in posture, balance, and velocity.
In [ 36 ], a gait retraining system was developed to modify footprint parameters. Although these works make a valuable contribution, there was no qualitative metric employed that informs if users were satisfied or enjoyed the feedback experience. This is critical because it informs designers about how the users enjoy, engage, and experience such systems.
Several authors have used QoE assessment in multimedia systems as a paradigm to quantify how various factors of the system influence perceived quality levels from the user perspective. A sample size of twenty-one participants was divided randomly into two groups. Both objective and subjective metrics were gathered. The authors considered system, psychological, and user factor to evaluate quality. The QoE evaluation suggested that users felt safer and accustomed with the use of AR when compared to virtual reality.
In [ 38 ], a QoE evaluation of a motor skills rehabilitation game was developed. The authors have assessed QoE through user engagement, task success, interaction, and socialization. This study reported that high QoE scores can be linked to high performance.
These works demonstrate the need of a qualitative study for different applications. Anatomical differences between males and females lead to differences in knee alignment, and are a potential cause of anterior cruciate ligament injuries in females [ 41 , 42 ].
The focus is on comparing subjective and objective metrics for correcting knee alignment with these two different feedback modalities Haptic and AR. Our gait system is composed of a capturing module, a presentation module and a data processing module.
The Feedback module contains two components: Haptic and Augmented Reality modules. This streaming protocol is important for it ensures that no data is lost, that feedback is presented without delay, and all modules can work independently.
In terms of internal configuration of each IMU, 10 streams of data were captured: 3D acceleration from triaxial accelerometer Acc xyz , 3D angular velocity from triaxial gyroscope Gyro xyz , 3D magnetic field from a triaxial magnetometer Mag xyz , and UNIX timestamp.
As discussed later in this section, the Acc xyz , Gyro xyz , and Mag xyz were fused to provide quaternion representation.
The developed protocol fuses, in real-time, accelerometer, gyroscope, and magnetometer data and generates the quaternion orientation.
This module, processes in real time, the quaternion and Euler angles of each sensor and generates angles for knees, hips, tibia, and trunk lean. Data from the sensors was sampled at 40Hz on all three axes and sent through a Wi-Fi interface to the server computer.
Further details on the multi-IMU streaming protocol is available in [ 44 , 45 ] for the interested reader. To represent the orientation of a rigid body or frame coordinates in 3D space, a quaternion representation was employed.
This complex number representation defines any spatial rotation around a fixed point or coordinate system. To get the angle between two joints with IMU, quaternion matrices were obtained by fusion of the 3 internal modules Acc xyz , Gyro xyz , Mag xyz using a Madgwick-based orientation filter [ 48 ]. The quaternion generated by the orientation filter represent s the spatial rotation of each IMU and can generate any joint angle knee angle in this case for each axis. Having each Euler angle, it is then possible to reference one IMU to another and determine the angle between two sensors.
To find the tibia projection angle in the frontal, lateral, and sagittal planes, we need to calculate unit vectors on each quaternion coordinate system. This calculation converts the current quaternion of each IMU to direction cosine matrices. We then applied this to the calibrated Euler angles. Finally, we apply trigonometry of right-angle triangle of IMU xyz of the directional vector v xyz on the desired plane Eq 5.
In this section, Haptic and Augmented Reality feedback modules are presented. A bespoke wearable haptic module was designed for gait feedback purposes as illustrated in Fig 3. No off-the-shelf haptic modules satisfied our requirements of being lightweight, wearable, and provide a haptic sensation. Each module was composed of a leg mounted strap; two vibration units Fig 3a ; and communication and micro-controller with battery unit Fig 3b.
It contains haptic motors a and the Wi-Fi microcontroller responsible for the web-socket client b. All the units are sheltered within ABS plastic cases 30x30x10mm for the haptic module and 40x30x10mm for the Wi-Fi micro-controller. The vibration units are enclosed within the plastic casing. There was also a pulse width modulation control to allow precise change of the intensity of the vibration unit if required. A freewheel diode was installed across each motor of the vibration units to remove voltage spikes due inductive nature of the load when switched off [ 50 ].
A WebSocket client in the AR module was employed as it allowed the web server to establish a connection with the feedback application and communicate directly with it without any delay typically web communication consists of a series of requests and responses between the client and the web server, where, for real-time applications, this technique is not well suited [ 52 ].
With the use of WebSockets, we established a connection only once, and the communication between the server and the feedback application could follow without problems related to delay and synchronization. The feedback state diagram is shown in Fig 4.
The user input is compared with the kinematic model which controls the feedback mechanism according to the activation threshold. These values represent normal angle limits of knee alignment [ 53 ].
The model constantly evaluates the current tibia angle in order to compare with threshold values. Each person has their own walking style and for this reason it is difficult for a participant to have perfect alignment throughout every single part of the gait cycle while walking naturally. Because of this, every small change between baseline no feedback and test both feedback was observed during testing.
These diagrams represent the feedback control system. User knee angle is used as input, which will be compared constantly with kinematic model. The user then receives haptic or AR stimuli to correct knee alignment. During the training phase see section IV , participants were told that no feedback from haptic means they are in correct alignment.
The objective given to the participant was to receive the least amount of vibration as possible. The user sees a projection of 6 circles in their field of view 3 of each leg as per Fig 5. Again, whenever the tibial angle was above or below the activation thresholds for valgus and varus.
For each leg, three circles control the states of the knee according to valgus and varus angles. The correct alignment of each leg is achieved when the blue circle in the middle is lit. The objective given to the participant is to keep the circles blue during trial.
AR feedback iscontrolled by colored circles: redfor misalignments and blue for alignment. Haptic controls are vibrations oneach leg: 1 and 4 for Valgus, 2 and 3 for Varus.
Participants consent was obtained in written format and stored in a secure location. Data were anonymized for all trials and participants. After ethics approval, a test with healthy participants was conducted. A convenience sampling approach was employed to recruit twenty-six participants 13 males, 13 females with an average age of Due to previous knee or walk abnormalities, data of two participants was omitted.
The gender balance guidelines have been applied as per ITU-P standards for objective and subjective quality assessment [ 54 ]. A within group experimental design was employed; hence each participant experienced both the haptic and AR feedback modalities. The ordering of how the participants experienced the feedback was randomised. Participants were tested on two different days and the protocol adhered to the approach taken in numerous related works in the literature [ 17 , 37 , 55 ] and included the steps outlined in Fig 6.
This protocol was consisted during all trials for all participants. The full protocol is available in S1 File. During the information phase, each participant was greeted and thanked for their participation. After a brief explanation, written consent was obtained. Participants were brought to the waiting room and were provided with an information sheet that fully described the experiment.
The screening process for participants for visual acuity, color perception, and haptic sensation required participants to achieve a threshold score to be eligible for the actual testing. For the Ishihara test, thirty-eight color plates were used and only 4 errors were allowed during examination.
For the haptic screening, participants were required to differentiate 4 vibration patterns and location [ 58 ]. For this experiment, we only analyzed tibia angle to evaluate feedback.
Full gait analysis considering all angles will be evaluated as part of a future work study. Each participant experienced one of the feedback modalities and had a week break before they were presented with the alternative modality feedback. As part of the training, participants were introduced to the AR and the haptic modules as appropriate for the given test day. The devices were fitted to the participant by the principal investigator and an opportunity for adjustment was provided to ensure there was no discomfort.
After sensor placement, participants were securely guided to a treadmill where they were asked to select a walking speed with which they felt comfortable the range selected by users was between 2. Following this, in the test, the speed each participant selected was maintained for training and testing of both feedback modalities. Instructions for each feedback were explained with 3 feedback sheets available in S1 File showing the difference of the three different knee states valgus, normal, varus.
Participants were aware that each leg was independent so that even though one leg was on valgus state, the other one could be aligned for example. Participants walked 2 minutes for base-line capture no feedback , 30 seconds for feedback training, and 2 minutes with feedback.
As per [ 59 ], twelve questions asked were asked of all participants on the experience of both feedback modalities. For the subjective analysis, QoE factors were evaluated in form of questionnaires after the gait assessment phase as per Fig 6.
The developed questionnaire was used to determine an overall mean opinion score MOS based on feedback from users [ 60 ]. The twelve questions were developed to evaluate system utility questions , usability questions , interaction questions , and immersion questions The rating system used was a seven-point Likert scale to determine whether or not the participant agreed with the statement.
The full questionnaire is available in [ 59 ] and per Table 3 in the results section. The ordering of the questions was randomized for the different participants to negate any ordering effects. As outlined in the methodology section, QoE and objective metrics were captured for each trial. Participants were categorized into AR and haptic. In order to compare differences across groups, a Shapiro-Wilk normality test [ 64 ] was conducted.
For the objective analysis, we have reported differences between AR and haptic groups for number of alignments after receiving feedback, and the amount of time participants were not aligned.
We have also reported the same analysis considering gender. In this section we present analysis and discussion of the data captured during the experiment: objective measures of performance i. In addition, we include analysis by gender. For the objective data, we analysed how the participant reacted to each of the types of feedback i. For each leg, 3 distinct states were defined: varus, correct position, and valgus.
We report, for each state, the time the participants remained in misalignment during the experiment, and the number of times the participant needed feedback feedback cue during the experiment 2 minutes. We also provide detail on the number of complete alignments both legs in correct position and misalignments for each leg.
Table 1 contains performance report of varus and valgus alignment of all participants after experiencing AR and Haptic feedback. It also includes a further categorization by gender. The results show statistically significant differences between the AR and Haptic feedback in terms of the number of varus, valgus, and total misalignments for baseline and test.
Table 2 contains performance data in terms of how long users were in the varus and valgus positions during the 2 minutes trials.
We have confirmed that only AR feedback could reduce varus time with statistically significant difference for baseline and testing. This suggests that the users were somewhat confused by the haptic feedback. Table 3 present results of the MOS self-reported measures via post-test questionnaires. Table IV presents the results considering the gender variable.
As per Table 3 , out of the 12 questions asked, only Question 1, which was asked if whenever the participant received feedback, he or she adjusted easily and quickly, reported a statistically significant difference between AR and Haptic feedback with a two-tailed p value of 0. This result is confirmed that even not knowing performance, participants felt the AR feedback was more effective in reducing misalignments.
Considering the discussion in section V. A about how participants responded to the haptic feedback i. For all other questions, excluding Question 2, the AR feedback had greater MOS than Haptic feedback although not statistically significant. Table 4 presents results of the MOS Questionnaire by gender.
The female group reported a statistically significant difference between AR and Haptic for Question 1. This QoE factor is related to adjustment to feedback, changes in walking styles and system support. In this section we discuss the results of the comparison between AR and haptic feedback. Due to the fact that haptic feedback has been reported as a viable feedback modality across many fields such as rehabilitation and gait re-education, our assumption was that haptic feedback would report better results in terms of user performance and also possibly QoE.
Haptic information is given directly at the joint that the user needs to change whilst AR feedback the participant needed to process visual information and change the leg related to that change. Surprisingly as seen in the results, AR feedback not only reduced the number of misalignments, but from the subjective questionnaire analysis, users reported that AR feedback helped to reduce the number of misalignments better than haptic.
These results demonstrate the utility of employing both feedbacks, but in particular AR feedback. Reducing misalignments can also reduce the injury incidence more. These results are important for the research community and was also a good indicator for future work, where we will extend the research for understanding physiological measures and what happens in a clinical setup for males and females.
For the QoE analysis, subjective evaluation of questionnaires for feedback utility, usability, interaction, and immersion was performed. Table 3 reported results of the MOS questionnaire for all participants. This correlates with the objective analysis in Table 1. For the MOS questionnaire considering gender, the male group reported that they believed their walking style changed based on the AR feedback.
They also reported higher engagement when using the AR glasses than haptic devices. The female group reported higher utility of AR feedback. These difference between gender groups highlight the importance of considering human factors and employing QoE analysis in these types of novel feedback studies. Considering that many researches were conducted using current feedback tools such as 2D screen and haptic, this study can be a new paradigm in using immersive technologies in gait re-training and promotion of rehabilitation protocols.
This paper presented a comparison of Haptic and Augmented Reality as feedback modalities in a gait analysis system. It compared, in terms of objective and subjective ratings, how users perceived and responded to Haptic and Augmented Reality feedback.
Based on the results, the novel AR approach has significant potential as a method of gait rehabilitation. The objective evaluation tells us that AR significantly reduces the number of knee misalignment. In addition, subjective questionnaire assessment provides interesting results in terms of how users feel their walk changed positively with AR feedback. The agreement of objective and subjective evaluations serves as basis of using AR as part of a rehabilitation protocol.
Both gender groups considered reported that AR had greater utility than haptic feedback. The male group showed statistically significant improvement in varus, valgus, total Misalignment, and valgus time.
Future work will also assess the validity that AR feedback not only provides higher QoE scores but also promotes less cognitive workload in comparison with haptic as well as instantiation of the QoE model proposed above. Physiologic measures and pupillary response will also be evaluated and their inference to QoE will be analysed. The authors would like to acknowledge Dr. Paul Archbold and Mr. Eoin Woodlock for the use of the laboratory space for data collection. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
A QoE assessment of haptic and augmented reality feedback modalities in a gait analysis system. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process. We would appreciate receiving your revised manuscript by Dec 06 PM. If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter.
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